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Sample records for metabolic network facilitates

  1. Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.

    Science.gov (United States)

    Puchałka, Jacek; Oberhardt, Matthew A; Godinho, Miguel; Bielecka, Agata; Regenhardt, Daniela; Timmis, Kenneth N; Papin, Jason A; Martins dos Santos, Vítor A P

    2008-10-01

    A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13)C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile bacterium and to

  2. Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.

    Directory of Open Access Journals (Sweden)

    Jacek Puchałka

    2008-10-01

    Full Text Available A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile

  3. VRML metabolic network visualizer.

    Science.gov (United States)

    Rojdestvenski, Igor

    2003-03-01

    A successful date collection visualization should satisfy a set of many requirements: unification of diverse data formats, support for serendipity research, support of hierarchical structures, algorithmizability, vast information density, Internet-readiness, and other. Recently, virtual reality has made significant progress in engineering, architectural design, entertainment and communication. We experiment with the possibility of using the immersive abstract three-dimensional visualizations of the metabolic networks. We present the trial Metabolic Network Visualizer software, which produces graphical representation of a metabolic network as a VRML world from a formal description written in a simple SGML-type scripting language.

  4. Robustness of metabolic networks

    Science.gov (United States)

    Jeong, Hawoong

    2009-03-01

    We investigated the robustness of cellular metabolism by simulating the system-level computational models, and also performed the corresponding experiments to validate our predictions. We address the cellular robustness from the ``metabolite''-framework by using the novel concept of ``flux-sum,'' which is the sum of all incoming or outgoing fluxes (they are the same under the pseudo-steady state assumption). By estimating the changes of the flux-sum under various genetic and environmental perturbations, we were able to clearly decipher the metabolic robustness; the flux-sum around an essential metabolite does not change much under various perturbations. We also identified the list of the metabolites essential to cell survival, and then ``acclimator'' metabolites that can control the cell growth were discovered. Furthermore, this concept of ``metabolite essentiality'' should be useful in developing new metabolic engineering strategies for improved production of various bioproducts and designing new drugs that can fight against multi-antibiotic resistant superbacteria by knocking-down the enzyme activities around an essential metabolite. Finally, we combined a regulatory network with the metabolic network to investigate its effect on dynamic properties of cellular metabolism.

  5. Facilitating value co-creation in networks

    DEFF Research Database (Denmark)

    Rasmussen, Mette Apollo

    place among network participants (=customers) and network facilitator (=provider). The study draws heavily on the theoretical framework of symbolic interactionism to explain how networking interactions lead to value co-creation. Symbolic interactionism offers a solid perspective for analyzing...... of symbolic interactionism in the study. Two in-depth ethnographic case studies provide the empirical basis for the dissertation. The dissertation has constructed ethnographic narratives of the following two networks in Ringsted Municipality: •Network 1: CSR networkNetwork 2: Network for managing directors...... of networking. The concept of “imaginative value” (Beckert, 2011) is used to explain the oscillating behaviors observed in the two networks. Imaginative value can be defined as symbolic value that actors ascribe to an object, in this case the network. I argue that the group practices in the networks led...

  6. Profiling metabolic networks to study cancer metabolism.

    Science.gov (United States)

    Hiller, Karsten; Metallo, Christian M

    2013-02-01

    Cancer is a disease of unregulated cell growth and survival, and tumors reprogram biochemical pathways to aid these processes. New capabilities in the computational and bioanalytical characterization of metabolism have now emerged, facilitating the identification of unique metabolic dependencies that arise in specific cancers. By understanding the metabolic phenotype of cancers as a function of their oncogenic profiles, metabolic engineering may be applied to design synthetically lethal therapies for some tumors. This process begins with accurate measurement of metabolic fluxes. Here we review advanced methods of quantifying pathway activity and highlight specific examples where these approaches have uncovered potential opportunities for therapeutic intervention. Copyright © 2012 Elsevier Ltd. All rights reserved.

  7. Genotype networks, innovation, and robustness in sulfur metabolism

    Science.gov (United States)

    2011-01-01

    Background A metabolism is a complex network of chemical reactions. This network synthesizes multiple small precursor molecules of biomass from chemicals that occur in the environment. The metabolic network of any one organism is encoded by a metabolic genotype, defined as the set of enzyme-coding genes whose products catalyze the network's reactions. Each metabolic genotype has a metabolic phenotype. We define this metabolic phenotype as the spectrum of different sources of a chemical element that a metabolism can use to synthesize biomass. We here focus on the element sulfur. We study properties of the space of all possible metabolic genotypes in sulfur metabolism by analyzing random metabolic genotypes that are viable on different numbers of sulfur sources. Results We show that metabolic genotypes with the same phenotype form large connected genotype networks - networks of metabolic networks - that extend far through metabolic genotype space. How far they reach through this space depends linearly on the number of super-essential reactions. A super-essential reaction is an essential reaction that occurs in all networks viable in a given environment. Metabolic networks can differ in how robust their phenotype is to the removal of individual reactions. We find that this robustness depends on metabolic network size, and on other variables, such as the size of minimal metabolic networks whose reactions are all essential in a specific environment. We show that different neighborhoods of any genotype network harbor very different novel phenotypes, metabolic innovations that can sustain life on novel sulfur sources. We also analyze the ability of evolving populations of metabolic networks to explore novel metabolic phenotypes. This ability is facilitated by the existence of genotype networks, because different neighborhoods of these networks contain very different novel phenotypes. Conclusions We show that the space of metabolic genotypes involved in sulfur metabolism

  8. Evolution of metabolic network organization

    Directory of Open Access Journals (Sweden)

    Bonchev Danail

    2010-05-01

    Full Text Available Abstract Background Comparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints. Results We used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell. Using machine learning techniques we identified the most relevant aspects of cellular organization that change under evolutionary pressures. We considered the transitions from prokarya to eukarya (with a focus on the transitions among the archaea, bacteria and eukarya, from unicellular to multicellular eukarya, from free living to host-associated bacteria, from anaerobic to aerobic, as well as the acquisition of cell motility or growth in an environment of various levels of salinity or temperature. Intuitively, we expect organisms with more complex lifestyles to have more complex and robust metabolic networks. Here we demonstrate for the first time that such organisms are not only characterized by larger, denser networks of metabolic pathways but also have more efficiently organized cross communications, as revealed by subtle changes in network topology. These changes are unevenly distributed among metabolic pathways, with specific categories of pathways being promoted to more central locations as an answer to environmental constraints. Conclusions Combining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules

  9. Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets

    NARCIS (Netherlands)

    Levering, J.; Fiedler, T.; Sieg, A.; van Grinsven, K.W.A.; Hering, S.; Veith, N.; Olivier, B.G.; Klett, L.; Hugenholtz, J.; Teusink, B.; Kreikemeyer, B.; Kummer, U.

    2016-01-01

    Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes

  10. Global networking: Meeting the challenges, facilitating collaboration.

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    Botelho, M; Oancea, R; Thomas, H F; Paganelli, C; Ferrillo, P J

    2018-03-01

    The constant change of information and technology advancement as well as the impact of social media has radically changed the world and education and, in particular, the needs of students, organisations and disadvantaged communities who share the aim of training and providing quality healthcare services. Dental organisations and education centres around the world have recognised the importance of networking in delivering effective education to students, healthcare professionals and communities. Networking is one way to meet the challenges of delivering healthcare education and services. This can be achieved by sharing of resources, expertise, knowledge and experience to benefit all the stakeholders in healthcare delivery. The joint ADEE/ADEA Meeting in London on 8-9 May 2017 has facilitated discussions amongst dental educators from all over the world during a workshop on "Global Networking: the how and why for dental educators." The aim of this workshop was to determine how can dental educators worldwide network to share ideas, experience, expertise and resources to improve both the curricula and the teaching and learning environment. A pre-conference survey was designed and implemented to identify the domains of interest and needs of participants. A structured questionnaire was administered, and this information was used to guide discussions on three main themes: curricula, faculty development and mobility of faculty and students. Four questions were then defined to help group leaders to frame discussions in the four working groups. The four groups engaged in parallel discussions, with the ideas recorded and collated by group leaders, which later served for the thematic analysis across the groups to draw the key points discussed. Overall, a great desire and potential to create a global networking to share and gain support and expertise at individual and organisational level was apparent and the working group has proposed an action plan, acknowledging that it

  11. A random network based, node attraction facilitated network evolution method

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    WenJun Zhang

    2016-03-01

    Full Text Available In present study, I present a method of network evolution that based on random network, and facilitated by node attraction. In this method, I assume that the initial network is a random network, or a given initial network. When a node is ready to connect, it tends to link to the node already owning the most connections, which coincides with the general rule (Barabasi and Albert, 1999 of node connecting. In addition, a node may randomly disconnect a connection i.e., the addition of connections in the network is accompanied by the pruning of some connections. The dynamics of network evolution is determined of the attraction factor Lamda of nodes, the probability of node connection, the probability of node disconnection, and the expected initial connectance. The attraction factor of nodes, the probability of node connection, and the probability of node disconnection are time and node varying. Various dynamics can be achieved by adjusting these parameters. Effects of simplified parameters on network evolution are analyzed. The changes of attraction factor Lamda can reflect various effects of the node degree on connection mechanism. Even the changes of Lamda only will generate various networks from the random to the complex. Therefore, the present algorithm can be treated as a general model for network evolution. Modeling results show that to generate a power-law type of network, the likelihood of a node attracting connections is dependent upon the power function of the node's degree with a higher-order power. Matlab codes for simplified version of the method are provided.

  12. Structural correlations in bacterial metabolic networks

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    Lizana Ludvig

    2011-01-01

    Full Text Available Abstract Background Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution. Results We consider the 'union-network' of 134 bacterial metabolisms, and also the union of two smaller subsets of closely related species. Each reaction-node is tagged with the number of organisms it belongs to, which we denote organism degree (OD, a key concept in our study. Network analysis shows that common reactions are found at the centre of the network and that the average OD decreases as we move to the periphery. Nodes of the same OD are also more likely to be connected to each other compared to a random OD relabelling based on their occurrence in the real data. This trend persists up to a distance of around five reactions. A simple growth model of metabolic networks is used to investigate the biochemical constraints put on metabolic-network evolution. Despite this seemingly drastic simplification, a 'union-network' of a collection of unrelated model networks, free of any selective pressure, still exhibit similar structural features as their bacterial counterpart. Conclusions The OD distribution quantifies topological properties of the evolutionary history of bacterial metabolic networks, and lends additional support to the importance of horizontal gene transfer during bacterial metabolic evolution where new reactions are attached at the periphery of the network. The neutral model of metabolic network growth can reproduce the main features of real networks, but we observe that the real networks contain a smaller common core, while they are more similar at the periphery

  13. Structural correlations in bacterial metabolic networks.

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    Bernhardsson, Sebastian; Gerlee, Philip; Lizana, Ludvig

    2011-01-20

    Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution. We consider the 'union-network' of 134 bacterial metabolisms, and also the union of two smaller subsets of closely related species. Each reaction-node is tagged with the number of organisms it belongs to, which we denote organism degree (OD), a key concept in our study. Network analysis shows that common reactions are found at the centre of the network and that the average OD decreases as we move to the periphery. Nodes of the same OD are also more likely to be connected to each other compared to a random OD relabelling based on their occurrence in the real data. This trend persists up to a distance of around five reactions. A simple growth model of metabolic networks is used to investigate the biochemical constraints put on metabolic-network evolution. Despite this seemingly drastic simplification, a 'union-network' of a collection of unrelated model networks, free of any selective pressure, still exhibit similar structural features as their bacterial counterpart. The OD distribution quantifies topological properties of the evolutionary history of bacterial metabolic networks, and lends additional support to the importance of horizontal gene transfer during bacterial metabolic evolution where new reactions are attached at the periphery of the network. The neutral model of metabolic network growth can reproduce the main features of real networks, but we observe that the real networks contain a smaller common core, while they are more similar at the periphery of the network. This suggests that natural selection and

  14. Facilitating conditions for boundary-spanning behavior in governance networks

    OpenAIRE

    Meerkerk, Ingmar; Edelenbos, Jurian

    2017-01-01

    textabstractThis article examines the impact of two facilitating conditions for boundary-spanning behaviour in urban governance networks. While research on boundary spanning is growing, there is little attention for antecedents. Combining governance network literature on project management and organizational literature on facilitative and servant leadership, we examine two potential conditions: a facilitative project management style and executive support. We conducted survey research among p...

  15. Uncovering transcriptional regulation of metabolism by using metabolic network topology

    DEFF Research Database (Denmark)

    Patil, Kiran Raosaheb; Nielsen, Jens

    2005-01-01

    therefore developed an algorithm that is based on hypothesis-driven data analysis to uncover the transcriptional regulatory architecture of metabolic networks. By using information on the metabolic network topology from genome-scale metabolic reconstruction, we show that it is possible to reveal patterns...... changes induced by complex regulatory mechanisms coordinating the activity of different metabolic pathways. It is difficult to map such global transcriptional responses by using traditional methods, because many genes in the metabolic network have relatively small changes at their transcription level. We...... in the metabolic network that follow a common transcriptional response. Thus, the algorithm enables identification of so-called reporter metabolites (metabolites around which the most significant transcriptional changes occur) and a set of connected genes with significant and coordinated response to genetic...

  16. Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

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    Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

    2016-01-01

    Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845

  17. Stability from Structure : Metabolic Networks Are Unlike Other Biological Networks

    NARCIS (Netherlands)

    Van Nes, P.; Bellomo, D.; Reinders, M.J.T.; De Ridder, D.

    2009-01-01

    In recent work, attempts have been made to link the structure of biochemical networks to their complex dynamics. It was shown that structurally stable network motifs are enriched in such networks. In this work, we investigate to what extent these findings apply to metabolic networks. To this end, we

  18. Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction.

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    Benjamin D Heavner

    2015-11-01

    Full Text Available We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159. We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.

  19. Networked Collaboration Canvas : How can Service Design facilitate Networked Collaboration?

    NARCIS (Netherlands)

    L. Henze; I.J. Mulder

    2014-01-01

    Whereas products and services are growing in complexity, industry needs to expand their networks to include expertise from other fields than their own. Consequently, innovation activities more and more take place in highly dynamic network environments, mixing people and parties, models, interests,

  20. A network perspective on metabolic inconsistency

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    Sonnenschein Nikolaus

    2012-05-01

    Full Text Available Abstract Background Integrating gene expression profiles and metabolic pathways under different experimental conditions is essential for understanding the coherence of these two layers of cellular organization. The network character of metabolic systems can be instrumental in developing concepts of agreement between expression data and pathways. A network-driven interpretation of gene expression data has the potential of suggesting novel classifiers for pathological cellular states and of contributing to a general theoretical understanding of gene regulation. Results Here, we analyze the coherence of gene expression patterns and a reconstruction of human metabolism, using consistency scores obtained from network and constraint-based analysis methods. We find a surprisingly strong correlation between the two measures, demonstrating that a substantial part of inconsistencies between metabolic processes and gene expression can be understood from a network perspective alone. Prompted by this finding, we investigate the topological context of the individual biochemical reactions responsible for the observed inconsistencies. On this basis, we are able to separate the differential contributions that bear physiological information about the system, from the unspecific contributions that unravel gaps in the metabolic reconstruction. We demonstrate the biological potential of our network-driven approach by analyzing transcriptome profiles of aldosterone producing adenomas that have been obtained from a cohort of Primary Aldosteronism patients. We unravel systematics in the data that could not have been resolved by conventional microarray data analysis. In particular, we discover two distinct metabolic states in the adenoma expression patterns. Conclusions The methodology presented here can help understand metabolic inconsistencies from a network perspective. It thus serves as a mediator between the topology of metabolic systems and their dynamical

  1. Hierarchical analysis of dependency in metabolic networks.

    Science.gov (United States)

    Gagneur, Julien; Jackson, David B; Casari, Georg

    2003-05-22

    Elucidation of metabolic networks for an increasing number of organisms reveals that even small networks can contain thousands of reactions and chemical species. The intimate connectivity between components complicates their decomposition into biologically meaningful sub-networks. Moreover, traditional higher-order representations of metabolic networks as metabolic pathways, suffers from the lack of rigorous definition, yielding pathways of disparate content and size. We introduce a hierarchical representation that emphasizes the gross organization of metabolic networks in largely independent pathways and sub-systems at several levels of independence. The approach highlights the coupling of different pathways and the shared compounds responsible for those couplings. By assessing our results on Escherichia coli (E.coli metabolic reactions, Genetic Circuits Research Group, University of California, San Diego, http://gcrg.ucsd.edu/organisms/ecoli.html, 'model v 1.01. reactions') against accepted biochemical annotations, we provide the first systematic synopsis of an organism's metabolism. Comparison with operons of E.coli shows that low-level clusters are reflected in genome organization and gene regulation. Source code, data sets and supplementary information are available at http://www.mas.ecp.fr/labo/equipe/gagneur/hierarchy/hierarchy.html

  2. Optimal flux patterns in cellular metabolic networks

    Energy Technology Data Exchange (ETDEWEB)

    Almaas, E

    2007-01-20

    The availability of whole-cell level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30,000 random cellular environments. The distribution of reaction fluxes is heavy-tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations have relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reaction are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central-carbon metabolic pathways for the sample of random environments.

  3. Goal-congruent default network activity facilitates cognitive control.

    Science.gov (United States)

    Spreng, R Nathan; DuPre, Elizabeth; Selarka, Dhawal; Garcia, Juliana; Gojkovic, Stefan; Mildner, Judith; Luh, Wen-Ming; Turner, Gary R

    2014-10-15

    Substantial neuroimaging evidence suggests that spontaneous engagement of the default network impairs performance on tasks requiring executive control. We investigated whether this impairment depends on the congruence between executive control demands and internal mentation. We hypothesized that activation of the default network might enhance performance on an executive control task if control processes engage long-term memory representations that are supported by the default network. Using fMRI, we scanned 36 healthy young adult humans on a novel two-back task requiring working memory for famous and anonymous faces. In this task, participants (1) matched anonymous faces interleaved with anonymous face, (2) matched anonymous faces interleaved with a famous face, or (3) matched a famous faces interleaved with an anonymous face. As predicted, we observed a facilitation effect when matching famous faces, compared with anonymous faces. We also observed greater activation of the default network during these famous face-matching trials. The results suggest that activation of the default network can contribute to task performance during an externally directed executive control task. Our findings provide evidence that successful activation of the default network in a contextually relevant manner facilitates goal-directed cognition. Copyright © 2014 the authors 0270-6474/14/3414108-07$15.00/0.

  4. On Functional Module Detection in Metabolic Networks

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    Koch, Ina; Ackermann, Jörg

    2013-01-01

    Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. PMID:24958145

  5. Ecological network analysis of China's societal metabolism.

    Science.gov (United States)

    Zhang, Yan; Liu, Hong; Li, Yating; Yang, Zhifeng; Li, Shengsheng; Yang, Naijin

    2012-01-01

    Uncontrolled socioeconomic development has strong negative effects on the ecological environment, including pollution and the depletion and waste of natural resources. These serious consequences result from the high flows of materials and energy through a socioeconomic system produced by exchanges between the system and its surroundings, causing the disturbance of metabolic processes. In this paper, we developed an ecological network model for a societal system, and used China in 2006 as a case study to illustrate application of the model. We analyzed China's basic metabolic processes and used ecological network analysis to study the network relationships within the system. Basic components comprised the internal environment, five sectors (agriculture, exploitation, manufacturing, domestic, and recycling), and the external environment. We defined 21 pairs of ecological relationships in China's societal metabolic system (excluding self-mutualism within a component). Using utility and throughflow analysis, we found that exploitation, mutualism, and competition relationships accounted for 76.2, 14.3, and 9.5% of the total relationships, respectively. In our trophic level analysis, the components were divided into producers, consumers, and decomposers according to their positions in the system. Our analyses revealed ways to optimize the system's structure and adjust its functions, thereby promoting healthier socioeconomic development, and suggested ways to apply ecological network analysis in future socioeconomic research. Copyright © 2011 Elsevier Ltd. All rights reserved.

  6. Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

    Science.gov (United States)

    Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

    2011-01-01

    Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

  7. Corporate Forms Facilitating Non-Profit Networking: Formalizing the Informal

    Directory of Open Access Journals (Sweden)

    Jakulevičienė Lyra

    2017-12-01

    Full Text Available Cooperation and networking among a variety of organisations for the purpose of research, projects, and other activities ranges from ad hoc to long term organisational relationships, formalised or based on informal cooperation. Although informality is frequently much valued and drives organisations to partner on substance rather than bureaucracy, formalisation of networks and cooperation might be indispensible for effective partnerships and activities, as well as representation of mutual interests beyond the national level. How shall such networks be formalised at European and/or national levels so that they are flexible enough, involve minimum bureaucracy, and engage the maximum scope of possible activities? This article focuses on the analysis of possible legal structures facilitating the work of a group of entities and individuals engaged in cross-border activities. This study examines the potential of national legal opportunities in five countries: Belgium, Estonia, Lithuania, Poland and the Netherlands, and the proven legal form of EEIG in reducing the barriers for cooperation, as well as the advantages and disadvantages of these legal forms for a formalized network and the purposes it serves.

  8. Scavenger Receptor BI Plays a Role in Facilitating Chylomicron Metabolism

    NARCIS (Netherlands)

    Out, R.; Kruijt, J.K.; Rensen, P.C.N.; Hildebrand, R.B.; Vos, P. de; Eck, M. van; Berkel, T.J.C. van

    2004-01-01

    The function of scavenger receptor class B type I (SR-BI) in mediating the selective uptake of high density lipoprotein (HDL) cholesterol esters is well established. However, the potential role of SR-BI in chylomicron and chylomicron remnant metabolism is largely unknown. In the present

  9. The reconstruction and analysis of tissue specific human metabolic networks.

    Science.gov (United States)

    Hao, Tong; Ma, Hong-Wu; Zhao, Xue-Ming; Goryanin, Igor

    2012-02-01

    Human tissues have distinct biological functions. Many proteins/enzymes are known to be expressed only in specific tissues and therefore the metabolic networks in various tissues are different. Though high quality global human metabolic networks and metabolic networks for certain tissues such as liver have already been studied, a systematic study of tissue specific metabolic networks for all main tissues is still missing. In this work, we reconstruct the tissue specific metabolic networks for 15 main tissues in human based on the previously reconstructed Edinburgh Human Metabolic Network (EHMN). The tissue information is firstly obtained for enzymes from Human Protein Reference Database (HPRD) and UniprotKB databases and transfers to reactions through the enzyme-reaction relationships in EHMN. As our knowledge of tissue distribution of proteins is still very limited, we replenish the tissue information of the metabolic network based on network connectivity analysis and thorough examination of the literature. Finally, about 80% of proteins and reactions in EHMN are determined to be in at least one of the 15 tissues. To validate the quality of the tissue specific network, the brain specific metabolic network is taken as an example for functional module analysis and the results reveal that the function of the brain metabolic network is closely related with its function as the centre of the human nervous system. The tissue specific human metabolic networks are available at .

  10. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network

    DEFF Research Database (Denmark)

    Förster, Jochen; Famili, I.; Fu, P.

    2003-01-01

    and the environment were included. A total of 708 structural open reading frames (ORFs) were accounted for in the reconstructed network, corresponding to 1035 metabolic reactions. Further, 140 reactions were included on the basis of biochemical evidence resulting in a genome-scale reconstructed metabolic network...... with Escherichia coli. The reconstructed metabolic network is the first comprehensive network for a eukaryotic organism, and it may be used as the basis for in silico analysis of phenotypic functions....

  11. The topology of metabolic isotope labeling networks

    Directory of Open Access Journals (Sweden)

    Wiechert Wolfgang

    2007-08-01

    Full Text Available Abstract Background Metabolic Flux Analysis (MFA based on isotope labeling experiments (ILEs is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures. Results With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools. Conclusion Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global

  12. Phylogeny of metabolic networks: A spectral graph theoretical ...

    Indian Academy of Sciences (India)

    Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of ...

  13. Incremental parameter estimation of kinetic metabolic network models

    Directory of Open Access Journals (Sweden)

    Jia Gengjie

    2012-11-01

    Full Text Available Abstract Background An efficient and reliable parameter estimation method is essential for the creation of biological models using ordinary differential equation (ODE. Most of the existing estimation methods involve finding the global minimum of data fitting residuals over the entire parameter space simultaneously. Unfortunately, the associated computational requirement often becomes prohibitively high due to the large number of parameters and the lack of complete parameter identifiability (i.e. not all parameters can be uniquely identified. Results In this work, an incremental approach was applied to the parameter estimation of ODE models from concentration time profiles. Particularly, the method was developed to address a commonly encountered circumstance in the modeling of metabolic networks, where the number of metabolic fluxes (reaction rates exceeds that of metabolites (chemical species. Here, the minimization of model residuals was performed over a subset of the parameter space that is associated with the degrees of freedom in the dynamic flux estimation from the concentration time-slopes. The efficacy of this method was demonstrated using two generalized mass action (GMA models, where the method significantly outperformed single-step estimations. In addition, an extension of the estimation method to handle missing data is also presented. Conclusions The proposed incremental estimation method is able to tackle the issue on the lack of complete parameter identifiability and to significantly reduce the computational efforts in estimating model parameters, which will facilitate kinetic modeling of genome-scale cellular metabolism in the future.

  14. A guide to integrating transcriptional regulatory and metabolic networks using PROM (probabilistic regulation of metabolism).

    Science.gov (United States)

    Simeonidis, Evangelos; Chandrasekaran, Sriram; Price, Nathan D

    2013-01-01

    The integration of transcriptional regulatory and metabolic networks is a crucial step in the process of predicting metabolic behaviors that emerge from either genetic or environmental changes. Here, we present a guide to PROM (probabilistic regulation of metabolism), an automated method for the construction and simulation of integrated metabolic and transcriptional regulatory networks that enables large-scale phenotypic predictions for a wide range of model organisms.

  15. Metabolic networks of Cucurbita maxima phloem.

    Science.gov (United States)

    Fiehn, Oliver

    2003-03-01

    Metabolomic analysis aims at a comprehensive characterization of biological samples. Yet, biologically meaningful interpretations are often limited by the poor spatial and temporal resolution of the acquired data sets. One way to remedy this is to limit the complexity of the cell types being studied. Cucurbita maxima Duch. vascular exudates provide an excellent material for metabolomics in this regard. Using automated mass spectral deconvolution, over 400 components have been detected in these exudates, but only 90 of them were tentatively identified. Many amino compounds were found in vascular exudates from leaf petioles at concentrations several orders of magnitude higher than in tissue disks from the same leaves, whereas hexoses and sucrose were found in far lower amounts. In order to find the expected impact of assimilation rates on sugar levels, total phloem composition of eight leaves from four plants was followed over 4.5 days. Surprisingly, no diurnal rhythm was found for any of the phloem metabolites that was statistically valid for all eight leaves. Instead, each leaf had its own distinct vascular exudate profile similar to leaves from the same plant, but clearly different from leaves harvested from plants at the same developmental stage. Thirty to forty per cent of all metabolite levels of individual leaves were different from the average of all metabolite profiles. Using metabolic co-regulation analysis, similarities and differences between the exudate profiles were more accurately characterized through network computation, specifically with respect to nitrogen metabolism.

  16. Deciphering transcriptional and metabolic networks associated with lysine metabolism during Arabidopsis seed development.

    Science.gov (United States)

    Angelovici, Ruthie; Fait, Aaron; Zhu, Xiaohong; Szymanski, Jedrzej; Feldmesser, Ester; Fernie, Alisdair R; Galili, Gad

    2009-12-01

    In order to elucidate transcriptional and metabolic networks associated with lysine (Lys) metabolism, we utilized developing Arabidopsis (Arabidopsis thaliana) seeds as a system in which Lys synthesis could be stimulated developmentally without application of chemicals and coupled this to a T-DNA insertion knockout mutation impaired in Lys catabolism. This seed-specific metabolic perturbation stimulated Lys accumulation starting from the initiation of storage reserve accumulation. Our results revealed that the response of seed metabolism to the inducible alteration of Lys metabolism was relatively minor; however, that which was observable operated in a modular manner. They also demonstrated that Lys metabolism is strongly associated with the operation of the tricarboxylic acid cycle while largely disconnected from other metabolic networks. In contrast, the inducible alteration of Lys metabolism was strongly associated with gene networks, stimulating the expression of hundreds of genes controlling anabolic processes that are associated with plant performance and vigor while suppressing a small number of genes associated with plant stress interactions. The most pronounced effect of the developmentally inducible alteration of Lys metabolism was an induction of expression of a large set of genes encoding ribosomal proteins as well as genes encoding translation initiation and elongation factors, all of which are associated with protein synthesis. With respect to metabolic regulation, the inducible alteration of Lys metabolism was primarily associated with altered expression of genes belonging to networks of amino acids and sugar metabolism. The combined data are discussed within the context of network interactions both between and within metabolic and transcriptional control systems.

  17. Facilitering

    DEFF Research Database (Denmark)

    Ravn, Ib

    2012-01-01

    Facilitering (af latin facilis: gørbart, let at gøre) er den teknik at gøre det lettere for en forsamlet gruppe mennesker at udrette det, den ønsker. Facilitator er en slags mødeleder eller ordstyrer, der bistår gruppen ved at styre formen på deltagernes samtale og interaktion snarere end indholdet...

  18. From genomes to in silico cells via metabolic networks

    DEFF Research Database (Denmark)

    Borodina, Irina; Nielsen, Jens

    2005-01-01

    Genome-scale metabolic models are the focal point of systems biology as they allow the collection of various data types in a form suitable for mathematical analysis. High-quality metabolic networks and metabolic networks with incorporated regulation have been successfully used for the analysis of...... approaches to obtain an in silico prediction of cellular function based on the interaction of all of the cellular components....

  19. The evolution of modularity in bacterial metabolic networks.

    Science.gov (United States)

    Kreimer, Anat; Borenstein, Elhanan; Gophna, Uri; Ruppin, Eytan

    2008-05-13

    Deciphering the modular organization of metabolic networks and understanding how modularity evolves have attracted tremendous interest in recent years. Here, we present a comprehensive large scale characterization of modularity across the bacterial tree of life, systematically quantifying the modularity of the metabolic networks of >300 bacterial species. Three main determinants of metabolic network modularity are identified. First, network size is an important topological determinant of network modularity. Second, several environmental factors influence network modularity, with endosymbionts and mammal-specific pathogens having lower modularity scores than bacterial species that occupy a wider range of niches. Moreover, even among the pathogens, those that alternate between two distinct niches, such as insect and mammal, tend to have relatively high metabolic network modularity. Third, horizontal gene transfer is an important force that contributes significantly to metabolic modularity. We additionally reconstruct the metabolic network of ancestral bacterial species and examine the evolution of modularity across the tree of life. This reveals a trend of modularity decrease from ancestors to descendants that is likely the outcome of niche specialization and the incorporation of peripheral metabolic reactions.

  20. Dead end metabolites--defining the known unknowns of the E. coli metabolic network.

    Directory of Open Access Journals (Sweden)

    Amanda Mackie

    Full Text Available The EcoCyc database is an online scientific database which provides an integrated view of the metabolic and regulatory network of the bacterium Escherichia coli K-12 and facilitates computational exploration of this important model organism. We have analysed the occurrence of dead end metabolites within the database--these are metabolites which lack the requisite reactions (either metabolic or transport that would account for their production or consumption within the metabolic network. 127 dead end metabolites were identified from the 995 compounds that are contained within the EcoCyc metabolic network. Their presence reflects either a deficit in our representation of the network or in our knowledge of E. coli metabolism. Extensive literature searches resulted in the addition of 38 transport reactions and 3 metabolic reactions to the database and led to an improved representation of the pathway for Vitamin B12 salvage. 39 dead end metabolites were identified as components of reactions that are not physiologically relevant to E. coli K-12--these reactions are properties of purified enzymes in vitro that would not be expected to occur in vivo. Our analysis led to improvements in the software that underpins the database and to the program that finds dead end metabolites within EcoCyc. The remaining dead end metabolites in the EcoCyc database likely represent deficiencies in our knowledge of E. coli metabolism.

  1. Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

    OpenAIRE

    Mart?n-Jim?nez, Cynthia A.; Salazar-Barreto, Diego; Barreto, George E.; Gonz?lez, Janneth

    2017-01-01

    Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework t...

  2. Graph methods for the investigation of metabolic networks in parasitology.

    Science.gov (United States)

    Cottret, Ludovic; Jourdan, Fabien

    2010-08-01

    Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

  3. Metabolic pathways variability and sequence/networks comparisons

    Science.gov (United States)

    Tun, Kyaw; Dhar, Pawan K; Palumbo, Maria Concetta; Giuliani, Alessandro

    2006-01-01

    Background In this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption. Results We demonstrate both the ability of the proposed method to build reliable biological classification of a set of microrganisms and the strong correlation between the metabolic network wiringand involved enzymes sequence space. Conclusion The method represents a valuable tool for the investigation of genotype/phenotype correlationsallowing for a direct comparison of different species as for their metabolic machinery. In addition the detection of enzymes whose sequence space is maximally correlated with the metabolicnetwork space gives an indication of the most crucial (on an evolutionary viewpoint) steps of the metabolic process. PMID:16420696

  4. Metabolic pathways variability and sequence/networks comparisons

    Directory of Open Access Journals (Sweden)

    Palumbo Maria

    2006-01-01

    Full Text Available Abstract Background In this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption. Results We demonstrate both the ability of the proposed method to build reliable biological classification of a set of microrganisms and the strong correlation between the metabolic network wiringand involved enzymes sequence space. Conclusion The method represents a valuable tool for the investigation of genotype/phenotype correlationsallowing for a direct comparison of different species as for their metabolic machinery. In addition the detection of enzymes whose sequence space is maximally correlated with the metabolicnetwork space gives an indication of the most crucial (on an evolutionary viewpoint steps of the metabolic process.

  5. The Importance of Transition Metals in the Expanding Network of Microbial Metabolism in the Archean Eon

    Science.gov (United States)

    Moore, E. K.; Jelen, B. I.; Giovannelli, D.; Prabhu, A.; Raanan, H.; Falkowski, P. G.

    2017-12-01

    Deep time changes in Earth surface redox conditions, particularly due to global oxygenation, has impacted the availability of different metals and substrates that are central in biology. Oxidoreductase proteins are molecular nanomachines responsible for all biological electron transfer processes across the tree of life. These enzymes largely contain transition metals in their active sites. Microbial metabolic pathways form a global network of electron transfer, which expanded throughout the Archean eon. Older metabolisms (sulfur reduction, methanogenesis, anoxygenic photosynthesis) accessed negative redox potentials, while later evolving metabolisms (oxygenic photosynthesis, nitrification/denitrification, aerobic respiration) accessed positive redox potentials. The incorporation of different transition metals facilitated biological innovation and the expansion of the network of microbial metabolism. Network analysis was used to examine the connections between microbial taxa, metabolic pathways, crucial metallocofactors, and substrates in deep time by incorporating biosignatures preserved in the geologic record. Nitrogen fixation and aerobic respiration have the highest level of betweenness among metabolisms in the network, indicating that the oldest metabolisms are not the most central. Fe has by far the highest betweenness among metals. Clustering analysis largely separates High Metal Bacteria (HMB), Low Metal Bacteria (LMB), and Archaea showing that simple un-weighted links between taxa, metabolism, and metals have phylogenetic relevance. On average HMB have the highest betweenness among taxa, followed by Archaea and LMB. There is a correlation between the number of metallocofactors and metabolic pathways in representative bacterial taxa, but Archaea do not follow this trend. In many cases older and more recently evolved metabolisms were clustered together supporting previous findings that proliferation of metabolic pathways is not necessarily chronological.

  6. The facilitation of groups and networks: capabilities to shape creative cooperation

    DEFF Research Database (Denmark)

    Rasmussen, Lauge Baungaard

    2003-01-01

    The facilitator, defined as a process guide of creative cooperation, is becoming more and more in focus to assist groups,teams and networks to meet these challenges. The author defines and exemplifies different levels of creative coorperation. Core capabilities of facilitation are defined and exp...... and explained at each level. Finally, possible societal and ethical aspects of facilitation are discussed as well as future perspectives of disseminating facilitative values and methods....

  7. Telecentre-Europe Network : Facilitating Knowledge Sharing and ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Telecentre-Europe Network came out of a workshop held in Barcelona in June 2007, after some 50 nongovernmental organizations observed that they shared similar electronic inclusion targets with the European Union and each other. Accordingly, the participants elected a taskforce to plan and create a telecentre network ...

  8. Integration of Genome Scale Metabolic Networks and Gene Regulation of Metabolic Enzymes With Physiologically Based Pharmacokinetics

    Science.gov (United States)

    Maldonado, Elaina M.; Leoncikas, Vytautas; Fisher, Ciarán P.; Moore, J. Bernadette; Plant, Nick J.

    2017-01-01

    The scope of physiologically based pharmacokinetic (PBPK) modeling can be expanded by assimilation of the mechanistic models of intracellular processes from systems biology field. The genome scale metabolic networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation. We demonstrate example models. PMID:28782239

  9. Horizontal and vertical growth of S. cerevisiae metabolic network.

    KAUST Repository

    Grassi, Luigi

    2011-10-14

    BACKGROUND: The growth and development of a biological organism is reflected by its metabolic network, the evolution of which relies on the essential gene duplication mechanism. There are two current views about the evolution of metabolic networks. The retrograde model hypothesizes that a pathway evolves by recruiting novel enzymes in a direction opposite to the metabolic flow. The patchwork model is instead based on the assumption that the evolution is based on the exploitation of broad-specificity enzymes capable of catalysing a variety of metabolic reactions. RESULTS: We analysed a well-studied unicellular eukaryotic organism, S. cerevisiae, and studied the effect of the removal of paralogous gene products on its metabolic network. Our results, obtained using different paralog and network definitions, show that, after an initial period when gene duplication was indeed instrumental in expanding the metabolic space, the latter reached an equilibrium and subsequent gene duplications were used as a source of more specialized enzymes rather than as a source of novel reactions. We also show that the switch between the two evolutionary strategies in S. cerevisiae can be dated to about 350 million years ago. CONCLUSIONS: Our data, obtained through a novel analysis methodology, strongly supports the hypothesis that the patchwork model better explains the more recent evolution of the S. cerevisiae metabolic network. Interestingly, the effects of a patchwork strategy acting before the Euascomycete-Hemiascomycete divergence are still detectable today.

  10. Slave nodes and the controllability of metabolic networks

    International Nuclear Information System (INIS)

    Kim, Dong-Hee; Motter, Adilson E

    2009-01-01

    Recent work on synthetic rescues has shown that the targeted deletion of specific metabolic genes can often be used to rescue otherwise non-viable mutants. This raises a fundamental biophysical question: to what extent can the whole-cell behavior of a large metabolic network be controlled by constraining the flux of one or more reactions in the network? This touches upon the issue of the number of degrees of freedom contained by one such network. Using the metabolic network of Escherichia coli as a model system, here we address this question theoretically by exploring not only reaction deletions, but also a continuum of all possible reaction expression levels. We show that the behavior of the metabolic network can be largely manipulated by the pinned expression of a single reaction. In particular, a relevant fraction of the metabolic reactions exhibits canalizing interactions, in that the specification of one reaction flux determines cellular growth as well as the fluxes of most other reactions in optimal steady states. The activity of individual reactions can thus be used as surrogates to monitor and possibly control cellular growth and other whole-cell behaviors. In addition to its implications for the study of control processes, our methodology provides a new approach to study how the integrated dynamics of the entire metabolic network emerges from the coordinated behavior of its component parts.

  11. Social Networking Tools to Facilitate Cross-Program Collaboration

    Science.gov (United States)

    Wallace, Paul; Howard, Barbara

    2010-01-01

    Students working on a highly collaborative project used social networking technology for community building activities as well as basic project-related communication. Requiring students to work on cross-program projects gives them real-world experience working in diverse, geographically dispersed groups. An application used at Appalachian State…

  12. Does habitat variability really promote metabolic network modularity?

    Science.gov (United States)

    Takemoto, Kazuhiro

    2013-01-01

    The hypothesis that variability in natural habitats promotes modular organization is widely accepted for cellular networks. However, results of some data analyses and theoretical studies have begun to cast doubt on the impact of habitat variability on modularity in metabolic networks. Therefore, we re-evaluated this hypothesis using statistical data analysis and current metabolic information. We were unable to conclude that an increase in modularity was the result of habitat variability. Although horizontal gene transfer was also considered because it may contribute for survival in a variety of environments, closely related to habitat variability, and is known to be positively correlated with network modularity, such a positive correlation was not concluded in the latest version of metabolic networks. Furthermore, we demonstrated that the previously observed increase in network modularity due to habitat variability and horizontal gene transfer was probably due to a lack of available data on metabolic reactions. Instead, we determined that modularity in metabolic networks is dependent on species growth conditions. These results may not entirely discount the impact of habitat variability and horizontal gene transfer. Rather, they highlight the need for a more suitable definition of habitat variability and a more careful examination of relationships of the network modularity with horizontal gene transfer, habitats, and environments.

  13. A compendium of inborn errors of metabolism mapped onto the human metabolic network.

    Science.gov (United States)

    Sahoo, Swagatika; Franzson, Leifur; Jonsson, Jon J; Thiele, Ines

    2012-10-01

    Inborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitine (AC) and fatty acid oxidation (FAO) metabolism. Using literary data, we reconstructed an AC/FAO module consisting of 352 reactions and 139 metabolites. When this module was combined with the human metabolic reconstruction, the synthesis of 39 acylcarnitines and 22 amino acids, which are routinely measured, was captured and 235 distinct IEMs could be mapped. We collected phenotypic and clinical features for each IEM enabling comprehensive classification. We found that carbohydrate, amino acid, and lipid metabolism were most affected by the IEMs, while the brain was the most commonly affected organ. Furthermore, we analyzed the IEMs in the context of metabolic network topology to gain insight into common features between metabolically connected IEMs. While many known examples were identified, we discovered some surprising IEM pairs that shared reactions as well as clinical features but not necessarily causal genes. Moreover, we could also re-confirm that acetyl-CoA acts as a central metabolite. This network based analysis leads to further insight of hot spots in human metabolism with respect to IEMs. The presented comprehensive knowledge base of IEMs will provide a valuable tool in studying metabolic changes involved in inherited metabolic diseases.

  14. Dissociation of rapid response learning and facilitation in perceptual and conceptual networks of person recognition.

    Science.gov (United States)

    Valt, Christian; Klein, Christoph; Boehm, Stephan G

    2015-08-01

    Repetition priming is a prominent example of non-declarative memory, and it increases the accuracy and speed of responses to repeatedly processed stimuli. Major long-hold memory theories posit that repetition priming results from facilitation within perceptual and conceptual networks for stimulus recognition and categorization. Stimuli can also be bound to particular responses, and it has recently been suggested that this rapid response learning, not network facilitation, provides a sound theory of priming of object recognition. Here, we addressed the relevance of network facilitation and rapid response learning for priming of person recognition with a view to advance general theories of priming. In four experiments, participants performed conceptual decisions like occupation or nationality judgments for famous faces. The magnitude of rapid response learning varied across experiments, and rapid response learning co-occurred and interacted with facilitation in perceptual and conceptual networks. These findings indicate that rapid response learning and facilitation in perceptual and conceptual networks are complementary rather than competing theories of priming. Thus, future memory theories need to incorporate both rapid response learning and network facilitation as individual facets of priming. © 2014 The British Psychological Society.

  15. Structuring evolution: biochemical networks and metabolic diversification in birds.

    Science.gov (United States)

    Morrison, Erin S; Badyaev, Alexander V

    2016-08-25

    Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a "global" carotenoid network - comprising of all known enzymatic reactions among naturally occurring carotenoids - with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds. We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network - compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution. The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution.

  16. Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

    Directory of Open Access Journals (Sweden)

    Brian R Granger

    2016-04-01

    Full Text Available The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space, a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

  17. Phylogeny of metabolic networks: A spectral graph theoretical ...

    Indian Academy of Sciences (India)

    The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a ...

  18. Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity.

    Science.gov (United States)

    Rolfsson, Óttar; Paglia, Giuseppe; Magnusdóttir, Manuela; Palsson, Bernhard Ø; Thiele, Ines

    2013-01-15

    Metabolic network reconstructions define metabolic information within a target organism and can therefore be used to address incomplete metabolic information. In the present study we used a computational approach to identify human metabolites whose metabolism is incomplete on the basis of their detection in humans but exclusion from the human metabolic network reconstruction RECON 1. Candidate solutions, composed of metabolic reactions capable of explaining the metabolism of these compounds, were then identified computationally from a global biochemical reaction database. Solutions were characterized with respect to how metabolites were incorporated into RECON 1 and their biological relevance. Through detailed case studies we show that biologically plausible non-intuitive hypotheses regarding the metabolism of these compounds can be proposed in a semi-automated manner, in an approach that is similar to de novo network reconstruction. We subsequently experimentally validated one of the proposed hypotheses and report that C9orf103, previously identified as a candidate tumour suppressor gene, encodes a functional human gluconokinase. The results of the present study demonstrate how semi-automatic gap filling can be used to refine and extend metabolic reconstructions, thereby increasing their biological scope. Furthermore, we illustrate how incomplete human metabolic knowledge can be coupled with gene annotation in order to prioritize and confirm gene functions.

  19. Integration of metabolome data with metabolic networks reveals reporter reactions

    DEFF Research Database (Denmark)

    Çakir, Tunahan; Patil, Kiran Raosaheb; Önsan, Zeynep Ilsen

    2006-01-01

    network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from......Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic...... is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through...

  20. Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling

    International Nuclear Information System (INIS)

    Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R.; Jijakli, Kenan; Salehi-Ashtiani, Kourosh

    2014-01-01

    Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.

  1. Microalgal Metabolic Network Model Refinement through High Throughput Functional Metabolic Profiling

    Directory of Open Access Journals (Sweden)

    Amphun eChaiboonchoe

    2014-12-01

    Full Text Available Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The Phenotype Microarray (PM technology (Biolog, Hayward, CA, USA provides an efficient, high throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi but it has not been reported for the phenotyping of microalgae. Here we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of D-amino acids, L-dipeptides, and L-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.

  2. Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling.

    Science.gov (United States)

    Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R; Jijakli, Kenan; Salehi-Ashtiani, Kourosh

    2014-01-01

    Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.

  3. Observability of plant metabolic networks is reflected in the correlation of metabolic profiles

    DEFF Research Database (Denmark)

    Schwahn, Kevin; Küken, Anika; Kliebenstein, Daniel James

    2016-01-01

    -of-the-art genome-scale metabolic networks. By using metabolic data profiles from a set of seven environmental perturbations as well as from natural variability, we demonstrate that the data profiles of sensor metabolites are more correlated than those of nonsensor metabolites. This pattern was confirmed...

  4. Metabolic Brain Network Analysis of Hypothyroidism Symptom Based on [18F]FDG-PET of Rats.

    Science.gov (United States)

    Wan, Hongkai; Tan, Ziyu; Zheng, Qiang; Yu, Jing

    2018-03-12

    Recent researches have demonstrated the value of using 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) positron emission tomography (PET) imaging to reveal the hypothyroidism-related damages in local brain regions. However, the influence of hypothyroidism on the entire brain network is barely studied. This study focuses on the application of graph theory on analyzing functional brain networks of the hypothyroidism symptom. For both the hypothyroidism and the control groups of Wistar rats, the functional brain networks were constructed by thresholding the glucose metabolism correlation matrices of 58 brain regions. The network topological properties (including the small-world properties and the nodal centralities) were calculated and compared between the two groups. We found that the rat brains, like human brains, have typical properties of the small-world network in both the hypothyroidism and the control groups. However, the hypothyroidism group demonstrated lower global efficiency and decreased local cliquishness of the brain network, indicating hypothyroidism-related impairment to the brain network. The hypothyroidism group also has decreased nodal centrality in the left posterior hippocampus, the right hypothalamus, pituitary, pons, and medulla. This observation accorded with the hypothyroidism-related functional disorder of hypothalamus-pituitary-thyroid (HPT) feedback regulation mechanism. Our research quantitatively confirms that hypothyroidism hampers brain cognitive function by causing impairment to the brain network of glucose metabolism. This study reveals the feasibility and validity of applying graph theory method to preclinical [ 18 F]FDG-PET images and facilitates future study on human subjects.

  5. Preferential attachment in the evolution of metabolic networks

    Directory of Open Access Journals (Sweden)

    Elofsson Arne

    2005-11-01

    Full Text Available Abstract Background Many biological networks show some characteristics of scale-free networks. Scale-free networks can evolve through preferential attachment where new nodes are preferentially attached to well connected nodes. In networks which have evolved through preferential attachment older nodes should have a higher average connectivity than younger nodes. Here we have investigated preferential attachment in the context of metabolic networks. Results The connectivities of the enzymes in the metabolic network of Escherichia coli were determined and representatives for these enzymes were located in 11 eukaryotes, 17 archaea and 46 bacteria. E. coli enzymes which have representatives in eukaryotes have a higher average connectivity while enzymes which are represented only in the prokaryotes, and especially the enzymes only present in βγ-proteobacteria, have lower connectivities than expected by chance. Interestingly, the enzymes which have been proposed as candidates for horizontal gene transfer have a higher average connectivity than the other enzymes. Furthermore, It was found that new edges are added to the highly connected enzymes at a faster rate than to enzymes with low connectivities which is consistent with preferential attachment. Conclusion Here, we have found indications of preferential attachment in the metabolic network of E. coli. A possible biological explanation for preferential attachment growth of metabolic networks is that novel enzymes created through gene duplication maintain some of the compounds involved in the original reaction, throughout its future evolution. In addition, we found that enzymes which are candidates for horizontal gene transfer have a higher average connectivity than other enzymes. This indicates that while new enzymes are attached preferentially to highly connected enzymes, these highly connected enzymes have sometimes been introduced into the E. coli genome by horizontal gene transfer. We speculate

  6. An integrated text mining framework for metabolic interaction network reconstruction

    Directory of Open Access Journals (Sweden)

    Preecha Patumcharoenpol

    2016-03-01

    Full Text Available Text mining (TM in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals as well as their relationships. Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions through metabolic events. Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module—MEE and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module—MINR. The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score. Evaluation of the MEE module using the constructed Metabolic Entities (ME corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively. As for the testing of the entity tagger for Gene and Protein (GP and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis. Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score >70%. Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme–metabolite interaction, respectively. This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network. This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network. The ME corpus, test corpus, source

  7. Signatures of arithmetic simplicity in metabolic network architecture.

    Directory of Open Access Journals (Sweden)

    William J Riehl

    2010-04-01

    Full Text Available Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that properties similar to those predicted for the artificial chemistry hold also for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity.

  8. Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles

    Science.gov (United States)

    Jia, Gengjie; Stephanopoulos, Gregory; Gunawan, Rudiyanto

    2012-01-01

    Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics. PMID:24957767

  9. Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles

    Directory of Open Access Journals (Sweden)

    Gengjie Jia

    2012-11-01

    Full Text Available Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA kinetics.

  10. Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

    Science.gov (United States)

    Thomas, Alex; Rahmanian, Sorena; Bordbar, Aarash; Palsson, Bernhard Ø.; Jamshidi, Neema

    2014-01-01

    Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.

  11. Facilitative Components of Collaborative Learning: A Review of Nine Health Research Networks.

    Science.gov (United States)

    Leroy, Lisa; Rittner, Jessica Levin; Johnson, Karin E; Gerteis, Jessie; Miller, Therese

    2017-02-01

    Collaborative research networks are increasingly used as an effective mechanism for accelerating knowledge transfer into policy and practice. This paper explored the characteristics and collaborative learning approaches of nine health research networks. Semi-structured interviews with representatives from eight diverse US health services research networks conducted between November 2012 and January 2013 and program evaluation data from a ninth. The qualitative analysis assessed each network's purpose, duration, funding sources, governance structure, methods used to foster collaboration, and barriers and facilitators to collaborative learning. The authors reviewed detailed notes from the interviews to distill salient themes. Face-to-face meetings, intentional facilitation and communication, shared vision, trust among members and willingness to work together were key facilitators of collaborative learning. Competing priorities for members, limited funding and lack of long-term support and geographic dispersion were the main barriers to coordination and collaboration across research network members. The findings illustrate the importance of collaborative learning in research networks and the challenges to evaluating the success of research network functionality. Conducting readiness assessments and developing process and outcome evaluation metrics will advance the design and show the impact of collaborative research networks. Copyright © 2017 Longwoods Publishing.

  12. Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data.

    Directory of Open Access Journals (Sweden)

    Andreas Kuehne

    2017-06-01

    Full Text Available In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.

  13. Metabolic Network Constrains Gene Regulation of C4 Photosynthesis: The Case of Maize.

    Science.gov (United States)

    Robaina-Estévez, Semidán; Nikoloski, Zoran

    2016-05-01

    Engineering C3 plants to increase their efficiency of carbon fixation as well as of nitrogen and water use simultaneously may be facilitated by understanding the mechanisms that underpin the C4 syndrome. Existing experimental studies have indicated that the emergence of the C4 syndrome requires co-ordination between several levels of cellular organization, from gene regulation to metabolism, across two co-operating cell systems-mesophyll and bundle sheath cells. Yet, determining the extent to which the structure of the C4 plant metabolic network may constrain gene expression remains unclear, although it will provide an important consideration in engineering C4 photosynthesis in C3 plants. Here, we utilize flux coupling analysis with the second-generation maize metabolic models to investigate the correspondence between metabolic network structure and transcriptomic phenotypes along the maize leaf gradient. The examined scenarios with publically available data from independent experiments indicate that the transcriptomic programs of the two cell types are co-ordinated, quantitatively and qualitatively, due to the presence of coupled metabolic reactions in specific metabolic pathways. Taken together, our study demonstrates that precise quantitative coupling will have to be achieved in order to ensure a successfully engineered transition from C3 to C4 crops. © The Author 2016. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.

  14. CardioNet: A human metabolic network suited for the study of cardiomyocyte metabolism

    Directory of Open Access Journals (Sweden)

    Karlstädt Anja

    2012-08-01

    Full Text Available Abstract Background Availability of oxygen and nutrients in the coronary circulation is a crucial determinant of cardiac performance. Nutrient composition of coronary blood may significantly vary in specific physiological and pathological conditions, for example, administration of special diets, long-term starvation, physical exercise or diabetes. Quantitative analysis of cardiac metabolism from a systems biology perspective may help to a better understanding of the relationship between nutrient supply and efficiency of metabolic processes required for an adequate cardiac output. Results Here we present CardioNet, the first large-scale reconstruction of the metabolic network of the human cardiomyocyte comprising 1793 metabolic reactions, including 560 transport processes in six compartments. We use flux-balance analysis to demonstrate the capability of the network to accomplish a set of 368 metabolic functions required for maintaining the structural and functional integrity of the cell. Taking the maintenance of ATP, biosynthesis of ceramide, cardiolipin and further important phospholipids as examples, we analyse how a changed supply of glucose, lactate, fatty acids and ketone bodies may influence the efficiency of these essential processes. Conclusions CardioNet is a functionally validated metabolic network of the human cardiomyocyte that enables theorectical studies of cellular metabolic processes crucial for the accomplishment of an adequate cardiac output.

  15. A Local Area Network to Facilitate Office Automation in the Administrative Sciences Department.

    Science.gov (United States)

    1986-03-27

    NO (nclude Securtry Classification) A L.OCAL AREA NET.CO IRK TO FACILITATE OFFICE AUTOMATION IN THE ADMINISTR.ATIVE SCIE-NCES DL P.RTMENT ullmHoward...edltorls are ob~solete Approved for public release; distribution is unlimited. A Local Area Network to Facilitate Office Automation in the... office automation . Accesion For NTIS CRA&I ii DTIC TAB 0 Unannounced 0 Justification ................. BY .......... . ........ .. ... D . ibution

  16. The TIM Barrel Architecture Facilitated the Early Evolution of Protein-Mediated Metabolism.

    Science.gov (United States)

    Goldman, Aaron David; Beatty, Joshua T; Landweber, Laura F

    2016-01-01

    The triosephosphate isomerase (TIM) barrel protein fold is a structurally repetitive architecture that is present in approximately 10% of all enzymes. It is generally assumed that this ubiquity in modern proteomes reflects an essential historical role in early protein-mediated metabolism. Here, we provide quantitative and comparative analyses to support several hypotheses about the early importance of the TIM barrel architecture. An information theoretical analysis of protein structures supports the hypothesis that the TIM barrel architecture could arise more easily by duplication and recombination compared to other mixed α/β structures. We show that TIM barrel enzymes corresponding to the most taxonomically broad superfamilies also have the broadest range of functions, often aided by metal and nucleotide-derived cofactors that are thought to reflect an earlier stage of metabolic evolution. By comparison to other putatively ancient protein architectures, we find that the functional diversity of TIM barrel proteins cannot be explained simply by their antiquity. Instead, the breadth of TIM barrel functions can be explained, in part, by the incorporation of a broad range of cofactors, a trend that does not appear to be shared by proteins in general. These results support the hypothesis that the simple and functionally general TIM barrel architecture may have arisen early in the evolution of protein biosynthesis and provided an ideal scaffold to facilitate the metabolic transition from ribozymes, peptides, and geochemical catalysts to modern protein enzymes.

  17. Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

    Science.gov (United States)

    Martínez, Verónica S.; Quek, Lake-Ee; Nielsen, Lars K.

    2014-01-01

    Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. PMID:25028891

  18. Enumeration of minimal stoichiometric precursor sets in metabolic networks

    NARCIS (Netherlands)

    Andrade, R.; Wannagat, M.; Coimbra Klein, C.; Acuna, V.; Marchetti Spaccamela, A.; Vieira Milreu, P.; Stougie, L.; Sagot, M.-F.

    2016-01-01

    Background: What an organism needs at least from its environment to produce a set of metabolites, e.g. target(s) of interest and/or biomass, has been called a minimal precursor set. Early approaches to enumerate all minimal precursor sets took into account only the topology of the metabolic network

  19. Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism

    DEFF Research Database (Denmark)

    Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu

    2012-01-01

    Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and...

  20. Global Network Reorganization During Dynamic Adaptations of Bacillus subtilis Metabolism

    NARCIS (Netherlands)

    Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu; Uhr, Markus; Muntel, Jan; Botella, Eric; Hessling, Bernd; Kleijn, Roelco Jacobus; Le Chat, Ludovic; Lecointe, Francois; Maeder, Ulrike; Nicolas, Pierre; Piersma, Sjouke; Ruegheimer, Frank; Becher, Doerte; Bessieres, Philippe; Bidnenko, Elena; Denham, Emma L.; Dervyn, Etienne; Devine, Kevin M.; Doherty, Geoff; Drulhe, Samuel; Felicori, Liza; Fogg, Mark J.; Goelzer, Anne; Hansen, Annette; Harwood, Colin R.; Hecker, Michael; Hubner, Sebastian; Hultschig, Claus; Jarmer, Hanne; Klipp, Edda; Leduc, Aurelie; Lewis, Peter; Molina, Frank; Noirot, Philippe; Peres, Sabine; Pigeonneau, Nathalie; Pohl, Susanne; Rasmussen, Simon; Rinn, Bernd; Schaffer, Marc; Schnidder, Julian; Schwikowski, Benno; Van Dijl, Jan Maarten; Veiga, Patrick; Walsh, Sean; Wilkinson, Anthony J.; Stelling, Joerg; Aymerich, Stephane; Sauer, Uwe

    2012-01-01

    Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and

  1. Underground metabolism: network-level perspective and biotechnological potential

    DEFF Research Database (Denmark)

    Notebaart, Richard A; Kintses, Bálint; Feist, Adam

    2018-01-01

    A key challenge in molecular systems biology is understanding how new pathways arise during evolution and how to exploit them for biotechnological applications. New pathways in metabolic networks often evolve by recruiting weak promiscuous activities of pre-existing enzymes. Here we describe recent...

  2. Metabolic networks in epilepsy by MR spectroscopic imaging.

    Science.gov (United States)

    Pan, J W; Spencer, D D; Kuzniecky, R; Duckrow, R B; Hetherington, H; Spencer, S S

    2012-12-01

    The concept of an epileptic network has long been suggested from both animal and human studies of epilepsy. Based on the common observation that the MR spectroscopic imaging measure of NAA/Cr is sensitive to neuronal function and injury, we use this parameter to assess for the presence of a metabolic network in mesial temporal lobe epilepsy (MTLE) patients. A multivariate factor analysis is performed with controls and MTLE patients, using NAA/Cr measures from 12 loci: the bilateral hippocampi, thalami, basal ganglia, and insula. The factor analysis determines which and to what extent these loci are metabolically covarying. We extract two independent factors that explain the data's variability in control and MTLE patients. In controls, these factors characterize a 'thalamic' and 'dominant subcortical' function. The MTLE patients also exhibit a 'thalamic' factor, in addition to a second factor involving the ipsilateral insula and bilateral basal ganglia. These data suggest that MTLE patients demonstrate a metabolic network that involves the thalami, also seen in controls. The MTLE patients also display a second set of metabolically covarying regions that may be a manifestation of the epileptic network that characterizes limbic seizure propagation. © 2012 John Wiley & Sons A/S.

  3. Estimating the size of the solution space of metabolic networks

    Directory of Open Access Journals (Sweden)

    Mulet Roberto

    2008-05-01

    Full Text Available Abstract Background Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations. Results In this work we propose a novel algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints. Using a technique derived from the fields of statistical physics and information theory we designed a message-passing algorithm to estimate the size of the affine space containing all possible steady-state flux distributions of metabolic networks. The algorithm, based on the well known Bethe approximation, can be used to approximately compute the volume of a non full-dimensional convex polytope in high dimensions. We first compare the accuracy of the predictions with an exact algorithm on small random metabolic networks. We also verify that the predictions of the algorithm match closely those of Monte Carlo based methods in the case of the Red Blood Cell metabolic network. Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism. Finally we analyze the statistical properties of the average fluxes of the reactions in the E. coli metabolic network. Conclusion We propose a

  4. Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks.

    Science.gov (United States)

    Kiparissides, A; Hatzimanikatis, V

    2017-01-01

    The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. Constraint based methods, such as Flux Balance Analysis (FBA) and Thermodynamics-based flux analysis (TFA), are commonly used to estimate the flow of metabolites through genome-wide metabolic networks, making it possible to identify the ranges of flux values that are consistent with the studied physiological and thermodynamic conditions. However, unless key intracellular fluxes and metabolite concentrations are known, constraint-based models lead to underdetermined problem formulations. This lack of information propagates as uncertainty in the estimation of fluxes and basic reaction properties such as the determination of reaction directionalities. Therefore, knowledge of which metabolites, if measured, would contribute the most to reducing this uncertainty can significantly improve our ability to define the internal state of the cell. In the present work we combine constraint based modeling, Design of Experiments (DoE) and Global Sensitivity Analysis (GSA) into the Thermodynamics-based Metabolite Sensitivity Analysis (TMSA) method. TMSA ranks metabolites comprising a metabolic network based on their ability to constrain the gamut of possible solutions to a limited, thermodynamically consistent set of internal states. TMSA is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier

  5. Metabolic crosstalk between membrane and storage lipids facilitates heat stress management in Schizosaccharomyces pombe

    Science.gov (United States)

    Péter, Mária; Glatz, Attila; Gudmann, Péter; Gombos, Imre; Török, Zsolt; Horváth, Ibolya; Vígh, László

    2017-01-01

    Cell membranes actively participate in stress sensing and signalling. Here we present the first in-depth lipidomic analysis to characterize alterations in the fission yeast Schizosaccharomyces pombe in response to mild heat stress (HS). The lipidome was assessed by a simple one-step methanolic extraction. Genetic manipulations that altered triglyceride (TG) content in the absence or presence of HS gave rise to distinct lipidomic fingerprints for S. pombe. Cells unable to produce TG demonstrated long-lasting growth arrest and enhanced signalling lipid generation. Our results reveal that metabolic crosstalk between membrane and storage lipids facilitates homeostatic maintenance of the membrane physical/chemical state that resists negative effects on cell growth and viability in response to HS. We propose a novel stress adaptation mechanism in which heat-induced TG synthesis contributes to membrane rigidization by accommodating unsaturated fatty acids of structural lipids, enabling their replacement by newly synthesized saturated fatty acids. PMID:28282432

  6. Facilitating participation:From the EML web site to the Learning Network for Learning Design

    NARCIS (Netherlands)

    Hummel, Hans; Tattersall, Colin; Burgos, Daniel; Brouns, Francis; Kurvers, Hub; Koper, Rob

    2004-01-01

    Please refer to original publication: Hummel, H., Tattersall, C., Burgos, D., Brouns, F., Kurvers, H., & Koper, R. (2005). Facilitating participation: From the EML website to the Learning Network for Learning Design. Interactive Learning Environments,13(1-2), 55-69

  7. Facilitating community building in Learning Networks through peer tutoring in ad hoc transient communities

    NARCIS (Netherlands)

    Kester, Liesbeth; Sloep, Peter; Van Rosmalen, Peter; Brouns, Francis; Koné, Malik; Koper, Rob

    2006-01-01

    De volledige referentie is: Kester, L., Sloep, P. B., Van Rosmalen, P., Brouns, F., Koné, M., & Koper, R. (2007). Facilitating Community Building in Learning Networks Through Peer-Tutoring in Ad Hoc Transient Communities. International Journal of Web based Communities, 3(2), 198-205.

  8. Novel Plasmodium falciparum metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance.

    Science.gov (United States)

    Carey, Maureen A; Papin, Jason A; Guler, Jennifer L

    2017-07-19

    Malaria remains a major public health burden and resistance has emerged to every antimalarial on the market, including the frontline drug, artemisinin. Our limited understanding of Plasmodium biology hinders the elucidation of resistance mechanisms. In this regard, systems biology approaches can facilitate the integration of existing experimental knowledge and further understanding of these mechanisms. Here, we developed a novel genome-scale metabolic network reconstruction, iPfal17, of the asexual blood-stage P. falciparum parasite to expand our understanding of metabolic changes that support resistance. We identified 11 metabolic tasks to evaluate iPfal17 performance. Flux balance analysis and simulation of gene knockouts and enzyme inhibition predict candidate drug targets unique to resistant parasites. Moreover, integration of clinical parasite transcriptomes into the iPfal17 reconstruction reveals patterns associated with antimalarial resistance. These results predict that artemisinin sensitive and resistant parasites differentially utilize scavenging and biosynthetic pathways for multiple essential metabolites, including folate and polyamines. Our findings are consistent with experimental literature, while generating novel hypotheses about artemisinin resistance and parasite biology. We detect evidence that resistant parasites maintain greater metabolic flexibility, perhaps representing an incomplete transition to the metabolic state most appropriate for nutrient-rich blood. Using this systems biology approach, we identify metabolic shifts that arise with or in support of the resistant phenotype. This perspective allows us to more productively analyze and interpret clinical expression data for the identification of candidate drug targets for the treatment of resistant parasites.

  9. (Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis.

    Science.gov (United States)

    He, Fei; Fromion, Vincent; Westerhoff, Hans V

    2013-11-21

    Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. This study integrates control engineering and classical MCA augmented with supply-demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the 'integral control' (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of 'integral control' should rarely be expected to lead to the 'perfect adaptation': although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems

  10. Sensitivity of chemical reaction networks: a structural approach. 1. Examples and the carbon metabolic network.

    Science.gov (United States)

    Mochizuki, Atsushi; Fiedler, Bernold

    2015-02-21

    In biological cells, chemical reaction pathways lead to complex network systems like metabolic networks. One experimental approach to the dynamics of such systems examines their "sensitivity": each enzyme mediating a reaction in the system is increased/decreased or knocked out separately, and the responses in the concentrations of chemicals or their fluxes are observed. In this study, we present a mathematical method, named structural sensitivity analysis, to determine the sensitivity of reaction systems from information on the network alone. We investigate how the sensitivity responses of chemicals in a reaction network depend on the structure of the network, and on the position of the perturbed reaction in the network. We establish and prove some general rules which relate the sensitivity response to the structure of the underlying network. We describe a hierarchical pattern in the flux response which is governed by branchings in the network. We apply our method to several hypothetical and real life chemical reaction networks, including the metabolic network of the Escherichia coli TCA cycle. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. Context-specific metabolic networks are consistent with experiments.

    Directory of Open Access Journals (Sweden)

    Scott A Becker

    2008-05-01

    Full Text Available Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are "genome-scale" and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

  12. Bacterial Unculturability and the Formation of Intercellular Metabolic Networks.

    Science.gov (United States)

    Pande, Samay; Kost, Christian

    2017-05-01

    The majority of known bacterial species cannot be cultivated under laboratory conditions. Here we argue that the adaptive emergence of obligate metabolic interactions in natural bacterial communities can explain this pattern. Bacteria commonly release metabolites into the external environment. Accumulating pools of extracellular metabolites create an ecological niche that benefits auxotrophic mutants, which have lost the ability to autonomously produce the corresponding metabolites. In addition to a diffusion-based metabolite transfer, auxotrophic cells can use contact-dependent means to obtain nutrients from other co-occurring cells. Spatial colocalisation and a continuous coevolution further increase the nutritional dependency and optimise fluxes through combined metabolic networks. Thus, bacteria likely function as networks of interacting cells that reciprocally exchange nutrients and biochemical functions rather than as physiologically autonomous units. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. The evolution of metabolic networks of E. coli

    Directory of Open Access Journals (Sweden)

    Baumler David J

    2011-11-01

    Full Text Available Abstract Background Despite the availability of numerous complete genome sequences from E. coli strains, published genome-scale metabolic models exist only for two commensal E. coli strains. These models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for E. coli strains could enhance these efforts. Results We used the genomic information from 16 E. coli strains to generate an E. coli pangenome metabolic network by evaluating their collective 76,990 ORFs. Each of these ORFs was assigned to one of 17,647 ortholog groups including ORFs associated with reactions in the most recent metabolic model for E. coli K-12. For orthologous groups that contain an ORF already represented in the MG1655 model, the gene to protein to reaction associations represented in this model could then be easily propagated to other E. coli strain models. All remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model (iEco1712_pan. The pangenome model included reactions from a metabolic model update for E. coli K-12 MG1655 (iEco1339_MG1655 and enabled development of five additional strain-specific genome-scale metabolic models. These additional models include a second K-12 strain (iEco1335_W3110 and four pathogenic strains (two enterohemorrhagic E. coli O157:H7 and two uropathogens. When compared to the E. coli K-12 models, the metabolic models for the enterohemorrhagic (iEco1344_EDL933 and iEco1345_Sakai and uropathogenic strains (iEco1288_CFT073 and iEco1301_UTI89 contained numerous lineage-specific gene and reaction differences. All six E. coli models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with 0.2% (w/v glucose. An ancestral

  14. Merging social networking environments and formal learning environments to support and facilitate interprofessional instruction.

    Science.gov (United States)

    King, Sharla; Greidanus, Elaine; Carbonaro, Michael; Drummond, Jane; Patterson, Steven

    2009-04-28

    This study describes the redesign of an interprofessional team development course for health science students. A theoretical model is hypothesized as a framework for the redesign process, consisting of two themes: 1) the increasing trend among post-secondary students to participate in social networking (e.g., Facebook, Second Life) and 2) the need for healthcare educators to provide interprofessional training that results in effective communities of practice and better patient care. The redesign focused on increasing the relevance of the course through the integration of custom-designed technology to facilitate social networking during their interprofessional education. Results suggest that students in an educationally structured social networking environment can be guided to join learning communities quickly and access course materials. More research and implementation work is required to effectively develop interprofessional health sciences communities in a combined face-to-face and on-line social networking context.

  15. From Accumulation to Degradation: Reprogramming Polyamine Metabolism Facilitates Dark-Induced Senescence in Barley Leaf Cells.

    Science.gov (United States)

    Sobieszczuk-Nowicka, Ewa; Kubala, Szymon; Zmienko, Agnieszka; Małecka, Arleta; Legocka, Jolanta

    2015-01-01

    The aim of this study was to analyze whether polyamine (PA) metabolism is involved in dark-induced Hordeum vulgare L. 'Nagrad' leaf senescence. In the cell, the titer of PAs is relatively constant and is carefully controlled. Senescence-dependent increases in the titer of the free PAs putrescine, spermidine, and spermine occurred when the process was induced, accompanied by the formation of putrescine conjugates. The addition of the anti-senescing agent cytokinin, which delays senescence, to dark-incubated leaves slowed the senescence-dependent PA accumulation. A feature of the senescence process was initial accumulation of PAs at the beginning of the process and their subsequent decrease during the later stages. Indeed, the process was accompanied by both enhanced expression of PA biosynthesis and catabolism genes and an increase in the activity of enzymes involved in the two metabolic pathways. To confirm whether the capacity of the plant to control senescence might be linked to PA, chlorophyll fluorescence parameters, and leaf nitrogen status in senescing barley leaves were measured after PA catabolism inhibition and exogenously applied γ-aminobutyric acid (GABA). The results obtained by blocking putrescine oxidation showed that the senescence process was accelerated. However, when the inhibitor was applied together with GABA, senescence continued without disruption. On the other hand, inhibition of spermidine and spermine oxidation delayed the process. It could be concluded that in dark-induced leaf senescence, the initial accumulation of PAs leads to facilitating their catabolism. Putrescine supports senescence through GABA production and spermidine/spermine supports senescence-dependent degradation processes, is verified by H2O2 generation.

  16. Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks*

    Science.gov (United States)

    Krumholz, Elias W.; Libourel, Igor G. L.

    2015-01-01

    Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable. PMID:26041773

  17. Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks.

    Science.gov (United States)

    Krumholz, Elias W; Libourel, Igor G L

    2015-07-31

    Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

  18. Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling.

    Directory of Open Access Journals (Sweden)

    Christine T Ferrara

    2008-03-01

    Full Text Available Although numerous quantitative trait loci (QTL influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptin(ob/ob and the diabetes-susceptible BTBR leptin(ob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines. We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.

  19. Current Understanding of the Formation and Adaptation of Metabolic Systems Based on Network Theory

    Directory of Open Access Journals (Sweden)

    Kazuhiro Takemoto

    2012-07-01

    Full Text Available Formation and adaptation of metabolic networks has been a long-standing question in biology. With recent developments in biotechnology and bioinformatics, the understanding of metabolism is progressively becoming clearer from a network perspective. This review introduces the comprehensive metabolic world that has been revealed by a wide range of data analyses and theoretical studies; in particular, it illustrates the role of evolutionary events, such as gene duplication and horizontal gene transfer, and environmental factors, such as nutrient availability and growth conditions, in evolution of the metabolic network. Furthermore, the mathematical models for the formation and adaptation of metabolic networks have also been described, according to the current understanding from a perspective of metabolic networks. These recent findings are helpful in not only understanding the formation of metabolic networks and their adaptation, but also metabolic engineering.

  20. Developmental changes in the metabolic network of snapdragon flowers.

    Directory of Open Access Journals (Sweden)

    Joëlle K Muhlemann

    Full Text Available Evolutionary and reproductive success of angiosperms, the most diverse group of land plants, relies on visual and olfactory cues for pollinator attraction. Previous work has focused on elucidating the developmental regulation of pathways leading to the formation of pollinator-attracting secondary metabolites such as scent compounds and flower pigments. However, to date little is known about how flowers control their entire metabolic network to achieve the highly regulated production of metabolites attracting pollinators. Integrative analysis of transcripts and metabolites in snapdragon sepals and petals over flower development performed in this study revealed a profound developmental remodeling of gene expression and metabolite profiles in petals, but not in sepals. Genes up-regulated during petal development were enriched in functions related to secondary metabolism, fatty acid catabolism, and amino acid transport, whereas down-regulated genes were enriched in processes involved in cell growth, cell wall formation, and fatty acid biosynthesis. The levels of transcripts and metabolites in pathways leading to scent formation were coordinately up-regulated during petal development, implying transcriptional induction of metabolic pathways preceding scent formation. Developmental gene expression patterns in the pathways involved in scent production were different from those of glycolysis and the pentose phosphate pathway, highlighting distinct developmental regulation of secondary metabolism and primary metabolic pathways feeding into it.

  1. Hospital networks: how to make them work in Belgium? Facilitators and barriers of different governance models.

    Science.gov (United States)

    De Pourcq, Kaat; De Regge, Melissa; Van den Heede, Koen; Van de Voorde, Carine; Gemmel, Paul; Eeckloo, Kristof

    2018-03-29

    Objectives This study aims to identify the facilitators and barriers to governance models of hospital collaborations. The country-specific characteristics of the Belgian healthcare system and legislation are taken into account. Methods A case study was carried out in six Belgian hospital collaborations. Different types of governance models were selected: two health systems, two participant-governed networks, and two lead-organization-governed networks. Within these collaborations, 43 people were interviewed. Results All structures have both advantages and disadvantages. It is important that the governance model fits the network. However, structural, procedural, and especially contextual factors also affect the collaborations, such as alignment of hospitals' and professionals' goals, competition, distance, level of integrated care, time needed for decision-making, and legal and financial incentives. Conclusion The fit between the governance model and the collaboration can facilitate the functioning of a collaboration. The main barriers we identified are contextual factors. The Belgian government needs to play a major role in facilitating collaboration.

  2. Facilitating efficient augmentation of transmission networks to connect renewable energy generation: the Australian experience

    International Nuclear Information System (INIS)

    Wright, Glen

    2012-01-01

    Australia is heavily dependent on coal for electricity generation. The Renewable Energy Target has spurred growth in the utilization of renewable energy sources, with further growth expected into the future. Australia's strongest renewable energy sources are generally distant from the transmission network in resource ‘basins’. Investment is needed to augment the transmission network to enable delivery of electricity from these sources to consumers. Considerable economies of scale flow from anticipating the connection of numerous generators in an area over time and sizing augmentations accordingly. Following a lengthy rulemaking process, the National Electricity Rules were recently amended by a new rule, designed to facilitate the construction of such efficiently sized augmentations. However, the new rule is more conservative than initially envisaged, making little substantive change to the current frameworks for augmentation and connection. This paper outlines these frameworks and the rulemaking process and identifies the key debates surrounding the rule change are identified. This paper then provides a detailed analysis of the new rule, concluding that it is defective in a number of respects and is unlikely to result in the efficient and timely augmentation of the network needed to unlock the potential of Australia's strongest renewable energy resources. - Highlights: ► Remoteness of renewable energy sources is a barrier to greater renewable energy utilization. ► Significant economies of scale flow from efficiently-sized transmission network augmentation. ► Current frameworks in Australia do not incentivise efficiently-sized network augmentations. ► The lack of property rights in an augmentation is particularly problematic. ► The new Scale Efficient Network Extensions rule is not apt to facilitate efficiently-sized network augmentations.

  3. METANNOGEN: compiling features of biochemical reactions needed for the reconstruction of metabolic networks

    Directory of Open Access Journals (Sweden)

    Holzhütter Hermann-Georg

    2007-01-01

    Full Text Available Abstract Background One central goal of computational systems biology is the mathematical modelling of complex metabolic reaction networks. The first and most time-consuming step in the development of such models consists in the stoichiometric reconstruction of the network, i. e. compilation of all metabolites, reactions and transport processes relevant to the considered network and their assignment to the various cellular compartments. Therefore an information system is required to collect and manage data from different databases and scientific literature in order to generate a metabolic network of biochemical reactions that can be subjected to further computational analyses. Results The computer program METANNOGEN facilitates the reconstruction of metabolic networks. It uses the well-known database of biochemical reactions KEGG of biochemical reactions as primary information source from which biochemical reactions relevant to the considered network can be selected, edited and stored in a separate, user-defined database. Reactions not contained in KEGG can be entered manually into the system. To aid the decision whether or not a reaction selected from KEGG belongs to the considered network METANNOGEN contains information of SWISSPROT and ENSEMBL and provides Web links to a number of important information sources like METACYC, BRENDA, NIST, and REACTOME. If a reaction is reported to occur in more than one cellular compartment, a corresponding number of reactions is generated each referring to one specific compartment. Transport processes of metabolites are entered like chemical reactions where reactants and products have different compartment attributes. The list of compartmentalized biochemical reactions and membrane transport processes compiled by means of METANNOGEN can be exported as an SBML file for further computational analysis. METANNOGEN is highly customizable with respect to the content of the SBML output file, additional data

  4. Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network

    Directory of Open Access Journals (Sweden)

    Holger eFinger

    2014-01-01

    Full Text Available Synchronization has been suggested as a mechanism of binding distributed feature representations facilitating segmentation of visual stimuli. Here we investigate this concept based on unsupervised learning using natural visual stimuli. We simulate dual-variable neural oscillators with separate activation and phase variables. The binding of a set of neurons is coded by synchronized phase variables. The network of tangential synchronizing connections learned from the induced activations exhibits small-world properties and allows binding even over larger distances. We evaluate the resulting dynamic phase maps using segmentation masks labeled by human experts. Our simulation results show a continuously increasing phase synchrony between neurons within the labeled segmentation masks. The evaluation of the network dynamics shows that the synchrony between network nodes establishes a relational coding of the natural image inputs. This demonstrates that the concept of binding by synchrony is applicable in the context of unsupervised learning using natural visual stimuli.

  5. Thermodynamic calculations for biochemical transport and reaction processes in metabolic networks

    NARCIS (Netherlands)

    Jol, Stefan J; Kümmel, Anne; Hatzimanikatis, Vassily; Beard, Daniel A; Heinemann, Matthias

    2010-01-01

    Thermodynamic analysis of metabolic networks has recently generated increasing interest for its ability to add constraints on metabolic network operation, and to combine metabolic fluxes and metabolite measurements in a mechanistic manner. Concepts for the calculation of the change in Gibbs energy

  6. Predicting selective drug targets in cancer through metabolic networks

    Science.gov (United States)

    Folger, Ori; Jerby, Livnat; Frezza, Christian; Gottlieb, Eyal; Ruppin, Eytan; Shlomi, Tomer

    2011-01-01

    The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled. PMID:21694718

  7. Enumeration of minimal stoichiometric precursor sets in metabolic networks.

    Science.gov (United States)

    Andrade, Ricardo; Wannagat, Martin; Klein, Cecilia C; Acuña, Vicente; Marchetti-Spaccamela, Alberto; Milreu, Paulo V; Stougie, Leen; Sagot, Marie-France

    2016-01-01

    What an organism needs at least from its environment to produce a set of metabolites, e.g. target(s) of interest and/or biomass, has been called a minimal precursor set. Early approaches to enumerate all minimal precursor sets took into account only the topology of the metabolic network (topological precursor sets). Due to cycles and the stoichiometric values of the reactions, it is often not possible to produce the target(s) from a topological precursor set in the sense that there is no feasible flux. Although considering the stoichiometry makes the problem harder, it enables to obtain biologically reasonable precursor sets that we call stoichiometric. Recently a method to enumerate all minimal stoichiometric precursor sets was proposed in the literature. The relationship between topological and stoichiometric precursor sets had however not yet been studied. Such relationship between topological and stoichiometric precursor sets is highlighted. We also present two algorithms that enumerate all minimal stoichiometric precursor sets. The first one is of theoretical interest only and is based on the above mentioned relationship. The second approach solves a series of mixed integer linear programming problems. We compared the computed minimal precursor sets to experimentally obtained growth media of several Escherichia coli strains using genome-scale metabolic networks. The results show that the second approach efficiently enumerates minimal precursor sets taking stoichiometry into account, and allows for broad in silico studies of strains or species interactions that may help to understand e.g. pathotype and niche-specific metabolic capabilities. sasita is written in Java, uses cplex as LP solver and can be downloaded together with all networks and input files used in this paper at http://www.sasita.gforge.inria.fr.

  8. (Im) Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis

    NARCIS (Netherlands)

    He, F.; Fromion, V.; Westerhoff, H.V.

    2013-01-01

    Background: Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a

  9. Amino Acid Flux from Metabolic Network Benefits Protein Translation: the Role of Resource Availability.

    Science.gov (United States)

    Hu, Xiao-Pan; Yang, Yi; Ma, Bin-Guang

    2015-06-09

    Protein translation is a central step in gene expression and affected by many factors such as codon usage bias, mRNA folding energy and tRNA abundance. Despite intensive previous studies, how metabolic amino acid supply correlates with protein translation efficiency remains unknown. In this work, we estimated the amino acid flux from metabolic network for each protein in Escherichia coli and Saccharomyces cerevisiae by using Flux Balance Analysis. Integrated with the mRNA expression level, protein abundance and ribosome profiling data, we provided a detailed description of the role of amino acid supply in protein translation. Our results showed that amino acid supply positively correlates with translation efficiency and ribosome density. Moreover, with the rank-based regression model, we found that metabolic amino acid supply facilitates ribosome utilization. Based on the fact that the ribosome density change of well-amino-acid-supplied genes is smaller than poorly-amino-acid-supply genes under amino acid starvation, we reached the conclusion that amino acid supply may buffer ribosome density change against amino acid starvation and benefit maintaining a relatively stable translation environment. Our work provided new insights into the connection between metabolic amino acid supply and protein translation process by revealing a new regulation strategy that is dependent on resource availability.

  10. Origins of Specificity and Promiscuity in Metabolic Networks

    Science.gov (United States)

    Carbonell, Pablo; Lecointre, Guillaume; Faulon, Jean-Loup

    2011-01-01

    How enzymes have evolved to their present form is linked to the question of how pathways emerged and evolved into extant metabolic networks. To investigate this mechanism, we have explored the chemical diversity present in a largely unbiased data set of catalytic reactions processed by modern enzymes across the tree of life. In order to get a quantitative estimate of enzyme chemical diversity, we measure enzyme multispecificity or promiscuity using the reaction molecular signatures. Our main finding is that reactions that are catalyzed by a highly specific enzyme are shared by poorly divergent species, suggesting a later emergence of this function during evolution. In contrast, reactions that are catalyzed by highly promiscuous enzymes are more likely to appear uniformly distributed across species in the tree of life. From a functional point of view, promiscuous enzymes are mainly involved in amino acid and lipid metabolisms, which might be associated with the earliest form of biochemical reactions. In this way, results presented in this paper might assist us with the identification of primeval promiscuous catalytic functions contributing to life's minimal metabolism. PMID:22052908

  11. Facilitating career advancement for women in the Geosciences through the Earth Science Women's Network (ESWN)

    Science.gov (United States)

    Hastings, M. G.; Kontak, R.; Holloway, T.; Kogan, M.; Laursen, S. L.; Marin-Spiotta, E.; Steiner, A. L.; Wiedinmyer, C.

    2011-12-01

    The Earth Science Women's Network (ESWN) is a network of women geoscientists, many of who are in the early stages of their careers. The mission of ESWN is to promote career development, build community, provide informal mentoring and support, and facilitate professional collaborations, all towards making women successful in their scientific careers. ESWN currently connects over 1000 women across the globe, and includes graduate students, postdoctoral associates, faculty from a diversity of colleges and universities, program managers, and government, non-government and industry researchers. ESWN facilitates communication between its members via an email listserv and in-person networking events, and also provides resources to the broader community through the public Earth Science Jobs Listserv that hosts over 1800 subscribers. With funding from a NSF ADVANCE PAID grant, our primary goals include growing our membership to serve a wider section of the geosciences community, designing and administering career development workshops, promoting professional networking at major scientific conferences, and developing web resources to build connections, collaborations, and peer mentoring for and among women in the Earth Sciences. Recognizing that women in particular face a number of direct and indirect biases while navigating their careers, we aim to provide a range of opportunities for professional development that emphasize different skills at different stages of career. For example, ESWN-hosted mini-workshops at national scientific conferences have targeted skill building for early career researchers (e.g., postdocs, tenure-track faculty), with a recent focus on raising extramural research funding and best practices for publishing in the geosciences literature. More concentrated, multi-day professional development workshops are offered annually with varying themes such as Defining Your Research Identity and Building Leadership Skills for Success in Scientific Organizations

  12. Reconstruction and in silico analysis of metabolic network for an oleaginous yeast, Yarrowia lipolytica.

    Directory of Open Access Journals (Sweden)

    Pengcheng Pan

    Full Text Available With the emergence of energy scarcity, the use of renewable energy sources such as biodiesel is becoming increasingly necessary. Recently, many researchers have focused their minds on Yarrowia lipolytica, a model oleaginous yeast, which can be employed to accumulate large amounts of lipids that could be further converted to biodiesel. In order to understand the metabolic characteristics of Y. lipolytica at a systems level and to examine the potential for enhanced lipid production, a genome-scale compartmentalized metabolic network was reconstructed based on a combination of genome annotation and the detailed biochemical knowledge from multiple databases such as KEGG, ENZYME and BIGG. The information about protein and reaction associations of all the organisms in KEGG and Expasy-ENZYME database was arranged into an EXCEL file that can then be regarded as a new useful database to generate other reconstructions. The generated model iYL619_PCP accounts for 619 genes, 843 metabolites and 1,142 reactions including 236 transport reactions, 125 exchange reactions and 13 spontaneous reactions. The in silico model successfully predicted the minimal media and the growing abilities on different substrates. With flux balance analysis, single gene knockouts were also simulated to predict the essential genes and partially essential genes. In addition, flux variability analysis was applied to design new mutant strains that will redirect fluxes through the network and may enhance the production of lipid. This genome-scale metabolic model of Y. lipolytica can facilitate system-level metabolic analysis as well as strain development for improving the production of biodiesels and other valuable products by Y. lipolytica and other closely related oleaginous yeasts.

  13. Robust detection and verification of linear relationships to generate metabolic networks using estimates of technical errors

    Directory of Open Access Journals (Sweden)

    Holschneider Matthias

    2007-05-01

    Full Text Available Abstract Background The size and magnitude of the metabolome, the ratio between individual metabolites and the response of metabolic networks is controlled by multiple cellular factors. A tight control over metabolite ratios will be reflected by a linear relationship of pairs of metabolite due to the flexibility of metabolic pathways. Hence, unbiased detection and validation of linear metabolic variance can be interpreted in terms of biological control. For robust analyses, criteria for rejecting or accepting linearities need to be developed despite technical measurement errors. The entirety of all pair wise linear metabolic relationships then yields insights into the network of cellular regulation. Results The Bayesian law was applied for detecting linearities that are validated by explaining the residues by the degree of technical measurement errors. Test statistics were developed and the algorithm was tested on simulated data using 3–150 samples and 0–100% technical error. Under the null hypothesis of the existence of a linear relationship, type I errors remained below 5% for data sets consisting of more than four samples, whereas the type II error rate quickly raised with increasing technical errors. Conversely, a filter was developed to balance the error rates in the opposite direction. A minimum of 20 biological replicates is recommended if technical errors remain below 20% relative standard deviation and if thresholds for false error rates are acceptable at less than 5%. The algorithm was proven to be robust against outliers, unlike Pearson's correlations. Conclusion The algorithm facilitates finding linear relationships in complex datasets, which is radically different from estimating linearity parameters from given linear relationships. Without filter, it provides high sensitivity and fair specificity. If the filter is activated, high specificity but only fair sensitivity is yielded. Total error rates are more favorable with

  14. Exploring photosynthesis evolution by comparative analysis of metabolic networks between chloroplasts and photosynthetic bacteria

    Directory of Open Access Journals (Sweden)

    Hou Jing

    2006-04-01

    Full Text Available Abstract Background Chloroplasts descended from cyanobacteria and have a drastically reduced genome following an endosymbiotic event. Many genes of the ancestral cyanobacterial genome have been transferred to the plant nuclear genome by horizontal gene transfer. However, a selective set of metabolism pathways is maintained in chloroplasts using both chloroplast genome encoded and nuclear genome encoded enzymes. As an organelle specialized for carrying out photosynthesis, does the chloroplast metabolic network have properties adapted for higher efficiency of photosynthesis? We compared metabolic network properties of chloroplasts and prokaryotic photosynthetic organisms, mostly cyanobacteria, based on metabolic maps derived from genome data to identify features of chloroplast network properties that are different from cyanobacteria and to analyze possible functional significance of those features. Results The properties of the entire metabolic network and the sub-network that consists of reactions directly connected to the Calvin Cycle have been analyzed using hypergraph representation. Results showed that the whole metabolic networks in chloroplast and cyanobacteria both possess small-world network properties. Although the number of compounds and reactions in chloroplasts is less than that in cyanobacteria, the chloroplast's metabolic network has longer average path length, a larger diameter, and is Calvin Cycle -centered, indicating an overall less-dense network structure with specific and local high density areas in chloroplasts. Moreover, chloroplast metabolic network exhibits a better modular organization than cyanobacterial ones. Enzymes involved in the same metabolic processes tend to cluster into the same module in chloroplasts. Conclusion In summary, the differences in metabolic network properties may reflect the evolutionary changes during endosymbiosis that led to the improvement of the photosynthesis efficiency in higher plants. Our

  15. A state of the art of metabolic networks of unicellular microalgae and cyanobacteria for biofuel production.

    Science.gov (United States)

    Baroukh, Caroline; Muñoz-Tamayo, Rafael; Steyer, Jean-Philippe; Bernard, Olivier

    2015-07-01

    The most promising and yet challenging application of microalgae and cyanobacteria is the production of renewable energy: biodiesel from microalgae triacylglycerols and bioethanol from cyanobacteria carbohydrates. A thorough understanding of microalgal and cyanobacterial metabolism is necessary to master and optimize biofuel production yields. To this end, systems biology and metabolic modeling have proven to be very efficient tools if supported by an accurate knowledge of the metabolic network. However, unlike heterotrophic microorganisms that utilize the same substrate for energy and as carbon source, microalgae and cyanobacteria require light for energy and inorganic carbon (CO2 or bicarbonate) as carbon source. This double specificity, together with the complex mechanisms of light capture, makes the representation of metabolic network nonstandard. Here, we review the existing metabolic networks of photoautotrophic microalgae and cyanobacteria. We highlight how these networks have been useful for gaining insight on photoautotrophic metabolism. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  16. Establishment Of The Shortest Route A Prototype For Facilitation In Road Network

    Directory of Open Access Journals (Sweden)

    Sumaira Yousuf Khan

    2015-08-01

    Full Text Available Calculating the shortest path between two locations in a road network is a significant problem in network analysis. Roads play a pivotal role in day to day activities of masses live in places and areas. They travel for various purposes that are to study to work to shop and to supply their goods and the like from one place to another place. Even in this modern era roads remain one of the mediums used most frequently for travel and transportation. Being ignorant of the shortest routes people sometimes have to travel long distances consume extra precious time money and bare undesirable mental stress. Karachi is the second most populated and the seventh biggest city of the world. It is the central place of Pakistan which is famous for industry banking trade and economic activity and there are places which frequently visit by the inhabitants for their miscellaneous requirements. Federal Board of Revenue FBR is one of the departments that deal with taxation and revenue generation in the country. A common man residing near or distant areas often visit FBR to settle their business property and tax related issues. In order to facilitate the masses an effort is being made to develop a prototype based on Dijkstras Algorithm DA to establish a shortest route that will help individuals in navigation and subsequently alleviate difficulties faced by them in travelling road networks.

  17. Microbial diversity and metabolic networks in acid mine drainage habitats.

    Science.gov (United States)

    Méndez-García, Celia; Peláez, Ana I; Mesa, Victoria; Sánchez, Jesús; Golyshina, Olga V; Ferrer, Manuel

    2015-01-01

    Acid mine drainage (AMD) emplacements are low-complexity natural systems. Low-pH conditions appear to be the main factor underlying the limited diversity of the microbial populations thriving in these environments, although temperature, ionic composition, total organic carbon, and dissolved oxygen are also considered to significantly influence their microbial life. This natural reduction in diversity driven by extreme conditions was reflected in several studies on the microbial populations inhabiting the various micro-environments present in such ecosystems. Early studies based on the physiology of the autochthonous microbiota and the growing success of omics-based methodologies have enabled a better understanding of microbial ecology and function in low-pH mine outflows; however, complementary omics-derived data should be included to completely describe their microbial ecology. Furthermore, recent updates on the distribution of eukaryotes and archaea recovered through sterile filtering (herein referred to as filterable fraction) in these environments demand their inclusion in the microbial characterization of AMD systems. In this review, we present a complete overview of the bacterial, archaeal (including filterable fraction), and eukaryotic diversity in these ecosystems, and include a thorough depiction of the metabolism and element cycling in AMD habitats. We also review different metabolic network structures at the organismal level, which is necessary to disentangle the role of each member of the AMD communities described thus far.

  18. A Bayesian approach to the evolution of metabolic networks on a phylogeny.

    Directory of Open Access Journals (Sweden)

    Aziz Mithani

    2010-08-01

    Full Text Available The availability of genomes of many closely related bacteria with diverse metabolic capabilities offers the possibility of tracing metabolic evolution on a phylogeny relating the genomes to understand the evolutionary processes and constraints that affect the evolution of metabolic networks. Using simple (independent loss/gain of reactions or complex (incorporating dependencies among reactions stochastic models of metabolic evolution, it is possible to study how metabolic networks evolve over time. Here, we describe a model that takes the reaction neighborhood into account when modeling metabolic evolution. The model also allows estimation of the strength of the neighborhood effect during the course of evolution. We present Gibbs samplers for sampling networks at the internal node of a phylogeny and for estimating the parameters of evolution over a phylogeny without exploring the whole search space by iteratively sampling from the conditional distributions of the internal networks and parameters. The samplers are used to estimate the parameters of evolution of metabolic networks of bacteria in the genus Pseudomonas and to infer the metabolic networks of the ancestral pseudomonads. The results suggest that pathway maps that are conserved across the Pseudomonas phylogeny have a stronger neighborhood structure than those which have a variable distribution of reactions across the phylogeny, and that some Pseudomonas lineages are going through genome reduction resulting in the loss of a number of reactions from their metabolic networks.

  19. A consensus yeast metabolic network obtained from a community approach to systems biology.

    NARCIS (Netherlands)

    Herrgard, M.J.; Swainston, N.; Dobson, P.; Dunn, W.B.; Arga, K.Y.; Arvas, M.; Bluthgen, N.; Borger, S.; Costenoble, E.R.; Heinemann, M.; Hucka, M.; Li, P.; Liebermeister, W.; Mo, M.L.; Oliveira, A.P.; Petranovic, D.; Pettifer, S.; Simeonides, E.; Smallbone, K.; Spasi, I.; Weichart, D.; Brent, R.; Broomhead, D.S.; Westerhoff, H.V.; Kirdar, B.; Penttila, M.; Klipp, E.; Paton, N.; Palsson, B.O.; Sauer, U.; Oliver, S.G.; Mendes, P.; Nielsen, J.; Kell, D.B.

    2008-01-01

    Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and

  20. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

    NARCIS (Netherlands)

    Herrgård, Markus J.; Swainston, Neil; Dobson, Paul; Dunn, Warwick B.; Arga, K. Yalçin; Arvas, Mikko; Blüthgen, Nils; Borger, Simon; Costenoble, Roeland; Heinemann, Matthias; Hucka, Michael; Novère, Nicolas Le; Li, Peter; Liebermeister, Wolfram; Mo, Monica L.; Oliveira, Ana Paula; Petranovic, Dina; Pettifer, Stephen; Simeonidis, Evangelos; Smallbone, Kieran; Spasić, Irena; Weichart, Dieter; Brent, Roger; Broomhead, David S.; Westerhoff, Hans V.; Kırdar, Betül; Penttilä, Merja; Klipp, Edda; Palsson, Bernhard Ø.; Sauer, Uwe; Oliver, Stephen G.; Mendes, Pedro; Nielsen, Jens; Kell, Douglas B.

    2008-01-01

    Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and

  1. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

    DEFF Research Database (Denmark)

    Herrgard, Markus; Swainston, Neil; Dobson, Paul

    2008-01-01

    a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously...... of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms....

  2. Controllability in cancer metabolic networks according to drug targets as driver nodes.

    Science.gov (United States)

    Asgari, Yazdan; Salehzadeh-Yazdi, Ali; Schreiber, Falk; Masoudi-Nejad, Ali

    2013-01-01

    Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.

  3. Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets.

    Science.gov (United States)

    Levering, Jennifer; Fiedler, Tomas; Sieg, Antje; van Grinsven, Koen W A; Hering, Silvio; Veith, Nadine; Olivier, Brett G; Klett, Lara; Hugenholtz, Jeroen; Teusink, Bas; Kreikemeyer, Bernd; Kummer, Ursula

    2016-08-20

    Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49. Initially, we based the reconstruction on genome annotations and already existing and curated metabolic networks of Bacillus subtilis, Escherichia coli, Lactobacillus plantarum and Lactococcus lactis. This initial draft was manually curated with the final reconstruction accounting for 480 genes associated with 576 reactions and 558 metabolites. In order to constrain the model further, we performed growth experiments of wild type and arcA deletion strains of S. pyogenes M49 in a chemically defined medium and calculated nutrient uptake and production fluxes. We additionally performed amino acid auxotrophy experiments to test the consistency of the model. The established genome-scale model can be used to understand the growth requirements of the human pathogen S. pyogenes and define optimal and suboptimal conditions, but also to describe differences and similarities between S. pyogenes and related lactic acid bacteria such as L. lactis in order to find strategies to reduce the growth of the pathogen and propose drug targets. Copyright © 2016 Elsevier B.V. All rights reserved.

  4. Homocysteine Facilitates Prominent Polygonal Angiogenetic Networks of a Choroidal Capillary Sprouting Model.

    Science.gov (United States)

    Lee, Yih-Jing; Chiu, Chien-Chao; Ke, Chia-Ying; Tien, Ni; Lin, Po-Kang

    2017-08-01

    To investigate the effects of homocysteine on choroidal angiogenesis, we established an ex vivo choroidal sprouting explant model and examined the potential growth factors for angiogenesis. Choroid fragments with retinal pigment epithelium were isolated from mouse and embedded in Matrigel. Homocysteine at different concentrations were added to the culture mediums. The choroidal explants were observed at different time points, and the total area of choroidal sprouting was measured and analyzed. Homocysteine evoked choroidal capillary sprouting by inducing capillary endothelial cell proliferation with pericyte formation and by facilitating polygonal angiogenetic networks. In some cases, vascular lumens were observed in the newly forming capillaries facilitated by homocysteine. The choroidal sprouting effect of homocysteine can only be observed at a certain range of homocysteine concentration, with 1-mM homocysteine exhibiting the most significantly increased choroidal sprouting areas. Isolectin overexpression was noted in the homocysteine-treated group. Possible growth factors for angiogenesis were detected through immunofluorescent staining, which demonstrated the overexpression of platelet-derived growth factor C and angiopoietin 1 in the homocysteine-treated preparations only. In these preparations, platelet-derived growth factor C was highly expressed in the tip cells of sprouting capillaries. We therefore conclude that platelet-derived growth factor C and angiopoietin 1 may play key roles in the choroid angiogenesis evoked by homocysteine.

  5. Using the reconstructed genome-scale human metabolic network to study physiology and pathology

    OpenAIRE

    Bordbar, Aarash; Palsson, Bernhard O.

    2012-01-01

    Metabolism plays a key role in many major human diseases. Generation of high-throughput omics data has ushered in a new era of systems biology. Genome-scale metabolic network reconstructions provide a platform to interpret omics data in a biochemically meaningful manner. The release of the global human metabolic network, Recon 1, in 2007 has enabled new systems biology approaches to study human physiology, pathology, and pharmacology. There are currently over 20 publications that utilize Reco...

  6. Coordinations between gene modules control the operation of plant amino acid metabolic networks

    Directory of Open Access Journals (Sweden)

    Galili Gad

    2009-01-01

    Full Text Available Abstract Background Being sessile organisms, plants should adjust their metabolism to dynamic changes in their environment. Such adjustments need particular coordination in branched metabolic networks in which a given metabolite can be converted into multiple other metabolites via different enzymatic chains. In the present report, we developed a novel "Gene Coordination" bioinformatics approach and use it to elucidate adjustable transcriptional interactions of two branched amino acid metabolic networks in plants in response to environmental stresses, using publicly available microarray results. Results Using our "Gene Coordination" approach, we have identified in Arabidopsis plants two oppositely regulated groups of "highly coordinated" genes within the branched Asp-family network of Arabidopsis plants, which metabolizes the amino acids Lys, Met, Thr, Ile and Gly, as well as a single group of "highly coordinated" genes within the branched aromatic amino acid metabolic network, which metabolizes the amino acids Trp, Phe and Tyr. These genes possess highly coordinated adjustable negative and positive expression responses to various stress cues, which apparently regulate adjustable metabolic shifts between competing branches of these networks. We also provide evidence implying that these highly coordinated genes are central to impose intra- and inter-network interactions between the Asp-family and aromatic amino acid metabolic networks as well as differential system interactions with other growth promoting and stress-associated genome-wide genes. Conclusion Our novel Gene Coordination elucidates that branched amino acid metabolic networks in plants are regulated by specific groups of highly coordinated genes that possess adjustable intra-network, inter-network and genome-wide transcriptional interactions. We also hypothesize that such transcriptional interactions enable regulatory metabolic adjustments needed for adaptation to the stresses.

  7. N-butyl Cyanoacrylate Glue Embolization of Arterial Networks to Facilitate Hepatic Arterial Skeletonization before Radioembolization

    Energy Technology Data Exchange (ETDEWEB)

    Samuelson, Shaun D.; Louie, John D.; Sze, Daniel Y., E-mail: dansze@stanford.edu [Stanford University School of Medicine, Division of Interventional Radiology (United States)

    2013-06-15

    Purpose. Avoidance of nontarget microsphere deposition via hepatoenteric anastomoses is essential to the safety of yttrium-90 radioembolization (RE). The hepatic hilar arterial network may remain partially patent after coil embolization of major arteries, resulting in persistent risk. We retrospectively reviewed cases where n-butyl cyanoacrylate (n-BCA) glue embolization was used to facilitate endovascular hepatic arterial skeletonization before RE. Methods. A total of 543 RE procedures performed between June 2004 and March 2012 were reviewed, and 10 were identified where n-BCA was used to embolize hepatoenteric anastomoses. Arterial anatomy, prior coil embolization, and technical details were recorded. Outcomes were reviewed to identify subsequent complications of n-BCA embolization or nontarget RE. Results. The rate of complete technical success was 80 % and partial success 20 %, with one nontarget embolization complication resulting in a minor change in treatment plan. No evidence of gastrointestinal or biliary ischemia or infarction was identified, and no microsphere-related gastroduodenal ulcerations or other evidence of nontarget RE were seen. Median volume of n-BCA used was <0.1 ml. Conclusion. n-BCA glue embolization is useful to eliminate hepatoenteric networks that may result in nontarget RE, especially in those that persist after coil embolization of major vessels such as the gastroduodenal and right gastric arteries.

  8. Prolonged rote learning produces delayed memory facilitation and metabolic changes in the hippocampus of the ageing human brain

    Directory of Open Access Journals (Sweden)

    Prendergast Julie

    2009-11-01

    Full Text Available Abstract Background Repeated rehearsal is one method by which verbal material may be transferred from short- to long-term memory. We hypothesised that extended engagement of memory structures through prolonged rehearsal would result in enhanced efficacy of recall and also of brain structures implicated in new learning. Twenty-four normal participants aged 55-70 (mean = 60.1 engaged in six weeks of rote learning, during which they learned 500 words per week every week (prose, poetry etc.. An extensive battery of memory tests was administered on three occasions, each six weeks apart. In addition, proton magnetic resonance spectroscopy (1H-MRS was used to measure metabolite levels in seven voxels of interest (VOIs (including hippocampus before and after learning. Results Results indicate a facilitation of new learning that was evident six weeks after rote learning ceased. This facilitation occurred for verbal/episodic material only, and was mirrored by a metabolic change in left posterior hippocampus, specifically an increase in NAA/(Cr+Cho ratio. Conclusion Results suggest that repeated activation of memory structures facilitates anamnesis and may promote neuronal plasticity in the ageing brain, and that compliance is a key factor in such facilitation as the effect was confined to those who engaged fully with the training.

  9. Prolonged rote learning produces delayed memory facilitation and metabolic changes in the hippocampus of the ageing human brain.

    LENUS (Irish Health Repository)

    Roche, Richard Ap

    2009-01-01

    BACKGROUND: Repeated rehearsal is one method by which verbal material may be transferred from short- to long-term memory. We hypothesised that extended engagement of memory structures through prolonged rehearsal would result in enhanced efficacy of recall and also of brain structures implicated in new learning. Twenty-four normal participants aged 55-70 (mean = 60.1) engaged in six weeks of rote learning, during which they learned 500 words per week every week (prose, poetry etc.). An extensive battery of memory tests was administered on three occasions, each six weeks apart. In addition, proton magnetic resonance spectroscopy (1H-MRS) was used to measure metabolite levels in seven voxels of interest (VOIs) (including hippocampus) before and after learning. RESULTS: Results indicate a facilitation of new learning that was evident six weeks after rote learning ceased. This facilitation occurred for verbal\\/episodic material only, and was mirrored by a metabolic change in left posterior hippocampus, specifically an increase in NAA\\/(Cr+Cho) ratio. CONCLUSION: Results suggest that repeated activation of memory structures facilitates anamnesis and may promote neuronal plasticity in the ageing brain, and that compliance is a key factor in such facilitation as the effect was confined to those who engaged fully with the training.

  10. A cortical attractor network with Martinotti cells driven by facilitating synapses.

    Directory of Open Access Journals (Sweden)

    Pradeep Krishnamurthy

    Full Text Available The population of pyramidal cells significantly outnumbers the inhibitory interneurons in the neocortex, while at the same time the diversity of interneuron types is much more pronounced. One acknowledged key role of inhibition is to control the rate and patterning of pyramidal cell firing via negative feedback, but most likely the diversity of inhibitory pathways is matched by a corresponding diversity of functional roles. An important distinguishing feature of cortical interneurons is the variability of the short-term plasticity properties of synapses received from pyramidal cells. The Martinotti cell type has recently come under scrutiny due to the distinctly facilitating nature of the synapses they receive from pyramidal cells. This distinguishes these neurons from basket cells and other inhibitory interneurons typically targeted by depressing synapses. A key aspect of the work reported here has been to pinpoint the role of this variability. We first set out to reproduce quantitatively based on in vitro data the di-synaptic inhibitory microcircuit connecting two pyramidal cells via one or a few Martinotti cells. In a second step, we embedded this microcircuit in a previously developed attractor memory network model of neocortical layers 2/3. This model network demonstrated that basket cells with their characteristic depressing synapses are the first to discharge when the network enters an attractor state and that Martinotti cells respond with a delay, thereby shifting the excitation-inhibition balance and acting to terminate the attractor state. A parameter sensitivity analysis suggested that Martinotti cells might, in fact, play a dominant role in setting the attractor dwell time and thus cortical speed of processing, with cellular adaptation and synaptic depression having a less prominent role than previously thought.

  11. Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling

    Directory of Open Access Journals (Sweden)

    Sriram Chandrasekaran

    2017-12-01

    Full Text Available Summary: Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a computational model of metabolism to analyze the metabolic state of naive and primed murine pluripotent stem cells. Using this approach, we find that one-carbon metabolism involving phosphoglycerate dehydrogenase, folate synthesis, and nucleotide synthesis is a key pathway that differs between the two states, resulting in differential sensitivity to anti-folates. The model also predicts that the pluripotency factor Lin28 regulates this one-carbon metabolic pathway, which we validate using metabolomics data from Lin28-deficient cells. Moreover, we identify and validate metabolic reactions related to S-adenosyl-methionine production that can differentially impact histone methylation in naive and primed cells. Our network-based approach provides a framework for characterizing metabolic changes influencing pluripotency and cell fate. : Chandrasekaran et al. use computational modeling, metabolomics, and metabolic inhibitors to discover metabolic differences between various pluripotent stem cell states and infer their impact on stem cell fate decisions. Keywords: systems biology, stem cell biology, metabolism, genome-scale modeling, pluripotency, histone methylation, naive (ground state, primed state, cell fate, metabolic network

  12. Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes

    DEFF Research Database (Denmark)

    Zelezniak, Aleksej; Pers, Tune Hannes; Pinho Soares, Simao Pedro

    2010-01-01

    mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets...... with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment...... factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic...

  13. Building a Multi-centre Clinical Research Facilitation Network: The ARC Experience

    Directory of Open Access Journals (Sweden)

    Ian Nicholson

    2017-06-01

    Full Text Available Introduction: In order to practice evidence-based veterinary medicine, good quality clinical evidence needs to be produced, in order that it can be apprasied systematically by the EBVM network, and used by vets. There is very little good-quality veterinary evidence for most of the veterinary procedures carried out every day across the world. Very few, if any, individuals have all the necessary qualities (case-load, time, research expertise, financial support to be able to systematically produce good-quality, and relevant, clinical research on their own, in a timely manner. The Association for Veterinary Soft Tissue Surgery (AVSTS www.avsts.org.uk is an affiliate group with the British Small Animal Veterinary Association (BSAVA, and functions as a clinical network of like-minded individuals. In 2013 AVSTS sought to create a role for itself in facilitating the production (by its members of multi-centre clinical research of relevance to its members.Materials and methods: Members of AVSTS were asked to join the AVSTS Research Cooperative (ARC, with a veterinary epidemiologist and an experienced multi-centre veterinary clinical researcher (to help with study design and statistical planning, and the Animal Health Trust clinical research ethics committee. An email list was established, and a page was set up on the AVSTS website, to allow information to be disseminated. The AVSTS spring and autumn meetings were used as a regular forum by ARC, to discuss its direction, to generate interest, to create and promote specific studies (in order to widen participation amongst different centres, and to update members about previous studies.Results: Membership of ARC has grown to 224 people, although the epidemiologist left. One multi-centre study has been published, two have been presented and await publication, one has been accepted for presentation, two other studies are gathering data at present, and further studies are in the pipeline. There has been

  14. A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks.

    Science.gov (United States)

    Merlet, Benjamin; Paulhe, Nils; Vinson, Florence; Frainay, Clément; Chazalviel, Maxime; Poupin, Nathalie; Gloaguen, Yoann; Giacomoni, Franck; Jourdan, Fabien

    2016-01-01

    This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

  15. Co-regulation of metabolic genes is better explained by flux coupling than by network distance.

    Directory of Open Access Journals (Sweden)

    Richard A Notebaart

    2008-01-01

    Full Text Available To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

  16. Computational solution to automatically map metabolite libraries in the context of genome scale metabolic networks

    Directory of Open Access Journals (Sweden)

    Benjamin eMerlet

    2016-02-01

    Full Text Available This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc and flat file formats (SBML and Matlab files. We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics and Glasgow Polyomics on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks.In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks.In order to achieve this goal we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

  17. Review of metabolic pathways activated in cancer cells as determined through isotopic labeling and network analysis.

    Science.gov (United States)

    Dong, Wentao; Keibler, Mark A; Stephanopoulos, Gregory

    2017-09-01

    Cancer metabolism has emerged as an indispensable part of contemporary cancer research. During the past 10 years, the use of stable isotopic tracers and network analysis have unveiled a number of metabolic pathways activated in cancer cells. Here, we review such pathways along with the particular tracers and labeling observations that led to the discovery of their rewiring in cancer cells. The list of such pathways comprises the reductive metabolism of glutamine, altered glycolysis, serine and glycine metabolism, mutant isocitrate dehydrogenase (IDH) induced reprogramming and the onset of acetate metabolism. Additionally, we demonstrate the critical role of isotopic labeling and network analysis in identifying these pathways. The alterations described in this review do not constitute a complete list, and future research using these powerful tools is likely to discover other cancer-related pathways and new metabolic targets for cancer therapy. Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  18. Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks

    DEFF Research Database (Denmark)

    Brochado, Ana Rita; Andrejev, Sergej; Maranas, Costas D.

    2012-01-01

    Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predic......Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary...

  19. Trade-offs between efficiency and robustness in bacterial metabolic networks are associated with niche breadth.

    Science.gov (United States)

    Morine, Melissa J; Gu, Hong; Myers, Ransom A; Bielawski, Joseph P

    2009-05-01

    The relation between structure and function in biologic networks is a central point of systems biology research. Key functional features--notably, efficiency and robustness--are linked to the topologic structure of a network, and there appears to be a degree of trade-off between these features, i.e., simulation studies indicate that more efficient networks tend to be less robust. Here, we investigate this issue in metabolic networks from 105 lineages of bacteria having a wide range of ecologies. We take quantitative measurements on each network and integrate this network data with ecologic data using a phylogenetic comparative model. In this setting, we find that biologic conclusions obtained with classical phylogenetic comparative methods are sensitive to correlations between model covariates and phylogenetic branch length. To avoid this problem, we propose a revised statistical framework--hierarchical mixed-effect regression--to accommodate phylogenetic nonindependence. Using this approach, we show that the cartography of metabolic networks does indeed reflect a trade-off between efficiency and robustness. Furthermore, ecologic characteristics related to niche breadth are strong predictors of network shape. Given the broad variation in niche breadth seen among species, we predict that there is no universally optimal balance between efficiency and robustness in bacterial metabolic networks and, thus, no universally optimal network structure. These results highlight the biologic relevance of variation in network structure and the potential role of niche breadth in shaping metabolic strategies of efficiency and robustness.

  20. Metabolic networks of Sodalis glossinidius: a systems biology approach to reductive evolution.

    Science.gov (United States)

    Belda, Eugeni; Silva, Francisco J; Peretó, Juli; Moya, Andrés

    2012-01-01

    Genome reduction is a common evolutionary process affecting bacterial lineages that establish symbiotic or pathogenic associations with eukaryotic hosts. Such associations yield highly reduced genomes with greatly streamlined metabolic abilities shaped by the type of ecological association with the host. Sodalis glossinidius, the secondary endosymbiont of tsetse flies, represents one of the few complete genomes available of a bacterium at the initial stages of this process. In the present study, genome reduction is studied from a systems biology perspective through the reconstruction and functional analysis of genome-scale metabolic networks of S. glossinidius. The functional profile of ancestral and extant metabolic networks sheds light on the evolutionary events underlying transition to a host-dependent lifestyle. Meanwhile, reductive evolution simulations on the extant metabolic network can predict possible future evolution of S. glossinidius in the context of genome reduction. Finally, knockout simulations in different metabolic systems reveal a gradual decrease in network robustness to different mutational events for bacterial endosymbionts at different stages of the symbiotic association. Stoichiometric analysis reveals few gene inactivation events whose effects on the functionality of S. glossinidius metabolic systems are drastic enough to account for the ecological transition from a free-living to host-dependent lifestyle. The decrease in network robustness across different metabolic systems may be associated with the progressive integration in the more stable environment provided by the insect host. Finally, reductive evolution simulations reveal the strong influence that external conditions exert on the evolvability of metabolic systems.

  1. Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites.

    Science.gov (United States)

    Hadadi, Noushin; Hafner, Jasmin; Soh, Keng Cher; Hatzimanikatis, Vassily

    2017-01-01

    Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite-level studies to atom-level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom-mapped reactions to atom-mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom-level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom-mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom-mapped reactions of the KEGG database and we provide an example of an atom-level representation of the core metabolic network of E. coli. Copyright © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Implementation of Integrated Service Networks under the Quebec Mental Health Reform: Facilitators and Barriers associated with Different Territorial Profiles

    Directory of Open Access Journals (Sweden)

    Marie-Josée Fleury

    2017-03-01

    Full Text Available Introduction: This study evaluates implementation of the Quebec Mental Health Reform (2005–2015, which promoted the development of integrated service networks, in 11 local service networks organized into four territorial groups according to socio-demographic characteristics and mental health services offered. Methods: Data were collected from documents concerning networks; structured questionnaires completed by 90 managers and by 16 respondent-psychiatrists; and semi-structured interviews with 102 network stakeholders. Factors associated with implementation and integration were organized according to: 1 reform characteristics; 2 implementation context; 3 organizational characteristics; and 4 integration strategies. Results: While local networks were in a process of development and expansion, none were fully integrated at the time of the study. Facilitators and barriers to implementation and integration were primarily associated with organizational characteristics. Integration was best achieved in larger networks including a general hospital with a psychiatric department, followed by networks with a psychiatric hospital. Formalized integration strategies such as service agreements, liaison officers, and joint training reduced some barriers to implementation in networks experiencing less favourable conditions. Conclusion: Strategies for the implementation of healthcare reform and integrated service networks should include sustained support and training in best-practices, adequate performance indicators and resources, formalized integration strategies to improve network coordination and suitable initiatives to promote staff retention.

  3. Metabolic Network Discovery by Top-Down and Bottom-Up Approaches and Paths for Reconciliation

    International Nuclear Information System (INIS)

    Çakır, Tunahan; Khatibipour, Mohammad Jafar

    2014-01-01

    The primary focus in the network-centric analysis of cellular metabolism by systems biology approaches is to identify the active metabolic network for the condition of interest. Two major approaches are available for the discovery of the condition-specific metabolic networks. One approach starts from genome-scale metabolic networks, which cover all possible reactions known to occur in the related organism in a condition-independent manner, and applies methods such as the optimization-based Flux-Balance Analysis to elucidate the active network. The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract information content of the data such that the active network is inferred. These approaches, termed bottom-up and top-down, respectively, are currently employed independently. However, considering that both approaches have the same goal, they can both benefit from each other paving the way for the novel integrative analysis methods of metabolome data- and flux-analysis approaches in the post-genomic era. This study reviews the strengths of constraint-based analysis and network inference methods reported in the metabolic systems biology field; then elaborates on the potential paths to reconcile the two approaches to shed better light on how the metabolism functions.

  4. Metabolic network reconstruction, growth characterization and 13C-metabolic flux analysis of the extremophile Thermus thermophilus HB8.

    Science.gov (United States)

    Swarup, Aditi; Lu, Jing; DeWoody, Kathleen C; Antoniewicz, Maciek R

    2014-07-01

    Thermus thermophilus is an extremely thermophilic bacterium with significant biotechnological potential. In this work, we have characterized aerobic growth characteristics of T. thermophilus HB8 at temperatures between 50 and 85°C, constructed a metabolic network model of its central carbon metabolism and validated the model using (13)C-metabolic flux analysis ((13)C-MFA). First, cells were grown in batch cultures in custom constructed mini-bioreactors at different temperatures to determine optimal growth conditions. The optimal temperature for T. thermophilus grown on defined medium with glucose was 81°C. The maximum growth rate was 0.25h(-1). Between 50 and 81°C the growth rate increased by 7-fold and the temperature dependence was described well by an Arrhenius model with an activation energy of 47kJ/mol. Next, we performed a (13)C-labeling experiment with [1,2-(13)C] glucose as the tracer and calculated intracellular metabolic fluxes using (13)C-MFA. The results provided support for the constructed network model and highlighted several interesting characteristics of T. thermophilus metabolism. We found that T. thermophilus largely uses glycolysis and TCA cycle to produce biosynthetic precursors, ATP and reducing equivalents needed for cells growth. Consistent with its proposed metabolic network model, we did not detect any oxidative pentose phosphate pathway flux or Entner-Doudoroff pathway activity. The biomass precursors erythrose-4-phosphate and ribose-5-phosphate were produced via the non-oxidative pentose phosphate pathway, and largely via transketolase, with little contribution from transaldolase. The high biomass yield on glucose that was measured experimentally was also confirmed independently by (13)C-MFA. The results presented here provide a solid foundation for future studies of T. thermophilus and its metabolic engineering applications. Copyright © 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  5. Light-emitting conjugated polymers with microporous network architecture: interweaving scaffold promotes electronic conjugation, facilitates exciton migration, and improves luminescence.

    Science.gov (United States)

    Xu, Yanhong; Chen, Long; Guo, Zhaoqi; Nagai, Atsushi; Jiang, Donglin

    2011-11-09

    Herein we report a strategy for the design of highly luminescent conjugated polymers by restricting rotation of the polymer building blocks through a microporous network architecture. We demonstrate this concept using tetraphenylethene (TPE) as a building block to construct a light-emitting conjugated microporous polymer. The interlocked network successfully restricted the rotation of the phenyl units, which are the major cause of fluorescence deactivation in TPE, thus providing intrinsic luminescence activity for the polymers. We show positive "CMP effects" that the network promotes π-conjugation, facilitates exciton migration, and improves luminescence activity. Although the monomer and linear polymer analogue in solvents are nonemissive, the network polymers are highly luminescent in various solvents and the solid state. Because emission losses due to rotation are ubiquitous among small chromophores, this strategy can be generalized for the de novo design of light-emitting materials by integrating the chromophores into an interlocked network architecture.

  6. Metabolic syndrome in healthy ponies facilitates nutritional countermeasures against pasture laminitis.

    Science.gov (United States)

    Kronfeld, David S; Treiber, Kibby H; Hess, Tanja M; Splan, Rebecca K; Byrd, Bridgett M; Staniar, W Burton; White, Nathanial W

    2006-07-01

    Treatment of clinical laminitis usually fails to prevent some degree of persistent disability; thus, intervention should aim at avoiding risk factors and preventing the disease. Efficiency of intervention would be improved by identifying predisposed horses and ponies. A herd of 160 healthy ponies included 54 previously laminitic (PL) and 106 never laminitic (NL). Pedigree analysis was consistent with dominant inheritance partially suppressed in males. Blood analysis revealed higher plasma concentrations of insulin and triglycerides but not cortisol, glucose, or free fatty acids in the PL group. Proxies for insulin sensitivity and beta-cell responsiveness, which were calculated from plasma insulin and glucose, indicated compensated insulin resistance in the PL group. A prelaminitic metabolic syndrome (PLMS) was derived statistically to have cut-off points for the 2 proxies, hypertriglyceridemia, and body condition score. It had a total predictive power of 78%. It identified 62 ponies with PLMS, and 98 as PLMS-free. Two months later, pasture starch concentration doubled, and 13 clinical cases of laminitis developed, 11 in the PLMS group and 2 in the PLMS-free group, giving an odds ratio of 10.4 (P = 0.0006). The PLMS can be used to identify predisposed ponies in need of special care; the efficiency of intervention would increase nearly 3-fold in the present case. It enables the design of new interventions suitable for testing. The PLMS also might influence market values.

  7. Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions

    Directory of Open Access Journals (Sweden)

    Orth Jeffrey D

    2012-05-01

    Full Text Available Abstract Background The iJO1366 reconstruction of the metabolic network of Escherichia coli is one of the most complete and accurate metabolic reconstructions available for any organism. Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network. Results A new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions. Conclusions Numerous improvements to the iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking.

  8. A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism.

    Science.gov (United States)

    Yuan, Huili; Cheung, C Y Maurice; Poolman, Mark G; Hilbers, Peter A J; van Riel, Natal A W

    2016-01-01

    Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses. © 2015 The Authors.The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

  9. Bringing metabolic networks to life: convenience rate law and thermodynamic constraints

    Directory of Open Access Journals (Sweden)

    Klipp Edda

    2006-12-01

    Full Text Available Abstract Background Translating a known metabolic network into a dynamic model requires rate laws for all chemical reactions. The mathematical expressions depend on the underlying enzymatic mechanism; they can become quite involved and may contain a large number of parameters. Rate laws and enzyme parameters are still unknown for most enzymes. Results We introduce a simple and general rate law called "convenience kinetics". It can be derived from a simple random-order enzyme mechanism. Thermodynamic laws can impose dependencies on the kinetic parameters. Hence, to facilitate model fitting and parameter optimisation for large networks, we introduce thermodynamically independent system parameters: their values can be varied independently, without violating thermodynamical constraints. We achieve this by expressing the equilibrium constants either by Gibbs free energies of formation or by a set of independent equilibrium constants. The remaining system parameters are mean turnover rates, generalised Michaelis-Menten constants, and constants for inhibition and activation. All parameters correspond to molecular energies, for instance, binding energies between reactants and enzyme. Conclusion Convenience kinetics can be used to translate a biochemical network – manually or automatically - into a dynamical model with plausible biological properties. It implements enzyme saturation and regulation by activators and inhibitors, covers all possible reaction stoichiometries, and can be specified by a small number of parameters. Its mathematical form makes it especially suitable for parameter estimation and optimisation. Parameter estimates can be easily computed from a least-squares fit to Michaelis-Menten values, turnover rates, equilibrium constants, and other quantities that are routinely measured in enzyme assays and stored in kinetic databases.

  10. Improving the description of metabolic networks: the TCA cycle as example

    NARCIS (Netherlands)

    Stobbe, Miranda D.; Houten, Sander M.; van Kampen, Antoine H. C.; Wanders, Ronald J. A.; Moerland, Perry D.

    2012-01-01

    To collect the ever-increasing yet scattered knowledge on metabolism, multiple pathway databases like the Kyoto Encyclopedia of Genes and Genomes have been created. A complete and accurate description of the metabolic network for human and other organisms is essential to foster new biological

  11. Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks

    Science.gov (United States)

    Carr, Michelle; Nielsen, Tore

    2015-01-01

    Study Objectives: The goals of the study were to assess semantic priming to emotion and nonemotion cue words using a novel measure of associational breadth for participants who either took rapid eye movement (REM) or nonrapid eye movement (NREM) naps or who remained awake, and to assess the relation of priming to REM sleep consolidation and REM sleep inertia effects. Design: The associational breadth task was applied in both a priming condition, where cue words were signaled to be memorized prior to sleep (primed), and a nonpriming condition, where cue words were not memorized (nonprimed). Cue words were either emotional (positive, negative) or nonemotional. Participants were randomly assigned to either an awake (WAKE) or a sleep condition, which was subsequently split into NREM or REM groups depending on stage at awakening. Setting: Hospital-based sleep laboratory. Participants: Fifty-eight healthy participants (22 male) ages 18 to 35 y (mean age = 23.3 ± 4.08 y). Measurements and Results: The REM group scored higher than the NREM or WAKE groups on primed, but not nonprimed emotional cue words; the effect was stronger for positive than for negative cue words. However, REM time and percent correlated negatively with degree of emotional priming. Priming occurred for REM awakenings but not for NREM awakenings, even when the latter sleep episodes contained some REM sleep. Conclusions: Associational breadth may be selectively consolidated during REM sleep for stimuli that have been tagged as important for future memory retrieval. That priming decreased with REM time and was higher only for REM sleep awakenings is consistent with two explanatory REM sleep processes: REM sleep consolidation serving emotional downregulation and REM sleep inertia. Citation: Carr M, Nielsen T. Morning REM sleep naps facilitate broad access to emotional semantic networks. SLEEP 2015;38(3):433–443. PMID:25409100

  12. Metabolic network modeling approaches for investigating the "hungry cancer".

    Science.gov (United States)

    Sharma, Ashwini Kumar; König, Rainer

    2013-08-01

    Metabolism is the functional phenotype of a cell, at a given condition, resulting from an intricate interplay of various regulatory processes. The study of these dynamic metabolic processes and their capabilities help to identify the fundamental properties of living systems. Metabolic deregulation is an emerging hallmark of cancer cells. This deregulation results in rewiring of the metabolic circuitry conferring an exploitative metabolic advantage for the tumor cells which leads to a distinct benefit in survival and lays the basis for unbound progression. Metabolism can be considered as a thermodynamic open-system in which source substrates of high value are being processed through a well established interconnected biochemical conversion system, strictly obeying physiochemical principles, generating useful intermediates and finally resulting in the release of byproducts. Based on this basic principle of an input-output balance, various models have been developed to interrogate metabolism elucidating its underlying functional properties. However, only a few modeling approaches have proved computationally feasible in elucidating the metabolic nature of cancer at a systems level. Besides this, statistical approaches have been set up to identify biochemical pathways being more relevant for specific types of tumor cells. In this review, we are briefly introducing the basic statistical approaches followed by the major modeling concepts. We have put an emphasis on the methods and their applications that have been used to a greater extent in understanding the metabolic remodeling of cancer. Copyright © 2013 Elsevier Ltd. All rights reserved.

  13. Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network.

    Science.gov (United States)

    Galhardo, Mafalda; Sinkkonen, Lasse; Berninger, Philipp; Lin, Jake; Sauter, Thomas; Heinäniemi, Merja

    2014-02-01

    Metabolic diseases and comorbidities represent an ever-growing epidemic where multiple cell types impact tissue homeostasis. Here, the link between the metabolic and gene regulatory networks was studied through experimental and computational analysis. Integrating gene regulation data with a human metabolic network prompted the establishment of an open-sourced web portal, IDARE (Integrated Data Nodes of Regulation), for visualizing various gene-related data in context of metabolic pathways. Motivated by increasing availability of deep sequencing studies, we obtained ChIP-seq data from widely studied human umbilical vein endothelial cells. Interestingly, we found that association of metabolic genes with multiple transcription factors (TFs) enriched disease-associated genes. To demonstrate further extensions enabled by examining these networks together, constraint-based modeling was applied to data from human preadipocyte differentiation. In parallel, data on gene expression, genome-wide ChIP-seq profiles for peroxisome proliferator-activated receptor (PPAR) γ, CCAAT/enhancer binding protein (CEBP) α, liver X receptor (LXR) and H3K4me3 and microRNA target identification for miR-27a, miR-29a and miR-222 were collected. Disease-relevant key nodes, including mitochondrial glycerol-3-phosphate acyltransferase (GPAM), were exposed from metabolic pathways predicted to change activity by focusing on association with multiple regulators. In both cell types, our analysis reveals the convergence of microRNAs and TFs within the branched chain amino acid (BCAA) metabolic pathway, possibly providing an explanation for its downregulation in obese and diabetic conditions.

  14. Metabolic pathway of non-alcoholic fatty liver disease: Network properties and robustness

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2017-03-01

    Full Text Available Nonalcoholic fatty liver disease (NAFLD is a systematic and complex disease involving various cytokines/metabolites. In present article, we use methodology of network biology to analyze network properties of NAFLD metabolic pathway. It is found that the metabolic pathway of NAFLD is not a typical complex network with power-law degree distribution, p(x=x^(-4.4275, x>=5. There is only one connected component in the metabolic pathway. The calculated cut cytokines/metabolites of the metabolic pathway are SREBP-1c, ChREBP, ObR, AMPK, IRE1alpha, ROS, PERK, elF2alpha, ATF4, CHOP, Bim, CASP8, Bid, CxII, Lipogenic enzymes, XBP1, and FFAs. The most important cytokine/metabolite for possible network robustness is FFAs, seconded by TNF-alpha. It is concluded that FFAs is the most important cytokine/metabolite in the metabolic pathway, seconded by ROS. FFAs, LEP, ACDC, CYP2E1, and Glucose are the only cytokines/metabolites that affect others without influences from other cytokines/metabolites. Finally, the IDs matrix for identifying possible sub-networks/modules is given. However, jointly combining the results of connectedness analysis and sub-networks/modules identification, we hold that there are not significant sub-networks/modules in the pathway.

  15. Facilitating Phenological Assessments at Local, Regional and National Scales: Year Two Progress of the USA National Phenology Network

    Science.gov (United States)

    Weltzin, J. F.

    2009-12-01

    Patterns of phenology for plants and animals control ecosystem processes, determine land surface properties, control biosphere-atmosphere interactions, and affect food production, health, conservation, and recreation. Although directional climate change has already caused documented shifts in organismal, population, community and ecosystem-level patterns and processes, a national phenological assessment requires a comprehensive suite of standardized methodologies to track phenology across a range of spatial and temporal scales (e.g., organismal to landscapes). The USA National Phenology Network (USA-NPN; www.usanpn.org) is an emerging and exciting partnership between federal agencies, the academic community, and the general public to establish a national science and monitoring initiative focused on phenology as a tool to understand how plants, animals and landscapes respond to climate variation, and as a tool to facilitate human adaptation to ongoing and potential future climate change. USA-NPN will (1) integrate with other formal and informal science observation networks (e.g., NEON, LTER, Ameriflux, NPS I & M, OBFS, GEO, public gardens, conservation groups) including regional phenology networks; (2) utilize and enhance remote sensing products, emerging technologies and data management capabilities; and (3) capitalize on myriad educational opportunities and a new readiness of the public to participate in investigations of nature on a national scale. In its second year of operation, USA-NPN produced many new phenology products and venues for phenology research and citizen involvement that will facilitate local, regional or national assessments of phenology. A new web-page contains an advanced on-line user interface to facilitate entry of contemporary data into the National Phenology Database. The new plant phenology monitoring program provides standardized methodologies and monitoring protocols for 215 local, regional, and nationally distributed plant species

  16. Integrating data from biological experiments into metabolic networks with the DBE information system.

    Science.gov (United States)

    Borisjuk, Ljudmilla; Hajirezaei, Mohammad-Reza; Klukas, Christian; Rolletschek, Hardy; Schreiber, Falk

    2005-01-01

    Modern 'omics'-technologies result in huge amounts of data about life processes. For analysis and data mining purposes this data has to be considered in the context of the underlying biological networks. This work presents an approach for integrating data from biological experiments into metabolic networks by mapping the data onto network elements and visualising the data enriched networks automatically. This methodology is implemented in DBE, an information system that supports the analysis and visualisation of experimental data in the context of metabolic networks. It consists of five parts: (1) the DBE-Database for consistent data storage, (2) the Excel-Importer application for the data import, (3) the DBE-Website as the interface for the system, (4) the DBE-Pictures application for the up- and download of binary (e. g. image) files, and (5) DBE-Gravisto, a network analysis and graph visualisation system. The usability of this approach is demonstrated in two examples.

  17. Building a Foundation for Knowledge Co-Creation in Collaborative Water Governance: Dimensions of Stakeholder Networks Facilitated through Bridging Organizations

    Directory of Open Access Journals (Sweden)

    Wietske Medema

    2017-01-01

    Full Text Available The sustainable governance of water resources relies on processes of multi-stakeholder collaborations and interactions that facilitate the sharing and integration of diverse sources and types of knowledge. In this context, it is essential to fully recognize the importance of fostering and enhancing critical connections within and between networks of relationships between different government and non-government agencies, as well as the dynamics that are in support of the development of new knowledge and practices. In Quebec, a network of watershed organizations (WOs has been put in place to serve as bridging organizations (BOs for stakeholder groups in their watershed territories. Using the WOs as a case study, this research aims to contribute to a greater understanding of how stakeholder groups can be effectively connected to support knowledge co-creation through networked relationships facilitated by BOs. In line with this overall research aim, the following research objectives are proposed: (1 to assess the quality of the knowledge that is developed and shared through the WOs and their stakeholder networks; (2 to determine the characteristics of stakeholders participating in the WOs’ networks that either hinder or support collaborations and knowledge co-creation; (3 to describe the collaborative processes and mechanisms through which the WOs facilitate stakeholder interactions and knowledge co-creation; and (4 to assess the quality of the relationships and interactions between stakeholders participating in the WOs’ collaborative networks. A comprehensive literature review is provided of collaborative network dimensions that are in support of knowledge co-creation that forms the foundation of a research framework to assess knowledge co-creation processes that are facilitated through BOs and their collaborative networks. Documented experiences have been gathered through face-to-face semi-structured interviews, as well as a Quebec-wide survey

  18. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology.

    Science.gov (United States)

    Herrgård, Markus J; Swainston, Neil; Dobson, Paul; Dunn, Warwick B; Arga, K Yalçin; Arvas, Mikko; Blüthgen, Nils; Borger, Simon; Costenoble, Roeland; Heinemann, Matthias; Hucka, Michael; Le Novère, Nicolas; Li, Peter; Liebermeister, Wolfram; Mo, Monica L; Oliveira, Ana Paula; Petranovic, Dina; Pettifer, Stephen; Simeonidis, Evangelos; Smallbone, Kieran; Spasić, Irena; Weichart, Dieter; Brent, Roger; Broomhead, David S; Westerhoff, Hans V; Kirdar, Betül; Penttilä, Merja; Klipp, Edda; Palsson, Bernhard Ø; Sauer, Uwe; Oliver, Stephen G; Mendes, Pedro; Nielsen, Jens; Kell, Douglas B

    2008-10-01

    Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.

  19. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

    Science.gov (United States)

    Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

    2017-05-01

    Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

  20. Bacterial metabolism in immediate response to nutritional perturbation with temporal and network view of metabolites.

    Science.gov (United States)

    Yukihira, Daichi; Fujimura, Yoshinori; Wariishi, Hiroyuki; Miura, Daisuke

    2015-09-01

    In this study, the initial propagation of metabolic perturbation in Escherichia coli was visualized to understand the dynamic characteristics of the metabolic pathways without the association of transcription alterations. E. coli cells were exposed to the sudden relief of glucose starvation, and time-dependent variances in metabolite balances were traced in the second scale. The acquired time-course data were represented by structural variations of the metabolite-metabolite correlation network. The initial correlation structure was altered immediately by the glucose pulse, followed by further structural variations within a few minutes. It was demonstrated that one metabolite temporally correlated with distinct metabolites with different timings, and such a behavior could imply a regulatory role for the metabolite in the metabolic network. Centrality analysis of the networks and partial correlation analysis indicated that preparation for growth and oxidative stress could be coupled as a structural property of the metabolic pathways.

  1. Characterization of the Usage of the Serine Metabolic Network in Human Cancer

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    Mahya Mehrmohamadi

    2014-11-01

    Full Text Available The serine, glycine, one-carbon (SGOC metabolic network is implicated in cancer pathogenesis, but its general functions are unknown. We carried out a computational reconstruction of the SGOC network and then characterized its expression across thousands of cancer tissues. Pathways including methylation and redox metabolism exhibited heterogeneous expression indicating a strong context dependency of their usage in tumors. From an analysis of coexpression, simultaneous up- or downregulation of nucleotide synthesis, NADPH, and glutathione synthesis was found to be a common occurrence in all cancers. Finally, we developed a method to trace the metabolic fate of serine using stable isotopes, high-resolution mass spectrometry, and a mathematical model. Although the expression of single genes didn’t appear indicative of flux, the collective expression of several genes in a given pathway allowed for successful flux prediction. Altogether, these findings identify expansive and heterogeneous functions for the SGOC metabolic network in human cancer.

  2. Construction and analysis of a genome-scale metabolic network for Bacillus licheniformis WX-02.

    Science.gov (United States)

    Guo, Jing; Zhang, Hong; Wang, Cheng; Chang, Ji-Wei; Chen, Ling-Ling

    2016-05-01

    We constructed the genome-scale metabolic network of Bacillus licheniformis (B. licheniformis) WX-02 by combining genomic annotation, high-throughput phenotype microarray (PM) experiments and literature-based metabolic information. The accuracy of the metabolic network was assessed by an OmniLog PM experiment. The final metabolic model iWX1009 contains 1009 genes, 1141 metabolites and 1762 reactions, and the predicted metabolic phenotypes showed an agreement rate of 76.8% with experimental PM data. In addition, key metabolic features such as growth yield, utilization of different substrates and essential genes were identified by flux balance analysis. A total of 195 essential genes were predicted from LB medium, among which 149 were verified with the experimental essential gene set of B. subtilis 168. With the removal of 5 reactions from the network, pathways for poly-γ-glutamic acid (γ-PGA) synthesis were optimized and the γ-PGA yield reached 83.8 mmol/h. Furthermore, the important metabolites and pathways related to γ-PGA synthesis and bacterium growth were comprehensively analyzed. The present study provides valuable clues for exploring the metabolisms and metabolic regulation of γ-PGA synthesis in B. licheniformis WX-02. Copyright © 2016 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

  3. An online practice and educational networking system for technical skills: learning experience in expert facilitated vs. independent learning communities.

    Science.gov (United States)

    Rojas, David; Cheung, Jeffrey J H; Weber, Bryce; Kapralos, Bill; Carnahan, Heather; Bägli, Darius J; Dubrowski, Adam

    2012-01-01

    This study explored the activities of trainees learning technical skills using an educational networking tool with and without expert facilitation. Medical students (participants) were video-recorded practicing suturing and knot tying techniques and the resulting videos were uploaded to an educational networking site. Participants were then divided into two groups (one group containing an expert facilitator while the other group did not) and encouraged to comment on the videos within their group. We monitored the number of logins and comments posted and all participants completed an exit survey. There were no differences between the activities the two groups (p = 0.387). We conclude that the presence of an expert within collaborative Internet environments in not necessary to promote interactivity amongst the learners.

  4. Ultraperformance liquid chromatography-mass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets.

    Science.gov (United States)

    Zhang, Ai-hua; Sun, Hui; Han, Ying; Yan, Guang-li; Yuan, Ye; Song, Gao-chen; Yuan, Xiao-xia; Xie, Ning; Wang, Xi-jun

    2013-08-06

    Metabolomics is the study of metabolic changes in biological systems and provides the small molecule fingerprints related to the disease. Extracting biomedical information from large metabolomics data sets by multivariate data analysis is of considerable complexity. Therefore, more efficient and optimizing metabolomics data processing technologies are needed to improve mass spectrometry applications in biomarker discovery. Here, we report the findings of urine metabolomic investigation of hepatitis C virus (HCV) patients by high-throughput ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) coupled with pattern recognition methods (principal component analysis, partial least-squares, and OPLS-DA) and network pharmacology. A total of 20 urinary differential metabolites (13 upregulated and 7 downregulated) were identified and contributed to HCV progress, involve several key metabolic pathways such as taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, histidine metabolism, arginine and proline metabolism, and so forth. Metabolites identified through metabolic profiling may facilitate the development of more accurate marker algorithms to better monitor disease progression. Network analysis validated close contact between these metabolites and implied the importance of the metabolic pathways. Mapping altered metabolites to KEGG pathways identified alterations in a variety of biological processes mediated through complex networks. These findings may be promising to yield a valuable and noninvasive tool that insights into the pathophysiology of HCV and to advance the early diagnosis and monitor the progression of disease. Overall, this investigation illustrates the power of the UPLC-MS platform combined with the pattern recognition and network analysis methods that can engender new insights into HCV pathobiology.

  5. A compendium of inborn errors of metabolism mapped onto the human metabolic network.

    OpenAIRE

    Sahoo, Swagatika; Franzson, Leifur; Jonsson, Jon J; Thiele, Ines

    2012-01-01

    Efst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn Inborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitin...

  6. A general model for metabolic scaling in self-similar asymmetric networks.

    Directory of Open Access Journals (Sweden)

    Alexander Byers Brummer

    2017-03-01

    Full Text Available How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE model argues that these two principles (space-filling and energy minimization are (i general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber's Law can still be attained within many asymmetric networks.

  7. Using a genome-scale metabolic network model to elucidate the mechanism of chloroquine action in Plasmodium falciparum

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    Shivendra G. Tewari

    2017-08-01

    Full Text Available Chloroquine, long the default first-line treatment against malaria, is now abandoned in large parts of the world because of widespread drug-resistance in Plasmodium falciparum. In spite of its importance as a cost-effective and efficient drug, a coherent understanding of the cellular mechanisms affected by chloroquine and how they influence the fitness and survival of the parasite remains elusive. Here, we used a systems biology approach to integrate genome-scale transcriptomics to map out the effects of chloroquine, identify targeted metabolic pathways, and translate these findings into mechanistic insights. Specifically, we first developed a method that integrates transcriptomic and metabolomic data, which we independently validated against a recently published set of such data for Krebs-cycle mutants of P. falciparum. We then used the method to calculate the effect of chloroquine treatment on the metabolic flux profiles of P. falciparum during the intraerythrocytic developmental cycle. The model predicted dose-dependent inhibition of DNA replication, in agreement with earlier experimental results for both drug-sensitive and drug-resistant P. falciparum strains. Our simulations also corroborated experimental findings that suggest differences in chloroquine sensitivity between ring- and schizont-stage P. falciparum. Our analysis also suggests that metabolic fluxes that govern reduced thioredoxin and phosphoenolpyruvate synthesis are significantly decreased and are pivotal to chloroquine-based inhibition of P. falciparum DNA replication. The consequences of impaired phosphoenolpyruvate synthesis and redox metabolism are reduced carbon fixation and increased oxidative stress, respectively, both of which eventually facilitate killing of the parasite. Our analysis suggests that a combination of chloroquine (or an analogue and another drug, which inhibits carbon fixation and/or increases oxidative stress, should increase the clearance of P

  8. Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks

    Directory of Open Access Journals (Sweden)

    Chang Jeong-Ho

    2006-06-01

    Full Text Available Abstract Background To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. Results To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. Conclusion By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway

  9. Dynamic brain glucose metabolism identifies anti-correlated cortical-cerebellar networks at rest.

    Science.gov (United States)

    Tomasi, Dardo G; Shokri-Kojori, Ehsan; Wiers, Corinde E; Kim, Sunny W; Demiral, Şukru B; Cabrera, Elizabeth A; Lindgren, Elsa; Miller, Gregg; Wang, Gene-Jack; Volkow, Nora D

    2017-12-01

    It remains unclear whether resting state functional magnetic resonance imaging (rfMRI) networks are associated with underlying synchrony in energy demand, as measured by dynamic 2-deoxy-2-[ 18 F]fluoroglucose (FDG) positron emission tomography (PET). We measured absolute glucose metabolism, temporal metabolic connectivity (t-MC) and rfMRI patterns in 53 healthy participants at rest. Twenty-two rfMRI networks emerged from group independent component analysis (gICA). In contrast, only two anti-correlated t-MC emerged from FDG-PET time series using gICA or seed-voxel correlations; one included frontal, parietal and temporal cortices, the other included the cerebellum and medial temporal regions. Whereas cerebellum, thalamus, globus pallidus and calcarine cortex arose as the strongest t-MC hubs, the precuneus and visual cortex arose as the strongest rfMRI hubs. The strength of the t-MC linearly increased with the metabolic rate of glucose suggesting that t-MC measures are strongly associated with the energy demand of the brain tissue, and could reflect regional differences in glucose metabolism, counterbalanced metabolic network demand, and/or differential time-varying delivery of FDG. The mismatch between metabolic and functional connectivity patterns computed as a function of time could reflect differences in the temporal characteristics of glucose metabolism as measured with PET-FDG and brain activation as measured with rfMRI.

  10. A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information

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    Thomas eNägele

    2016-03-01

    Full Text Available The functional connection of experimental metabolic time series data with biochemical network information is an important, yet complex, issue in systems biology. Frequently, experimental analysis of diurnal, circadian or developmental dynamics of metabolism results in a comprehensive and multidimensional data matrix comprising information about metabolite concentrations, protein levels and/or enzyme activities. While, irrespective of the type of organism, the experimental high-throughput analysis of the transcriptome, proteome and metabolome has become a common part of many systems biology studies, functional data integration in a biochemical and physiological context is still challenging. Here, an approach is presented which addresses the functional connection of experimental time series data with biochemical network information which can be inferred, for example, from a metabolic network reconstruction. Based on a time-continuous and variance-weighted regression analysis of experimental data, metabolic functions, i.e. first-order derivatives of metabolite concentrations, were related to time-dependent changes in other biochemically relevant metabolic functions, i.e. second-order derivatives of metabolite concentrations. This finally revealed time points of perturbed dependencies in metabolic functions indicating a modified biochemical interaction. The approach was validated using previously published experimental data on a diurnal time course of metabolite levels, enzyme activities and metabolic flux simulations. To support and ease the presented approach of functional time series analysis, a graphical user interface including a test data set and a manual is provided which can be run within the numerical software environment Matlab®.

  11. A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

    Science.gov (United States)

    Röhl, Annika; Bockmayr, Alexander

    2017-01-03

    Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

  12. Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency

    Science.gov (United States)

    Weng, Rui; Shen, Sensen; Tian, Yonglu; Burton, Casey; Xu, Xinyuan; Liu, Yi; Chang, Cuilan; Bai, Yu; Liu, Huwei

    2015-07-01

    Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer’s disease and Parkinson’s disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states.

  13. Comparative analysis of Salmonella genomes identifies a metabolic network for escalating growth in the inflamed gut.

    Science.gov (United States)

    Nuccio, Sean-Paul; Bäumler, Andreas J

    2014-03-18

    The Salmonella genus comprises a group of pathogens associated with illnesses ranging from gastroenteritis to typhoid fever. We performed an in silico analysis of comparatively reannotated Salmonella genomes to identify genomic signatures indicative of disease potential. By removing numerous annotation inconsistencies and inaccuracies, the process of reannotation identified a network of 469 genes involved in central anaerobic metabolism, which was intact in genomes of gastrointestinal pathogens but degrading in genomes of extraintestinal pathogens. This large network contained pathways that enable gastrointestinal pathogens to utilize inflammation-derived nutrients as well as many of the biochemical reactions used for the enrichment and biochemical discrimination of Salmonella serovars. Thus, comparative genome analysis identifies a metabolic network that provides clues about the strategies for nutrient acquisition and utilization that are characteristic of gastrointestinal pathogens. IMPORTANCE While some Salmonella serovars cause infections that remain localized to the gut, others disseminate throughout the body. Here, we compared Salmonella genomes to identify characteristics that distinguish gastrointestinal from extraintestinal pathogens. We identified a large metabolic network that is functional in gastrointestinal pathogens but decaying in extraintestinal pathogens. While taxonomists have used traits from this network empirically for many decades for the enrichment and biochemical discrimination of Salmonella serovars, our findings suggest that it is part of a "business plan" for growth in the inflamed gastrointestinal tract. By identifying a large metabolic network characteristic of Salmonella serovars associated with gastroenteritis, our in silico analysis provides a blueprint for potential strategies to utilize inflammation-derived nutrients and edge out competing gut microbes.

  14. Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data

    Directory of Open Access Journals (Sweden)

    Ai-Di Zhang

    2013-01-01

    Full Text Available With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases.

  15. TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Jensen Paul A

    2011-09-01

    Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

  16. Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information.

    Directory of Open Access Journals (Sweden)

    Xinyan Wang

    Full Text Available Chronic obstructive pulmonary disease (COPD is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.

  17. Understanding the control of acyl flux through the lipid metabolic network of plant oil biosynthesis.

    Science.gov (United States)

    Bates, Philip D

    2016-09-01

    Plant oil biosynthesis involves a complex metabolic network with multiple subcellular compartments, parallel pathways, cycles, and pathways that have a dual function to produce essential membrane lipids and triacylglycerol. Modern molecular biology techniques provide tools to alter plant oil compositions through bioengineering, however with few exceptions the final composition of triacylglycerol cannot be predicted. One reason for limited success in oilseed bioengineering is the inadequate understanding of how to control the flux of fatty acids through various fatty acid modification, and triacylglycerol assembly pathways of the lipid metabolic network. This review focuses on the mechanisms of acyl flux through the lipid metabolic network, and highlights where uncertainty resides in our understanding of seed oil biosynthesis. This article is part of a Special Issue entitled: Plant Lipid Biology edited by Kent D. Chapman and Ivo Feussner. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. The hypothalamic neural-glial network and the metabolic syndrome

    NARCIS (Netherlands)

    Jastroch, Martin; Morin, Silke; Tschöp, Matthias H.; Yi, Chun-Xia

    2014-01-01

    Despite numerous educational interventions and biomedical research efforts, modern society continues to suffer from obesity and its associated metabolic diseases, such as type 2 diabetes mellitus, and these diseases show little sign of abating. One reason for this is an incomplete understanding of

  19. Genome scale metabolic network reconstruction of Spirochaeta cellobiosiphila

    Directory of Open Access Journals (Sweden)

    Bharat Manna

    2017-10-01

    Full Text Available Substantial rise in the global energy demand is one of the biggest challenges in this century. Environmental pollution due to rapid depletion of the fossil fuel resources and its alarming impact on the climate change and Global Warming have motivated researchers to look for non-petroleum-based sustainable, eco-friendly, renewable, low-cost energy alternatives, such as biofuel. Lignocellulosic biomass is one of the most promising bio-resources with huge potential to contribute to this worldwide energy demand. However, the complex organization of the Cellulose, Hemicellulose and Lignin in the Lignocellulosic biomass requires extensive pre-treatment and enzymatic hydrolysis followed by fermentation, raising overall production cost of biofuel. This encourages researchers to design cost-effective approaches for the production of second generation biofuels. The products from enzymatic hydrolysis of cellulose are mostly glucose monomer or cellobiose unit that are subjected to fermentation. Spirochaeta genus is a well-known group of obligate or facultative anaerobes, living primarily on carbohydrate metabolism. Spirochaeta cellobiosiphila sp. is a facultative anaerobe under this genus, which uses a variety of monosaccharides and disaccharides as energy sources. However, most rapid growth occurs on cellobiose and fermentation yields significant amount of ethanol, acetate, CO2, H2 and small amounts of formate. It is predicted to be promising microbial machinery for industrial fermentation processes for biofuel production. The metabolic pathways that govern cellobiose metabolism in Spirochaeta cellobiosiphila are yet to be explored. The function annotation of the genome sequence of Spirochaeta cellobiosiphila is in progress. In this work we aim to map all the metabolic activities for reconstruction of genome-scale metabolic model of Spirochaeta cellobiosiphila.

  20. Detection of driver metabolites in the human liver metabolic network using structural controllability analysis

    Science.gov (United States)

    2014-01-01

    Background Abnormal states in human liver metabolism are major causes of human liver diseases ranging from hepatitis to hepatic tumor. The accumulation in relevant data makes it feasible to derive a large-scale human liver metabolic network (HLMN) and to discover important biological principles or drug-targets based on network analysis. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis (which is a newly prevailed concept in networks) to biological networks. The exploration on the connections between structural controllability theory and the HLMN could be used to uncover valuable information on the human liver metabolism from a fresh perspective. Results We applied structural controllability analysis to the HLMN and detected driver metabolites. The driver metabolites tend to have strong ability to influence the states of other metabolites and weak susceptibility to be influenced by the states of others. In addition, the metabolites were classified into three classes: critical, high-frequency and low-frequency driver metabolites. Among the identified 36 critical driver metabolites, 27 metabolites were found to be essential; the high-frequency driver metabolites tend to participate in different metabolic pathways, which are important in regulating the whole metabolic systems. Moreover, we explored some other possible connections between the structural controllability theory and the HLMN, and find that transport reactions and the environment play important roles in the human liver metabolism. Conclusion There are interesting connections between the structural controllability theory and the human liver metabolism: driver metabolites have essential biological functions; the crucial role of extracellular metabolites and transport reactions in controlling the HLMN highlights the importance of the environment in the health of human liver metabolism. PMID:24885538

  1. Social Networking as a Facilitator for Lifelong Learning in Multinational Employee’s Career

    Directory of Open Access Journals (Sweden)

    Andreea Nicoleta VISAN

    2017-01-01

    Full Text Available This paper discusses how multinational employees who are leaving in Bucharest, Romania use social networks as a tool for their everyday tasks and work, and how they want to satisfy their personal development needs by having access to information from these digital platforms. The case study described was conducted in Bucharest in 2017 and followed a results analysis with structured tables and graphs. In the study took part 24 participants who were selected among multinational IT employees in Bucharest. Social networks contribute to employee’s lifelong educational process: besides providing them positive gratification, they also contribute to their personal development and careers growth. Even though all individuals who participated in this study use social networks, more efforts should be done in order for companies in Bucharest to know the benefits of social networks and employee’s opinion about their contribution to lifelong learning.

  2. [Gene networks that regulate secondary metabolism in actinomycetes: pleiotropic regulators].

    Science.gov (United States)

    Rabyk, M V; Ostash, B O; Fedorenko, V O

    2014-01-01

    Current advances in the research and practical applications of pleiotropic regulatory genes for antibiotic production in actinomycetes are reviewed. The basic regulatory mechanisms found in these bacteria are outlined. Examples described in the review show the importance of the manipulation of regulatory systems that affect the synthesis of antibiotics for the metabolic engineering of the actinomycetes. Also, the study of these genes is the basis for the development of genetic engineering approaches towards the induction of "cryptic" part of the actinomycetes secondary metabolome, which capacity for production of biologically active compounds is much bigger than the diversity of antibiotics underpinned by traditional microbiological screening. Besides the practical problems, the study of regulatory genes for antibiotic biosynthesis will provide insights into the process of evolution of complex regulatory systems that coordinate the expression of gene operons, clusters and regulons, involved in the control of secondary metabolism and morphogenesis of actinomycetes.

  3. [Controlling arachidonic acid metabolic network: from single- to multi-target inhibitors of key enzymes].

    Science.gov (United States)

    Liu, Ying; Chen, Zheng; Shang, Er-chang; Yang, Kun; Wei, Deng-guo; Zhou, Lu; Jiang, Xiao-lu; He, Chong; Lai, Lu-hua

    2009-03-01

    Inflammatory diseases are common medical conditions seen in disorders of human immune system. There is a great demand for anti-inflammatory drugs. There are major inflammatory mediators in arachidonic acid metabolic network. Several enzymes in this network have been used as key targets for the development of anti-inflammatory drugs. However, specific single-target inhibitors can not sufficiently control the network balance and may cause side effects at the same time. Most inflammation induced diseases come from the complicated coupling of inflammatory cascades involving multiple targets. In order to treat these complicated diseases, drugs that can intervene multi-targets at the same time attracted much attention. The goal of this review is mainly focused on the key enzymes in arachidonic acid metabolic network, such as phospholipase A2, cyclooxygenase, 5-lipoxygenase and eukotriene A4 hydrolase. Advance in single target and multi-targe inhibitors is summarized.

  4. Human-Centered Development of an Online Social Network for Metabolic Syndrome Management.

    Science.gov (United States)

    Núñez-Nava, Jefersson; Orozco-Sánchez, Paola A; López, Diego M; Ceron, Jesus D; Alvarez-Rosero, Rosa E

    2016-01-01

    According to the International Diabetes Federation (IDF), a quarter of the world's population has Metabolic Syndrome (MS). To develop (and assess the users' degree of satisfaction of) an online social network for patients who suffer from Metabolic Syndrome, based on the recommendations and requirements of the Human-Centered Design. Following the recommendations of the ISO 9241-210 for Human-Centered Design (HCD), an online social network was designed to promote physical activity and healthy nutrition. In order to guarantee the active participation of the users during the development of the social network, a survey, an in-depth interview, a focal group, and usability tests were carried out with people suffering from MS. The study demonstrated how the different activities, recommendations, and requirements of the ISO 9241-210 are integrated into a traditional software development process. Early usability tests demonstrated that the user's acceptance and the effectiveness and efficiency of the social network are satisfactory.

  5. Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets

    Directory of Open Access Journals (Sweden)

    Ina Koch

    2017-06-01

    Full Text Available Motivation:Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem.Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs.Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the

  6. Social networks improve leaderless group navigation by facilitating long-distance communication

    Directory of Open Access Journals (Sweden)

    Nikolai W. F. BODE, A. Jamie WOOD, Daniel W. FRANKS

    2012-04-01

    Full Text Available Group navigation is of great importance for many animals, such as migrating flocks of birds or shoals of fish. One theory states that group membership can improve navigational accuracy compared to limited or less accurate individual navigational ability in groups without leaders (“Many-wrongs principle”. Here, we simulate leaderless group navigation that includes social connections as preferential interactions between individuals. Our results suggest that underlying social networks can reduce navigational errors of groups and increase group cohesion. We use network summary statistics, in particular network motifs, to study which characteristics of networks lead to these improvements. It is networks in which preferences between individuals are not clustered, but spread evenly across the group that are advantageous in group navigation by effectively enhancing long-distance information exchange within groups. We suggest that our work predicts a base-line for the type of social structure we might expect to find in group-living animals that navigate without leaders [Current Zoology 58 (2: 329-341, 2012].

  7. Acorn: A grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface

    Directory of Open Access Journals (Sweden)

    Bushell Michael E

    2011-05-01

    Full Text Available Abstract Background Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment. Results Here, we present Acorn, an open source (GNU GPL grid computing system for constraint-based simulations of genome scale metabolic reaction networks within an interactive web environment. The grid-based architecture allows efficient execution of computationally intensive, iterative protocols such as Flux Variability Analysis, which can be readily scaled up as the numbers of models (and users increase. The web interface uses AJAX, which facilitates efficient model browsing and other search functions, and intuitive implementation of appropriate simulation conditions. Research groups can install Acorn locally and create user accounts. Users can also import models in the familiar SBML format and link reaction formulas to major functional genomics portals of choice. Selected models and simulation results can be shared between different users and made publically available. Users can construct pathway map layouts and import them into the server using a desktop editor integrated within the system. Pathway maps are then used to visualise numerical results within the web environment. To illustrate these features we have deployed Acorn and created a

  8. Metabolic network as a progression biomarker of premanifest Huntington's disease

    NARCIS (Netherlands)

    Tang, Chris C.; Feigin, Andrew; Ma, Yilong; Habeck, Christian; Paulsen, Jane S.; Leenders, Klaus L.; Teune, Laura K.; van Oostrom, Joost C. H.; Guttman, Mark; Dhawan, Vijay; Eidelberg, David

    Background. The evaluation of effective disease-modifying therapies for neurodegenerative disorders relies on objective and accurate measures of progression in at-risk individuals. Here we used a computational approach to identify a functional brain network associated with the progression of

  9. Computing autocatalytic sets to unravel inconsistencies in metabolic network reconstructions

    DEFF Research Database (Denmark)

    Schmidt, R.; Waschina, S.; Boettger-Schmidt, D.

    2015-01-01

    are neglected by other gap-finding methods. We tested our method on the Model SEED, which is the largest repository for automatically generated genome-scale network reconstructions. In this way, we were able to identify a significant number of missing pathways in several of these reconstructions. Hence...

  10. Long-distance mechanism of neurotransmitter recycling mediated by glial network facilitates visual function in Drosophila.

    Science.gov (United States)

    Chaturvedi, Ratna; Reddig, Keith; Li, Hong-Sheng

    2014-02-18

    Neurons rely on glia to recycle neurotransmitters such as glutamate and histamine for sustained signaling. Both mammalian and insect glia form intercellular gap-junction networks, but their functional significance underlying neurotransmitter recycling is unknown. Using the Drosophila visual system as a genetic model, here we show that a multicellular glial network transports neurotransmitter metabolites between perisynaptic glia and neuronal cell bodies to mediate long-distance recycling of neurotransmitter. In the first visual neuropil (lamina), which contains a multilayer glial network, photoreceptor axons release histamine to hyperpolarize secondary sensory neurons. Subsequently, the released histamine is taken up by perisynaptic epithelial glia and converted into inactive carcinine through conjugation with β-alanine for transport. In contrast to a previous assumption that epithelial glia deliver carcinine directly back to photoreceptor axons for histamine regeneration within the lamina, we detected both carcinine and β-alanine in the fly retina, where they are found in photoreceptor cell bodies and surrounding pigment glial cells. Downregulating Inx2 gap junctions within the laminar glial network causes β-alanine accumulation in retinal pigment cells and impairs carcinine synthesis, leading to reduced histamine levels and photoreceptor synaptic vesicles. Consequently, visual transmission is impaired and the fly is less responsive in a visual alert analysis compared with wild type. Our results suggest that a gap junction-dependent laminar and retinal glial network transports histamine metabolites between perisynaptic glia and photoreceptor cell bodies to mediate a novel, long-distance mechanism of neurotransmitter recycling, highlighting the importance of glial networks in the regulation of neuronal functions.

  11. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

    Science.gov (United States)

    Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L.; Schulz, Andreas L.; Ohl, Frank W.; Lommerzheim, Marcel; Neumann, Heiko

    2016-01-01

    Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

  12. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning

    Directory of Open Access Journals (Sweden)

    Christian Jarvers

    2016-11-01

    Full Text Available Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e. tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN, which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that

  13. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning.

    Science.gov (United States)

    Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L; Schulz, Andreas L; Ohl, Frank W; Lommerzheim, Marcel; Neumann, Heiko

    2016-01-01

    Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

  14. Similarity facilitates relationships on social networks: a field experiment on facebook.

    Science.gov (United States)

    Martin, Angélique; Jacob, Céline; Guéguen, Nicolas

    2013-08-01

    People interact more readily with someone with whom they think they have something in common, but the effect of an incidental similarity has never been examined on social networks. Facebook users were contacted by a stranger who also possessed a Facebook page and who asked them to become his friend. The request message contained one item of similarity, two items of similarity, or none. Compliance to the request was the dependent variable. Increased compliance to the request was found when comparing the two similarity conditions with the control no-similarity condition. However, no difference was found between the two similarity conditions. Similarity appears to foster relationships on social networks.

  15. Cerebral metabolic correlates of attention networks in Alzheimer's Disease: A study of the Stroop.

    Science.gov (United States)

    Melrose, Rebecca J; Young, Stephanie; Weissberger, Gali H; Natta, Laura; Harwood, Dylan; Mandelkern, Mark; Sultzer, David L

    2017-11-01

    Patients with Alzheimer's Disease (AD) show difficulties with attention. Cognitive neuroscience models posit that attention can be broken down into alerting, orienting, and executive networks. We used the Stroop Color-Word test to interrogate the neural correlates of attention deficits in AD. We hypothesized that the Word, Color, and Color-Word conditions of the Stroop would all tap into the alerting and orienting networks. The Color-Word condition would additionally tap into the executive network. A ratio of Color-Word to Color naming performance would isolate the executive network from the others. To identify the neural underpinnings of attention in AD we correlated performance on the Stroop with brain metabolic activity. Sixty-six patients with probable AD completed [ 18 F] fluorodeoxyglucose PET scanning and neuropsychological testing. Analysis was conducted with SPM12 (p<0.001 uncorrected, extent threshold 50 voxels). Performance on the Word, Color, and Color-Word conditions directly correlated with metabolic rate in right inferior parietal lobules/intraparietal sulci. The Color-Word/Color ratio revealed associations with metabolic rate in right medial prefrontal cortex and insula/operculum. Overall findings were largely consistent with the hypothesized neuroanatomical substrates of the alerting, orienting, and executive networks. As such, attention deficits in AD reflect compromise to multiple large-scale networks. Published by Elsevier Ltd.

  16. Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes.

    Science.gov (United States)

    Zelezniak, Aleksej; Pers, Tune H; Soares, Simão; Patti, Mary Elizabeth; Patil, Kiran Raosaheb

    2010-04-01

    Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites--metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM.

  17. Speech Experience Shapes the Speechreading Network and Subsequent Deafness Facilitates It

    Science.gov (United States)

    Suh, Myung-Whan; Lee, Hyo-Jeong; Kim, June Sic; Chung, Chun Kee; Oh, Seung-Ha

    2009-01-01

    Speechreading is a visual communicative skill for perceiving speech. In this study, we tested the effects of speech experience and deafness on the speechreading neural network in normal hearing controls and in two groups of deaf patients who became deaf either before (prelingual deafness) or after (postlingual deafness) auditory language…

  18. Transcriptional regulation and steady-state modeling of metabolic networks

    DEFF Research Database (Denmark)

    Zelezniak, Aleksej

    with the changes in gene expression of both reactions that produce and reactions that consume a given metabolite. Analysis of a large compendium of gene expression data further suggested that, contrary to previous thinking, transcriptional regulation at metabolic branch points is highly plastic and, in several...... to exhibit a biodegradation performance superior to pure cultures, making them attractive research targets. It is believed that nutrition plays a crucial role in shaping microbial communities. Interspecies metabolite cross-feeding can confer several advantages to the community as a whole. For example, more...

  19. Flux Balance Analysis of Cyanobacterial Metabolism.The Metabolic Network of Synechocystis sp. PCC 6803

    Czech Academy of Sciences Publication Activity Database

    Knoop, H.; Gründel, M.; Zilliges, Y.; Lehmann, R.; Hoffmann, S.; Lockau, W.; Steuer, Ralf

    2013-01-01

    Roč. 9, č. 6 (2013), e1003081-e1003081 ISSN 1553-7358 R&D Projects: GA MŠk(CZ) EE2.3.20.0256 Institutional support: RVO:67179843 Keywords : SP STRAIN PCC-6803 * SP ATCC 51142 * photoautotrophic metabolism * anacystis-nidulans * reconstructions * pathway * plants * models * growth Subject RIV: EI - Biotechnology ; Bionics Impact factor: 4.829, year: 2013

  20. Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501.

    Science.gov (United States)

    Babaei, Parizad; Marashi, Sayed-Amir; Asad, Sedigheh

    2015-11-01

    Pseudomonas stutzeri A1501 is an endophytic bacterium capable of nitrogen fixation. This strain has been isolated from the rice rhizosphere and provides the plant with fixed nitrogen and phytohormones. These interesting features encouraged us to study the metabolism of this microorganism at the systems-level. In this work, we present the first genome-scale metabolic model (iPB890) for P. stutzeri, involving 890 genes, 1135 reactions, and 813 metabolites. A combination of automatic and manual approaches was used in the reconstruction process. Briefly, using the metabolic networks of Pseudomonas aeruginosa and Pseudomonas putida as templates, a draft metabolic network of P. stutzeri was reconstructed. Then, the draft network was driven through an iterative and curative process of gap filling. In the next step, the model was evaluated using different experimental data such as specific growth rate, Biolog substrate utilization data and other experimental observations. In most of the evaluation cases, the model was successful in correctly predicting the cellular phenotypes. Thus, we posit that the iPB890 model serves as a suitable platform to explore the metabolism of P. stutzeri.

  1. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders

    Directory of Open Access Journals (Sweden)

    Qingying Meng

    2016-05-01

    Full Text Available Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient–host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control and hippocampus (cognitive processing from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine.

  2. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

    Science.gov (United States)

    Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

    2016-05-01

    Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  3. Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions.

    Science.gov (United States)

    Takemoto, Kazuhiro; Aie, Kazuki

    2017-05-25

    Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host-pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host-pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host-pathogen interactions.

  4. Random sampling of elementary flux modes in large-scale metabolic networks.

    Science.gov (United States)

    Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugénio C; Rocha, Isabel

    2012-09-15

    The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. dmachado@deb.uminho.pt Supplementary data are available at Bioinformatics online.

  5. Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli

    DEFF Research Database (Denmark)

    Seo, Sang Woo; Kim, Donghyuk; Latif, Haythem

    2014-01-01

    The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response...

  6. Genome-scale metabolic network of Cordyceps militaris useful for comparative analysis of entomopathogenic fungi.

    Science.gov (United States)

    Vongsangnak, Wanwipa; Raethong, Nachon; Mujchariyakul, Warasinee; Nguyen, Nam Ninh; Leong, Hon Wai; Laoteng, Kobkul

    2017-08-30

    The first genome-scale metabolic network of Cordyceps militaris (iWV1170) was constructed representing its whole metabolisms, which consisted of 894 metabolites and 1,267 metabolic reactions across five compartments, including the plasma membrane, cytoplasm, mitochondria, peroxisome and extracellular space. The iWV1170 could be exploited to explain its phenotypes of growth ability, cordycepin and other metabolites production on various substrates. A high number of genes encoding extracellular enzymes for degradation of complex carbohydrates, lipids and proteins were existed in C. militaris genome. By comparative genome-scale analysis, the adenine metabolic pathway towards putative cordycepin biosynthesis was reconstructed, indicating their evolutionary relationships across eleven species of entomopathogenic fungi. The overall metabolic routes involved in the putative cordycepin biosynthesis were also identified in C. militaris, including central carbon metabolism, amino acid metabolism (glycine, l-glutamine and l-aspartate) and nucleotide metabolism (adenosine and adenine). Interestingly, a lack of the sequence coding for ribonucleotide reductase inhibitor was observed in C. militaris that might contribute to its over-production of cordycepin. Copyright © 2017. Published by Elsevier B.V.

  7. Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism.

    Science.gov (United States)

    Tabe-Bordbar, Shayan; Marashi, Sayed-Amir

    2013-12-01

    Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing EMs may hamper their applicability as, in most genome-scale metabolic models, no EM can be computed due to running out of memory. In this study, we present a method for computing randomly sampled EMs. In this approach, a network reduction algorithm is used for EM computation, which is based on flux balance-based methods. We show that this approach can be used to recover the EMs in the medium- and genome-scale metabolic network models, while the EMs are sampled in an unbiased way. The applicability of such results is shown by computing “estimated” control-effective flux values in Escherichia coli metabolic network.

  8. Quantitative Tools for Dissection of Hydrogen-Producing Metabolic Networks-Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Rabinowitz, Joshua D.; Dismukes, G.Charles.; Rabitz, Herschel A.; Amador-Noguez, Daniel

    2012-10-19

    During this project we have pioneered the development of integrated experimental-computational technologies for the quantitative dissection of metabolism in hydrogen and biofuel producing microorganisms (i.e. C. acetobutylicum and various cyanobacteria species). The application of these new methodologies resulted in many significant advances in the understanding of the metabolic networks and metabolism of these organisms, and has provided new strategies to enhance their hydrogen or biofuel producing capabilities. As an example, using mass spectrometry, isotope tracers, and quantitative flux-modeling we mapped the metabolic network structure in C. acetobutylicum. This resulted in a comprehensive and quantitative understanding of central carbon metabolism that could not have been obtained using genomic data alone. We discovered that biofuel production in this bacterium, which only occurs during stationary phase, requires a global remodeling of central metabolism (involving large changes in metabolite concentrations and fluxes) that has the effect of redirecting resources (carbon and reducing power) from biomass production into solvent production. This new holistic, quantitative understanding of metabolism is now being used as the basis for metabolic engineering strategies to improve solvent production in this bacterium. In another example, making use of newly developed technologies for monitoring hydrogen and NAD(P)H levels in vivo, we dissected the metabolic pathways for photobiological hydrogen production by cyanobacteria Cyanothece sp. This investigation led to the identification of multiple targets for improving hydrogen production. Importantly, the quantitative tools and approaches that we have developed are broadly applicable and we are now using them to investigate other important biofuel producers, such as cellulolytic bacteria.

  9. Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

    Science.gov (United States)

    Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

    2016-01-01

    We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can

  10. Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems.

    Science.gov (United States)

    Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

    2016-01-01

    We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can

  11. Metabolic network modeling of microbial interactions in natural and engineered environmental systems

    Directory of Open Access Journals (Sweden)

    Octavio ePerez-Garcia

    2016-05-01

    Full Text Available We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA, experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e. i lumped networks, ii compartment per guild networks, iii bi-level optimization simulations and iv dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial

  12. Molecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion mice.

    Science.gov (United States)

    Miraldi, Emily R; Sharfi, Hadar; Friedline, Randall H; Johnson, Hannah; Zhang, Tejia; Lau, Ken S; Ko, Hwi Jin; Curran, Timothy G; Haigis, Kevin M; Yaffe, Michael B; Bonneau, Richard; Lauffenburger, Douglas A; Kahn, Barbara B; Kim, Jason K; Neel, Benjamin G; Saghatelian, Alan; White, Forest M

    2013-07-24

    Metabolic syndrome describes a set of obesity-related disorders that increase diabetes, cardiovascular, and mortality risk. Studies of liver-specific protein-tyrosine phosphatase 1b (PTP1b) deletion mice (L-PTP1b(-/-)) suggest that hepatic PTP1b inhibition would mitigate metabolic-syndrome through amelioration of hepatic insulin resistance, endoplasmic-reticulum stress, and whole-body lipid metabolism. However, the altered molecular-network states underlying these phenotypes are poorly understood. We used mass spectrometry to quantify protein-phosphotyrosine network changes in L-PTP1b(-/-) mouse livers relative to control mice on normal and high-fat diets. We applied a phosphosite-set-enrichment analysis to identify known and novel pathways exhibiting PTP1b- and diet-dependent phosphotyrosine regulation. Detection of a PTP1b-dependent, but functionally uncharacterized, set of phosphosites on lipid-metabolic proteins motivated global lipidomic analyses that revealed altered polyunsaturated-fatty-acid (PUFA) and triglyceride metabolism in L-PTP1b(-/-) mice. To connect phosphosites and lipid measurements in a unified model, we developed a multivariate-regression framework, which accounts for measurement noise and systematically missing proteomics data. This analysis resulted in quantitative models that predict roles for phosphoproteins involved in oxidation-reduction in altered PUFA and triglyceride metabolism.

  13. c-Myc activates multiple metabolic networks to generate substrates for cell-cycle entry.

    Energy Technology Data Exchange (ETDEWEB)

    Morrish, Fionnuala M.; Isern, Nancy; Sadilek, Martin; Jeffrey, Mark; Hockenbery, David M.

    2009-05-18

    Cell proliferation requires the coordinated activity of cytosolic and mitochondrial metabolic pathways to provide ATP and building blocks for DNA, RNA, and protein synthesis. Many metabolic pathway genes are targets of the c-myc oncogene and cell cycle regulator. However, the contribution of c-Myc to the activation of cytosolic and mitochondrial metabolic networks during cell cycle entry is unknown. Here, we report the metabolic fates of [U-13C] glucose in serum-stimulated myc-/- and myc+/+ fibroblasts by 13C isotopomer NMR analysis. We demonstrate that endogenous c-myc increased 13C-labeling of ribose sugars, purines, and amino acids, indicating partitioning of glucose carbons into C1/folate and pentose phosphate pathways, and increased tricarboxylic acid cycle turnover at the expense of anaplerotic flux. Myc expression also increased global O-linked GlcNAc protein modification, and inhibition of hexosamine biosynthesis selectively reduced growth of Myc-expressing cells, suggesting its importance in Myc-induced proliferation. These data reveal a central organizing role for the Myc oncogene in the metabolism of cycling cells. The pervasive deregulation of this oncogene in human cancers may be explained by its role in directing metabolic networks required for cell proliferation.

  14. Use of multiple dispersal pathways facilitates amphibian persistence in stream networks

    Science.gov (United States)

    Campbell, Grant E.H.; Nichols, J.D.; Lowe, W.H.; Fagan, W.F.

    2010-01-01

    Although populations of amphibians are declining worldwide, there is no evidence that salamanders occupying small streams are experiencing enigmatic declines, and populations of these species seem stable. Theory predicts that dispersal through multiple pathways can stabilize populations, preventing extinction in habitat networks. However, empirical data to support this prediction are absent for most species, especially those at risk of decline. Our mark-recapture study of stream salamanders reveals both a strong upstream bias in dispersal and a surprisingly high rate of overland dispersal to adjacent headwater streams. This evidence of route-dependent variation in dispersal rates suggests a spatial mechanism for population stability in headwater-stream salamanders. Our results link the movement behavior of stream salamanders to network topology, and they underscore the importance of identifying and protecting critical dispersal pathways when addressing region-wide population declines.

  15. Synaptic Noise Facilitates the Emergence of Self-Organized Criticality in the Caenorhabditis elegans Neuronal Network

    OpenAIRE

    Çiftçi, Koray

    2017-01-01

    Avalanches with power-law distributed size parameters have been observed in neuronal networks. This observation might be a manifestation of the self-organized criticality (SOC). Yet, the physiological mechanicsm of this behavior is currently unknown. Describing synaptic noise as transmission failures mainly originating from the probabilistic nature of neurotransmitter release, this study investigates the potential of this noise as a mechanism for driving the functional architecture of the neu...

  16. Data-driven integration of genome-scale regulatory and metabolic network models

    Science.gov (United States)

    Imam, Saheed; Schäuble, Sascha; Brooks, Aaron N.; Baliga, Nitin S.; Price, Nathan D.

    2015-01-01

    Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert—a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system. PMID:25999934

  17. Data-driven integration of genome-scale regulatory and metabolic network models

    Directory of Open Access Journals (Sweden)

    Saheed eImam

    2015-05-01

    Full Text Available Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription and signaling have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert – a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

  18. Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

    Science.gov (United States)

    Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-03-01

    "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Role of Social Knowledge Networking technology in facilitating meaningful use of Electronic Health Record medication reconciliation.

    Science.gov (United States)

    Rangachari, Pavani

    2016-06-01

    Despite the federal policy impetus towards EHR Medication Reconciliation, hospital adherence has lagged for one chief reason; low physician engagement, which in turn emanates from lack of consensus in regard to which physician is responsible for managing a patient's medication list, and the importance of medication reconciliation as a tool for improving patient safety and quality of care. The Technology-in-Practice (TIP) framework stresses the role of human action in enacting structures of technology use or "technologies-in-practice." Applying the TIP framework to the EHR Medication Reconciliation context, helps frame the problem as one of low physician engagement in performing EHR Medication Reconciliation, translating to limited-use-EHR-in-practice. Concurrently, the problem suggests a hierarchical network structure, reflecting limited communication among hospital administrators and clinical providers on the importance of EHR Medication Reconciliation in improving patient safety. Integrating the TIP literature with the more recent knowledge-in-Practice (KIP) literature suggests that EHR-in-practice could be transformed from "limited use" to "meaningful use" through the use of Social Knowledge Networking (SKN) Technology to create new social network structures, and enable engagement, learning, and practice change. Correspondingly, the objectives of this paper are to: 1) Conduct a narrative review of the literature on "technology use," to understand how technologies-in-practice may be transformed from limited use to meaningful use; 2) Conduct a narrative review of the literature on "organizational change implementation," to understand how changes in technology use could be successfully implemented and sustained in a healthcare organizational context; and 3) Apply lessons learned from the narrative literature reviews to identify strategies for the meaningful use and successful implementation of EHR Medication Reconciliation technology.

  20. Facilitating employment opportunities for adults with intellectual and developmental disability through parents and social networks.

    Science.gov (United States)

    Petner-Arrey, Jami; Howell-Moneta, Angela; Lysaght, Rosemary

    2015-07-01

    People with intellectual and developmental disability (IDD) have historically had high unemployment and underemployment rates and continue to face significant barriers to attaining and sustaining employment. The purpose of this research, conducted in Ontario, Canada was to better understand the experiences of people with IDD gaining and keeping productivity roles. We used qualitative semi-structured interviews with 74 participants with IDD and their families or caregivers as proxies regarding the employment of a person with IDD. We selected a sample of persons from three different geographic regions in Ontario, Canada, and analyzed data through coding methods consistent with a grounded theory approach. Our results demonstrate the importance of parents and other members of social and family networks relative to connecting with work options and sustaining work over time, especially through continued advocacy and investment. Parents helped individuals with IDD negotiate the right job fit, though they often encountered challenges as a result of their efforts. Practitioners must understand how to support parents to be effective advocates for their adult children with IDD, assist them to develop and maintain their social networks and help them to avoid caregiver burnout. Implications for Rehabilitation People with intellectual and developmental disability (IDD) face numerous challenges in indentifying work options and overcoming barriers to employment. Parents and other non-paid support members of social networks can be instrumental in ensuring that persons with IDD not only secure initial job placements, but also sustain employment and employment alternatives. Professionals that support persons with IDD can direct their efforts to helping persons with IDD develop strong social connections, as well as helping parents to prevent burnout.

  1. Network-based analysis of the sphingolipid metabolism in hypertension

    DEFF Research Database (Denmark)

    Fenger, Mogens; Linneberg, Allan; Jeppesen, Jørgen

    2015-01-01

    of the complex genotype determines the state and dynamics of any trait, which may be modified to various extent by non-genetic factors. Thus, diseases are heterogenous ensembles of conditions with a common endpoint. Numerous studies have been performed to define genes of importance for a trait or disease......Common diseases like essential hypertension or diabetes mellitus are complex as they are polygenic in nature, such that each genetic variation only has a small influence on the disease. Genes operates in integrated networks providing the blue-print for all biological processes and conditional......, but only a few genes with small effect have been identified. The major reasons for this modest progress is the unresolved heterogeneity of the regulation of blood pressure and the shortcomings of the prevailing monogenic approach to capture genetic effects in a polygenic condition. Here, a two...

  2. Compartmentalized metabolic network reconstruction of microbial communities to determine the effect of agricultural intervention on soils.

    Science.gov (United States)

    Alvarez-Silva, María Camila; Álvarez-Yela, Astrid Catalina; Gómez-Cano, Fabio; Zambrano, María Mercedes; Husserl, Johana; Danies, Giovanna; Restrepo, Silvia; González-Barrios, Andrés Fernando

    2017-01-01

    Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA) were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community.

  3. Compartmentalized metabolic network reconstruction of microbial communities to determine the effect of agricultural intervention on soils.

    Directory of Open Access Journals (Sweden)

    María Camila Alvarez-Silva

    Full Text Available Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community.

  4. Dynamic Metabolic Footprinting Reveals the Key Components of Metabolic Network in Yeast Saccharomyces cerevisiae

    DEFF Research Database (Denmark)

    Chumnanpuen, Pramote; Hansen, Michael Adsetts Edberg; Smedsgaard, Jørn

    2014-01-01

    relies on analysis at a single time point. Using direct infusion-mass spectrometry (DI-MS), we could observe the dynamic metabolic footprinting in yeast S. cerevisiae BY4709 (wild type) cultured on 3 different C-sources (glucose, glycerol, and ethanol) and sampled along 10 time points with 5 biological...... ionization (ESI) modes were performed to obtain the complete information about the metabolite content. Using sparse principal component analysis (Sparse PCA), we further identified those pairs of metabolites that significantly contribute to the separation. From the list of significant metabolite pairs, we...

  5. Optimization of Bioprocess Productivity Based on Metabolic-Genetic Network Models with Bilevel Dynamic Programming.

    Science.gov (United States)

    Jabarivelisdeh, Banafsheh; Waldherr, Steffen

    2018-03-26

    One of the main goals of metabolic engineering is to obtain high levels of a microbial product through genetic modifications. To improve the productivity of such a process, the dynamic implementation of metabolic engineering strategies has been proven to be more beneficial compared to static genetic manipulations in which the gene expression is not controlled over time, by resolving the trade-off between growth and production. In this work, a bilevel optimization framework based on constraint-based models is applied to identify an optimal strategy for dynamic genetic and process level manipulations to increase productivity. The dynamic enzyme-cost flux balance analysis (deFBA) as underlying metabolic network model captures the network dynamics and enables the analysis of temporal regulation in the metabolic-genetic network. We apply our computational framework to maximize ethanol productivity in a batch process with Escherichia coli. The results highlight the importance of integrating the genetic level and enzyme production and degradation processes for obtaining optimal dynamic gene and process manipulations. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  6. GAM: a web-service for integrated transcriptional and metabolic network analysis.

    Science.gov (United States)

    Sergushichev, Alexey A; Loboda, Alexander A; Jha, Abhishek K; Vincent, Emma E; Driggers, Edward M; Jones, Russell G; Pearce, Edward J; Artyomov, Maxim N

    2016-07-08

    Novel techniques for high-throughput steady-state metabolomic profiling yield information about changes of nearly thousands of metabolites. Such metabolomic profiles, when analyzed together with transcriptional profiles, can reveal novel insights about underlying biological processes. While a number of conceptual approaches have been developed for data integration, easily accessible tools for integrated analysis of mammalian steady-state metabolomic and transcriptional data are lacking. Here we present GAM ('genes and metabolites'): a web-service for integrated network analysis of transcriptional and steady-state metabolomic data focused on identification of the most changing metabolic subnetworks between two conditions of interest. In the web-service, we have pre-assembled metabolic networks for humans, mice, Arabidopsis and yeast and adapted exact solvers for an optimal subgraph search to work in the context of these metabolic networks. The output is the most regulated metabolic subnetwork of size controlled by false discovery rate parameters. The subnetworks are then visualized online and also can be downloaded in Cytoscape format for subsequent processing. The web-service is available at: https://artyomovlab.wustl.edu/shiny/gam/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  7. Thermodynamic calculations for biochemical transport and reaction processes in metabolic networks.

    Science.gov (United States)

    Jol, Stefan J; Kümmel, Anne; Hatzimanikatis, Vassily; Beard, Daniel A; Heinemann, Matthias

    2010-11-17

    Thermodynamic analysis of metabolic networks has recently generated increasing interest for its ability to add constraints on metabolic network operation, and to combine metabolic fluxes and metabolite measurements in a mechanistic manner. Concepts for the calculation of the change in Gibbs energy of biochemical reactions have long been established. However, a concept for incorporation of cross-membrane transport in these calculations is still missing, although the theory for calculating thermodynamic properties of transport processes is long known. Here, we have developed two equivalent equations to calculate the change in Gibbs energy of combined transport and reaction processes based on two different ways of treating biochemical thermodynamics. We illustrate the need for these equations by showing that in some cases there is a significant difference between the proposed correct calculation and using an approximative method. With the developed equations, thermodynamic analysis of metabolic networks spanning over multiple physical compartments can now be correctly described. Copyright © 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  8. Combined metabolomic and correlation networks analyses reveal fumarase insufficiency altered amino acid metabolism.

    Science.gov (United States)

    Hou, Entai; Li, Xian; Liu, Zerong; Zhang, Fuchang; Tian, Zhongmin

    2018-04-01

    Fumarase catalyzes the interconversion of fumarate and l-malate in the tricarboxylic acid cycle. Fumarase insufficiencies were associated with increased levels of fumarate, decreased levels of malate and exacerbated salt-induced hypertension. To gain insights into the metabolism profiles induced by fumarase insufficiency and identify key regulatory metabolites, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze fumarase insufficient human umbilical vein endothelial cells (HUVEC) and negative controls. A total of 24 altered metabolites involved in seven metabolic pathways were identified as significantly altered, and enriched for the biological module of amino acids metabolism. In addition, Pearson correlation network analysis revealed that fumaric acid, l-malic acid, l-aspartic acid, glycine and l-glutamic acid were hub metabolites according to Pagerank based on their three centrality indices. Alanine aminotransferase and glutamate dehydrogenase activities increased significantly in fumarase deficiency HUVEC. These results confirmed that fumarase insufficiency altered amino acid metabolism. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism at metabolomics level. Copyright © 2017 John Wiley & Sons, Ltd.

  9. Adenylate Kinase and AMP Signaling Networks: Metabolic Monitoring, Signal Communication and Body Energy Sensing

    Directory of Open Access Journals (Sweden)

    Andre Terzic

    2009-04-01

    Full Text Available Adenylate kinase and downstream AMP signaling is an integrated metabolic monitoring system which reads the cellular energy state in order to tune and report signals to metabolic sensors. A network of adenylate kinase isoforms (AK1-AK7 are distributed throughout intracellular compartments, interstitial space and body fluids to regulate energetic and metabolic signaling circuits, securing efficient cell energy economy, signal communication and stress response. The dynamics of adenylate kinase-catalyzed phosphotransfer regulates multiple intracellular and extracellular energy-dependent and nucleotide signaling processes, including excitation-contraction coupling, hormone secretion, cell and ciliary motility, nuclear transport, energetics of cell cycle, DNA synthesis and repair, and developmental programming. Metabolomic analyses indicate that cellular, interstitial and blood AMP levels are potential metabolic signals associated with vital functions including body energy sensing, sleep, hibernation and food intake. Either low or excess AMP signaling has been linked to human disease such as diabetes, obesity and hypertrophic cardiomyopathy. Recent studies indicate that derangements in adenylate kinase-mediated energetic signaling due to mutations in AK1, AK2 or AK7 isoforms are associated with hemolytic anemia, reticular dysgenesis and ciliary dyskinesia. Moreover, hormonal, food and antidiabetic drug actions are frequently coupled to alterations of cellular AMP levels and associated signaling. Thus, by monitoring energy state and generating and distributing AMP metabolic signals adenylate kinase represents a unique hub within the cellular homeostatic network.

  10. BAP1 inhibits the ER stress gene regulatory network and modulates metabolic stress response.

    Science.gov (United States)

    Dai, Fangyan; Lee, Hyemin; Zhang, Yilei; Zhuang, Li; Yao, Hui; Xi, Yuanxin; Xiao, Zhen-Dong; You, M James; Li, Wei; Su, Xiaoping; Gan, Boyi

    2017-03-21

    The endoplasmic reticulum (ER) is classically linked to metabolic homeostasis via the activation of unfolded protein response (UPR), which is instructed by multiple transcriptional regulatory cascades. BRCA1 associated protein 1 (BAP1) is a tumor suppressor with de-ubiquitinating enzyme activity and has been implicated in chromatin regulation of gene expression. Here we show that BAP1 inhibits cell death induced by unresolved metabolic stress. This prosurvival role of BAP1 depends on its de-ubiquitinating activity and correlates with its ability to dampen the metabolic stress-induced UPR transcriptional network. BAP1 inhibits glucose deprivation-induced reactive oxygen species and ATP depletion, two cellular events contributing to the ER stress-induced cell death. In line with this, Bap1 KO mice are more sensitive to tunicamycin-induced renal damage. Mechanically, we show that BAP1 represses metabolic stress-induced UPR and cell death through activating transcription factor 3 (ATF3) and C/EBP homologous protein (CHOP), and reveal that BAP1 binds to ATF3 and CHOP promoters and inhibits their transcription. Taken together, our results establish a previously unappreciated role of BAP1 in modulating the cellular adaptability to metabolic stress and uncover a pivotal function of BAP1 in the regulation of the ER stress gene-regulatory network. Our study may also provide new conceptual framework for further understanding BAP1 function in cancer.

  11. Transplantable living scaffolds comprised of micro-tissue engineered aligned astrocyte networks to facilitate central nervous system regeneration

    Science.gov (United States)

    Winter, Carla C.; Katiyar, Kritika S.; Hernandez, Nicole S.; Song, Yeri J.; Struzyna, Laura A.; Harris, James P.; Cullen, D. Kacy

    2017-01-01

    Neurotrauma, stroke, and neurodegenerative disease may result in widespread loss of neural cells as well as the complex interconnectivity necessary for proper central nervous system function, generally resulting in permanent functional deficits. Potential regenerative strategies involve the recruitment of endogenous neural stem cells and/or directed axonal regeneration through the use of tissue engineered “living scaffolds” built to mimic features of three-dimensional (3-D) in vivo migratory or guidance pathways. Accordingly, we devised a novel biomaterial encasement scheme using tubular hydrogel-collagen micro-columns that facilitated the self-assembly of seeded astrocytes into 3-D living scaffolds consisting of long, cable-like aligned astrocytic networks. Here, robust astrocyte alignment was achieved within a micro-column inner diameter (ID) of 180 μm or 300–350 μm but not 1.0 mm, suggesting that radius of curvature dictated the extent of alignment. Moreover, within small ID micro-columns, >70% of the astrocytes assumed a bi-polar morphology, versus ~10% in larger micro-columns or planar surfaces. Cell–cell interactions also influenced the aligned architecture, as extensive astrocyte-collagen contraction was achieved at high (9–12 × 105 cells/mL) but not lower (2–6 × 105 cells/mL) seeding densities. This high density micro-column seeding led to the formation of ultra-dense 3-D “bundles” of aligned bi-polar astrocytes within collagen measuring up to 150 μm in diameter yet extending to a remarkable length of over 2.5 cm. Importantly, co-seeded neurons extended neurites directly along the aligned astrocytic bundles, demonstrating permissive cues for neurite extension. These transplantable cable-like astrocytic networks structurally mimic the glial tube that guides neuronal progenitor migration in vivo along the rostral migratory stream, and therefore may be useful to guide progenitor cells to repopulate sites of widespread neurodegeneration

  12. Modeling and Robustness Analysis of Biochemical Networks of Glycerol Metabolism by Klebsiella Pneumoniae

    Science.gov (United States)

    Ye, Jianxiong; Feng, Enmin; Wang, Lei; Xiu, Zhilong; Sun, Yaqin

    Glycerol bioconversion to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae (K. pneumoniae) can be characterized by an intricate network of interactions among biochemical fluxes, metabolic compounds, key enzymes and genetic regulatory. To date, there still exist some uncertain factors in this complex network because of the limitation in bio-techniques, especially in measuring techniques for intracellular substances. In this paper, among these uncertain factors, we aim to infer the transport mechanisms of glycerol and 1,3-PD across the cell membrane, which have received intensive interest in recent years. On the basis of different inferences of the transport mechanisms, we reconstruct various metabolic networks correspondingly and subsequently develop their dynamical systems (S-systems). To determine the most reasonable metabolic network from all possible ones, we establish a quantitative definition of biological robustness and undertake parameter identification and robustness analysis for each system. Numerical results show that it is most possible that both glycerol and 1,3-PD pass the cell membrane by active transport and passive diffusion.

  13. Emergence of Complexity in Protein Functions and Metabolic Networks

    Science.gov (United States)

    Pohorille, Andzej

    2009-01-01

    In modern organisms proteins perform a majority of cellular functions, such as chemical catalysis, energy transduction and transport of material across cell walls. Although great strides have been made towards understanding protein evolution, a meaningful extrapolation from contemporary proteins to their earliest ancestors is virtually impossible. In an alternative approach, the origin of water-soluble proteins was probed through the synthesis of very large libraries of random amino acid sequences and subsequently subjecting them to in vitro evolution. In combination with computer modeling and simulations, these experiments allow us to address a number of fundamental questions about the origins of proteins. Can functionality emerge from random sequences of proteins? How did the initial repertoire of functional proteins diversify to facilitate new functions? Did this diversification proceed primarily through drawing novel functionalities from random sequences or through evolution of already existing proto-enzymes? Did protein evolution start from a pool of proteins defined by a frozen accident and other collections of proteins could start a different evolutionary pathway? Although we do not have definitive answers to these questions, important clues have been uncovered. Considerable progress has been also achieved in understanding the origins of membrane proteins. We will address this issue in the example of ion channels - proteins that mediate transport of ions across cell walls. Remarkably, despite overall complexity of these proteins in contemporary cells, their structural motifs are quite simple, with -helices being most common. By combining results of experimental and computer simulation studies on synthetic models and simple, natural channels, I will show that, even though architectures of membrane proteins are not nearly as diverse as those of water-soluble proteins, they are sufficiently flexible to adapt readily to the functional demands arising during

  14. Metabolic network analysis of Bacillus clausii on minimal and semirich medium using C-13-Labeled glucose

    DEFF Research Database (Denmark)

    Christiansen, Torben; Christensen, Bjarke; Nielsen, Jens

    2002-01-01

    to increase with increasing specific growth rate but at a much lower level than previously reported for Bacillus subtilis. Two futile cycles in the pyruvate metabolism were included in the metabolic network. A substantial flux in the futile cycle involving malic enzyme was estimated, whereas only a very small...... or zero flux through PEP carboxykinase was estimated, indicating that the latter enzyme was not active during growth on glucose. The uptake of the amino acids in a semirich medium containing 15 of the 20 amino acids normally present in proteins was estimated using fully labeled glucose in batch...

  15. Habitat variability does not generally promote metabolic network modularity in flies and mammals.

    Science.gov (United States)

    Takemoto, Kazuhiro

    2016-01-01

    The evolution of species habitat range is an important topic over a wide range of research fields. In higher organisms, habitat range evolution is generally associated with genetic events such as gene duplication. However, the specific factors that determine habitat variability remain unclear at higher levels of biological organization (e.g., biochemical networks). One widely accepted hypothesis developed from both theoretical and empirical analyses is that habitat variability promotes network modularity; however, this relationship has not yet been directly tested in higher organisms. Therefore, I investigated the relationship between habitat variability and metabolic network modularity using compound and enzymatic networks in flies and mammals. Contrary to expectation, there was no clear positive correlation between habitat variability and network modularity. As an exception, the network modularity increased with habitat variability in the enzymatic networks of flies. However, the observed association was likely an artifact, and the frequency of gene duplication appears to be the main factor contributing to network modularity. These findings raise the question of whether or not there is a general mechanism for habitat range expansion at a higher level (i.e., above the gene scale). This study suggests that the currently widely accepted hypothesis for habitat variability should be reconsidered. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  16. Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks.

    Directory of Open Access Journals (Sweden)

    Sylvain Prigent

    2017-01-01

    Full Text Available Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from

  17. Monitoring and managing metabolic effects of antipsychotics: a cluster randomized trial of an intervention combining evidence-based quality improvement and external facilitation.

    Science.gov (United States)

    Owen, Richard R; Drummond, Karen L; Viverito, Kristen M; Marchant, Kathy; Pope, Sandra K; Smith, Jeffrey L; Landes, Reid D

    2013-10-08

    Treatment of psychotic disorders consists primarily of second generation antipsychotics, which are associated with metabolic side effects such as overweight/obesity, diabetes, and dyslipidemia. Evidence-based clinical practice guidelines recommend timely assessment and management of these conditions; however, research studies show deficits and delays in metabolic monitoring and management for these patients. This protocol article describes the project 'Monitoring and Management for Metabolic Side Effects of Antipsychotics,' which is testing an approach to implement recommendations for these practices. This project employs a cluster randomized clinical trial design to test effectiveness of an evidence-based quality improvement plus facilitation intervention. Eligible study sites were VA Medical Centers with ≥300 patients started on a new antipsychotic prescription in a six-month period. A total of 12 sites, matched in pairs based on scores on an organizational practice survey, were then randomized within pairs to intervention or control conditions.Study participants include VA employees involved in metabolic monitoring and management of patients treated with antipsychotics at participating sites. The intervention involves researchers partnering with clinical stakeholders to facilitate tailoring of local implementation strategies to address barriers to metabolic side-effect monitoring and management. The intervention includes a Design Phase (initial site visit and subsequent development of a local implementation plan); Implementation Phase (guided by an experienced external facilitator); and a Sustainability Phase. Evaluation includes developmental, implementation-focused, progress-focused and interpretative formative evaluation components, as well as summative evaluation. Evaluation methods include surveys, qualitative data collection from provider participants, and quantitative data analysis of data for all patients prescribed a new antipsychotic medication at a

  18. Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network

    Directory of Open Access Journals (Sweden)

    Heavner Benjamin D

    2012-06-01

    Full Text Available Abstract Background Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature. Results Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3. Additional file 1 Function testYeastModel.m.m. Click here for file Additional file 2 Function model

  19. Metabolic networks: a signal-oriented approach to cellular models.

    Science.gov (United States)

    Lengeler, J W

    2000-01-01

    Complete genomes, far advanced proteomes, and even 'metabolomes' are available for at least a few organisms, e.g., Escherichia coli. Systematic functional analyses of such complete data sets will produce a wealth of information and promise an understanding of the dynamics of complex biological networks and perhaps even of entire living organisms. Such complete and holistic descriptions of biological systems, however, will increasingly require a quantitative analysis and the help of mathematical models for simulating whole systems. In particular, new procedures are required that allow a meaningful reduction of the information derived from complex systems that will consequently be used in the modeling process. In this review the biological elements of such a modeling procedure will be described. In a first step, complex living systems must be structured into well-defined and clearly delimited functional units, the elements of which have a common physiological goal, belong to a single genetic unit, and respond to the signals of a signal transduction system that senses changes in physiological states of the organism. These functional units occur at each level of complexity and more complex units originate by grouping several lower level elements into a single, more complex unit. To each complexity level corresponds a global regulator that is epistatic over lower level regulators. After its structuring into modules (functional units), a biological system is converted in a second step into mathematical submodels that by progressive combination can also be assembled into more aggregated model structures. Such a simplification of a cell (an organism) reduces its complexity to a level amenable to present modeling capacities. The universal biochemistry, however, promises a set of rules valid for modeling biological systems, from unicellular microorganisms and cells, to multicellular organisms and to populations.

  20. Insulin Biosynthetic Interaction Network Component, TMEM24, Facilitates Insulin Reserve Pool Release

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    Anita Pottekat

    2013-09-01

    Full Text Available Insulin homeostasis in pancreatic β cells is now recognized as a critical element in the progression of obesity and type II diabetes (T2D. Proteins that interact with insulin to direct its sequential synthesis, folding, trafficking, and packaging into reserve granules in order to manage release in response to elevated glucose remain largely unknown. Using a conformation-based approach combined with mass spectrometry, we have generated the insulin biosynthetic interaction network (insulin BIN, a proteomic roadmap in the β cell that describes the sequential interacting partners of insulin along the secretory axis. The insulin BIN revealed an abundant C2 domain-containing transmembrane protein 24 (TMEM24 that manages glucose-stimulated insulin secretion from a reserve pool of granules, a critical event impaired in patients with T2D. The identification of TMEM24 in the context of a comprehensive set of sequential insulin-binding partners provides a molecular description of the insulin secretory pathway in β cells.

  1. Comparative genomic reconstruction of transcriptional networks controlling central metabolism in the Shewanella genus

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    Kovaleva Galina

    2011-06-01

    Full Text Available Abstract Background Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. The Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria. Results To explore conservation and variations in the Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. Multiple variations in regulatory strategies between the Shewanella spp. and E. coli include regulon contraction and expansion (as in the case of PdhR, HexR, FadR, numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. PsrA for fatty acid degradation and, conversely, orthologous regulators to control distinct pathways (e.g. TyrR, ArgR, Crp. Conclusions We tentatively defined the first reference collection of ~100 transcriptional regulons in 16 Shewanella genomes. The resulting regulatory network contains ~600 regulated genes per genome that are mostly involved in metabolism of carbohydrates, amino acids, fatty acids, vitamins, metals, and stress responses. Several reconstructed regulons including NagR for N-acetylglucosamine catabolism were experimentally validated in S

  2. Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism.

    Science.gov (United States)

    Lavrova, Anastasia I; Postnikov, Eugene B; Zyubin, Andrey Yu; Babak, Svetlana V

    2017-04-01

    We consider two approaches to modelling the cell metabolism of 6-mercaptopurine, one of the important chemotherapy drugs used for treating acute lymphocytic leukaemia: kinetic ordinary differential equations, and Boolean networks supplied with one controlling node, which takes continual values. We analyse their interplay with respect to taking into account ATP concentration as a key parameter of switching between different pathways. It is shown that the Boolean networks, which allow avoiding the complexity of general kinetic modelling, preserve the possibility of reproducing the principal switching mechanism.

  3. Integration of expression data in genome-scale metabolic network reconstructions

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    Anna S. Blazier

    2012-08-01

    Full Text Available With the advent of high-throughput technologies, the field of systems biology has amassed an abundance of omics data, quantifying thousands of cellular components across a variety of scales, ranging from mRNA transcript levels to metabolite quantities. Methods are needed to not only integrate this omics data but to also use this data to heighten the predictive capabilities of computational models. Several recent studies have successfully demonstrated how flux balance analysis (FBA, a constraint-based modeling approach, can be used to integrate transcriptomic data into genome-scale metabolic network reconstructions to generate predictive computational models. In this review, we summarize such FBA-based methods for integrating expression data into genome-scale metabolic network reconstructions, highlighting their advantages as well as their limitations.

  4. A metabolic network approach for the identification and prioritization of antimicrobial drug targets.

    Science.gov (United States)

    Chavali, Arvind K; D'Auria, Kevin M; Hewlett, Erik L; Pearson, Richard D; Papin, Jason A

    2012-03-01

    For many infectious diseases, novel treatment options are needed in order to address problems with cost, toxicity and resistance to current drugs. Systems biology tools can be used to gain valuable insight into pathogenic processes and aid in expediting drug discovery. In the past decade, constraint-based modeling of genome-scale metabolic networks has become widely used. Focusing on pathogen metabolic networks, we review in silico strategies used to identify effective drug targets and highlight recent successes as well as limitations associated with such computational analyses. We further discuss how accounting for the host environment and even targeting the host may offer new therapeutic options. These systems-level approaches are beginning to provide novel avenues for drug targeting against infectious agents. Copyright © 2011 Elsevier Ltd. All rights reserved.

  5. In Silico Genome-Scale Reconstruction and Validation of the Corynebacterium glutamicum Metabolic Network

    DEFF Research Database (Denmark)

    Kjeldsen, Kjeld Raunkjær; Nielsen, J.

    2009-01-01

    A genome-scale metabolic model of the Gram-positive bacteria Corynebacterium glutamicum ATCC 13032 was constructed comprising 446 reactions and 411 metabolite, based on the annotated genome and available biochemical information. The network was analyzed using constraint based methods. The model w...... and lactate. Comparable flux values between in silico model and experimental values were seen, although some differences in the phenotypic behavior between the model and the experimental data were observed,...

  6. Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii.

    Science.gov (United States)

    Gargouri, Mahmoud; Park, Jeong-Jin; Holguin, F Omar; Kim, Min-Jeong; Wang, Hongxia; Deshpande, Rahul R; Shachar-Hill, Yair; Hicks, Leslie M; Gang, David R

    2015-08-01

    Microalgae-based biofuels are promising sources of alternative energy, but improvements throughout the production process are required to establish them as economically feasible. One of the most influential improvements would be a significant increase in lipid yields, which could be achieved by altering the regulation of lipid biosynthesis and accumulation. Chlamydomonas reinhardtii accumulates oil (triacylglycerols, TAG) in response to nitrogen (N) deprivation. Although a few important regulatory genes have been identified that are involved in controlling this process, a global understanding of the larger regulatory network has not been developed. In order to uncover this network in this species, a combined omics (transcriptomic, proteomic and metabolomic) analysis was applied to cells grown in a time course experiment after a shift from N-replete to N-depleted conditions. Changes in transcript and protein levels of 414 predicted transcription factors (TFs) and transcriptional regulators (TRs) were monitored relative to other genes. The TF and TR genes were thus classified by two separate measures: up-regulated versus down-regulated and early response versus late response relative to two phases of polar lipid synthesis (before and after TAG biosynthesis initiation). Lipidomic and primary metabolite profiling generated compound accumulation levels that were integrated with the transcript dataset and TF profiling to produce a transcriptional regulatory network. Evaluation of this proposed regulatory network led to the identification of several regulatory hubs that control many aspects of cellular metabolism, from N assimilation and metabolism, to central metabolism, photosynthesis and lipid metabolism. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.

  7. PPAR? population shift produces disease-related changes in molecular networks associated with metabolic syndrome

    OpenAIRE

    Jurkowski, W; Roomp, K; Crespo, I; Schneider, J G; del Sol, A

    2011-01-01

    Peroxisome proliferator-activated receptor gamma (PPARγ) is a key regulator of adipocyte differentiation and has an important role in metabolic syndrome. Phosphorylation of the receptor's ligand-binding domain at serine 273 has been shown to change the expression of a large number of genes implicated in obesity. The difference in gene expression seen when comparing wild-type phosphorylated with mutant non-phosphorylated PPARγ may have important consequences for the cellular molecular network,...

  8. Metabolic Networks and Metabolites Underlie Associations Between Maternal Glucose During Pregnancy and Newborn Size at Birth

    OpenAIRE

    Scholtens, Denise M.; Bain, James R.; Reisetter, Anna C.; Muehlbauer, Michael J.; Nodzenski, Michael; Stevens, Robert D.; Ilkayeva, Olga; Lowe, Lynn P.; Metzger, Boyd E.; Newgard, Christopher B.; Lowe, William L.

    2016-01-01

    Maternal metabolites and metabolic networks underlying associations between maternal glucose during pregnancy and newborn birth weight and adiposity demand fuller characterization. We performed targeted and nontargeted gas chromatography/mass spectrometry metabolomics on maternal serum collected at fasting and 1 h following glucose beverage consumption during an oral glucose tolerance test (OGTT) for 400 northern European mothers at ?28 weeks' gestation in the Hyperglycemia and Adverse Pregna...

  9. Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes

    Science.gov (United States)

    De Martino, Daniele

    2017-12-01

    In this work maximum entropy distributions in the space of steady states of metabolic networks are considered upon constraining the first and second moments of the growth rate. Coexistence of fast and slow phenotypes, with bimodal flux distributions, emerges upon considering control on the average growth (optimization) and its fluctuations (heterogeneity). This is applied to the carbon catabolic core of Escherichia coli where it quantifies the metabolic activity of slow growing phenotypes and it provides a quantitative map with metabolic fluxes, opening the possibility to detect coexistence from flux data. A preliminary analysis on data for E. coli cultures in standard conditions shows degeneracy for the inferred parameters that extend in the coexistence region.

  10. Metabolic network analysis-based identification of antimicrobial drug targets in category A bioterrorism agents.

    Directory of Open Access Journals (Sweden)

    Yong-Yeol Ahn

    Full Text Available The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nucleic acid pathways common to all strains across the three distinct genera were identified as targets. Antimicrobial agents against some of these enzymes are available. Thus, a combination of cross species-specific antibiotics and common antimicrobials against shared targets may represent a useful combinatorial therapeutic approach against all Category A bioterrorism agents.

  11. Micropatterning Facilitates the Long-Term Growth and Analysis of iPSC-Derived Individual Human Neurons and Neuronal Networks.

    Science.gov (United States)

    Burbulla, Lena F; Beaumont, Kristin G; Mrksich, Milan; Krainc, Dimitri

    2016-08-01

    The discovery of induced pluripotent stem cells (iPSCs) and their application to patient-specific disease models offers new opportunities for studying the pathophysiology of neurological disorders. However, current methods for culturing iPSC-derived neuronal cells result in clustering of neurons, which precludes the analysis of individual neurons and defined neuronal networks. To address this challenge, cultures of human neurons on micropatterned surfaces are developed that promote neuronal survival over extended periods of time. This approach facilitates studies of neuronal development, cellular trafficking, and related mechanisms that require assessment of individual neurons and specific network connections. Importantly, micropatterns support the long-term stability of cultured neurons, which enables time-dependent analysis of cellular processes in living neurons. The approach described in this paper allows mechanistic studies of human neurons, both in terms of normal neuronal development and function, as well as time-dependent pathological processes, and provides a platform for testing of new therapeutics in neuropsychiatric disorders. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Multiobjective flux balancing using the NISE method for metabolic network analysis.

    Science.gov (United States)

    Oh, Young-Gyun; Lee, Dong-Yup; Lee, Sang Yup; Park, Sunwon

    2009-01-01

    Flux balance analysis (FBA) is well acknowledged as an analysis tool of metabolic networks in the framework of metabolic engineering. However, FBA has a limitation for solving a multiobjective optimization problem which considers multiple conflicting objectives. In this study, we propose a novel multiobjective flux balance analysis method, which adapts the noninferior set estimation (NISE) method (Solanki et al., 1993) for multiobjective linear programming (MOLP) problems. NISE method can generate an approximation of the Pareto curve for conflicting objectives without redundant iterations of single objective optimization. Furthermore, the flux distributions at each Pareto optimal solution can be obtained for understanding the internal flux changes in the metabolic network. The functionality of this approach is shown by applying it to a genome-scale in silico model of E. coli. Multiple objectives for the poly(3-hydroxybutyrate) [P(3HB)] production are considered simultaneously, and relationships among them are identified. The Pareto curve for maximizing succinic acid production vs. maximizing biomass production is used for the in silico analysis of various combinatorial knockout strains. This proposed method accelerates the strain improvement in the metabolic engineering by reducing computation time of obtaining the Pareto curve and analysis time of flux distribution at each Pareto optimal solution. (c) 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009.

  13. Metabolic network rewiring of propionate flux compensates vitamin B12 deficiency in C. elegans

    Science.gov (United States)

    Watson, Emma; Olin-Sandoval, Viridiana; Hoy, Michael J; Li, Chi-Hua; Louisse, Timo; Yao, Victoria; Mori, Akihiro; Holdorf, Amy D; Troyanskaya, Olga G; Ralser, Markus; Walhout, Albertha JM

    2016-01-01

    Metabolic network rewiring is the rerouting of metabolism through the use of alternate enzymes to adjust pathway flux and accomplish specific anabolic or catabolic objectives. Here, we report the first characterization of two parallel pathways for the breakdown of the short chain fatty acid propionate in Caenorhabditis elegans. Using genetic interaction mapping, gene co-expression analysis, pathway intermediate quantification and carbon tracing, we uncover a vitamin B12-independent propionate breakdown shunt that is transcriptionally activated on vitamin B12 deficient diets, or under genetic conditions mimicking the human diseases propionic- and methylmalonic acidemia, in which the canonical B12-dependent propionate breakdown pathway is blocked. Our study presents the first example of transcriptional vitamin-directed metabolic network rewiring to promote survival under vitamin deficiency. The ability to reroute propionate breakdown according to B12 availability may provide C. elegans with metabolic plasticity and thus a selective advantage on different diets in the wild. DOI: http://dx.doi.org/10.7554/eLife.17670.001 PMID:27383050

  14. Coevolution Trumps Pleiotropy: Carbon Assimilation Traits Are Independent of Metabolic Network Structure in Budding Yeast

    Science.gov (United States)

    Opulente, Dana A.; Morales, Christopher M.; Carey, Lucas B.; Rest, Joshua S.

    2013-01-01

    Phenotypic traits may be gained and lost together because of pleiotropy, the involvement of common genes and networks, or because of simultaneous selection for multiple traits across environments (multiple-trait coevolution). However, the extent to which network pleiotropy versus environmental coevolution shapes shared responses has not been addressed. To test these alternatives, we took advantage of the fact that the genus Saccharomyces has variation in habitat usage and diversity in the carbon sources that a given strain can metabolize. We examined patterns of gain and loss in carbon utilization traits across 488 strains of Saccharomyces to investigate whether the structure of metabolic pathways or selection pressure from common environments may have caused carbon utilization traits to be gained and lost together. While most carbon sources were gained and lost independently of each other, we found four clusters that exhibit non-random patterns of gain and loss across strains. Contrary to the network pleiotropy hypothesis, we did not find that these patterns are explained by the structure of metabolic pathways or shared enzymes. Consistent with the hypothesis that common environments shape suites of phenotypes, we found that the environment a strain was isolated from partially predicts the carbon sources it can assimilate. PMID:23326606

  15. Integration of Posttranscriptional Gene Networks into Metabolic Adaptation and Biofilm Maturation in Candida albicans

    Science.gov (United States)

    Harrison, Paul F.; Lo, Tricia L.; Quenault, Tara; Dagley, Michael J.; Bellousoff, Matthew; Powell, David R.; Beilharz, Traude H.; Traven, Ana

    2015-01-01

    The yeast Candida albicans is a human commensal and opportunistic pathogen. Although both commensalism and pathogenesis depend on metabolic adaptation, the regulatory pathways that mediate metabolic processes in C. albicans are incompletely defined. For example, metabolic change is a major feature that distinguishes community growth of C. albicans in biofilms compared to suspension cultures, but how metabolic adaptation is functionally interfaced with the structural and gene regulatory changes that drive biofilm maturation remains to be fully understood. We show here that the RNA binding protein Puf3 regulates a posttranscriptional mRNA network in C. albicans that impacts on mitochondrial biogenesis, and provide the first functional data suggesting evolutionary rewiring of posttranscriptional gene regulation between the model yeast Saccharomyces cerevisiae and C. albicans. A proportion of the Puf3 mRNA network is differentially expressed in biofilms, and by using a mutant in the mRNA deadenylase CCR4 (the enzyme recruited to mRNAs by Puf3 to control transcript stability) we show that posttranscriptional regulation is important for mitochondrial regulation in biofilms. Inactivation of CCR4 or dis-regulation of mitochondrial activity led to altered biofilm structure and over-production of extracellular matrix material. The extracellular matrix is critical for antifungal resistance and immune evasion, and yet of all biofilm maturation pathways extracellular matrix biogenesis is the least understood. We propose a model in which the hypoxic biofilm environment is sensed by regulators such as Ccr4 to orchestrate metabolic adaptation, as well as the regulation of extracellular matrix production by impacting on the expression of matrix-related cell wall genes. Therefore metabolic changes in biofilms might be intimately linked to a key biofilm maturation mechanism that ultimately results in untreatable fungal disease. PMID:26474309

  16. Integration of Posttranscriptional Gene Networks into Metabolic Adaptation and Biofilm Maturation in Candida albicans.

    Directory of Open Access Journals (Sweden)

    Jiyoti Verma-Gaur

    2015-10-01

    Full Text Available The yeast Candida albicans is a human commensal and opportunistic pathogen. Although both commensalism and pathogenesis depend on metabolic adaptation, the regulatory pathways that mediate metabolic processes in C. albicans are incompletely defined. For example, metabolic change is a major feature that distinguishes community growth of C. albicans in biofilms compared to suspension cultures, but how metabolic adaptation is functionally interfaced with the structural and gene regulatory changes that drive biofilm maturation remains to be fully understood. We show here that the RNA binding protein Puf3 regulates a posttranscriptional mRNA network in C. albicans that impacts on mitochondrial biogenesis, and provide the first functional data suggesting evolutionary rewiring of posttranscriptional gene regulation between the model yeast Saccharomyces cerevisiae and C. albicans. A proportion of the Puf3 mRNA network is differentially expressed in biofilms, and by using a mutant in the mRNA deadenylase CCR4 (the enzyme recruited to mRNAs by Puf3 to control transcript stability we show that posttranscriptional regulation is important for mitochondrial regulation in biofilms. Inactivation of CCR4 or dis-regulation of mitochondrial activity led to altered biofilm structure and over-production of extracellular matrix material. The extracellular matrix is critical for antifungal resistance and immune evasion, and yet of all biofilm maturation pathways extracellular matrix biogenesis is the least understood. We propose a model in which the hypoxic biofilm environment is sensed by regulators such as Ccr4 to orchestrate metabolic adaptation, as well as the regulation of extracellular matrix production by impacting on the expression of matrix-related cell wall genes. Therefore metabolic changes in biofilms might be intimately linked to a key biofilm maturation mechanism that ultimately results in untreatable fungal disease.

  17. Characterizing steady states of genome-scale metabolic networks in continuous cell cultures.

    Science.gov (United States)

    Fernandez-de-Cossio-Diaz, Jorge; Leon, Kalet; Mulet, Roberto

    2017-11-01

    In the continuous mode of cell culture, a constant flow carrying fresh media replaces culture fluid, cells, nutrients and secreted metabolites. Here we present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. We derive a number of consequences from the model that are independent of parameter values. The ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties. This conclusion is robust even in the presence of multi-stability, which is explained in our model by a negative feedback loop due to toxic byproduct accumulation. A complex landscape of steady states emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced.

  18. An interactive web tool for facilitating shared decision-making in dementia-care networks: a field study

    Directory of Open Access Journals (Sweden)

    Marijke eSpan

    2015-07-01

    Full Text Available BackgroundAn interactive web tool has been developed for facilitating shared decision-making in dementia-care networks. The DecideGuide provides a chat function for easier communication between network members, a deciding together function for step-by-step decision-making, and an individual opinion function for eight dementia-related life domains. The aim of this study was to gain insight in the user friendliness of the DecideGuide, user acceptance and satisfaction, and participants’ opinion of the DecideGuide for making decisions.Materials and methodsA 5-month field study included four dementia-care networks (19 participants in total. The data derived from structured interviews, observations, and information that participants logged in the DecideGuide. Structured interviews took place at the start, middle, and end of the field study with people with dementia, informal caregivers, and case managers. Results1. The user friendliness of the chat and individual opinion functions was adequate for case managers and most informal caregivers. Older participants, with or without dementia, had some difficulties using a tablet and the DecideGuide. The deciding together function does not yet provide adequate instructions for all. The user interface needs simplification. 2. User acceptance and satisfaction: everybody liked the chat’s easy communication, handling difficult issues for discussion, and the option of individual opinions. 3. The DecideGuide helped participants structure their thoughts. They felt more involved and shared more information about daily issues than before. ConclusionParticipants found the DecideGuide valuable in decision-making. The chat function seems powerful in helping members engage with one another constructively. Such engagement is a prerequisite for making shared decisions. Regardless of participants’ use of the tool, they saw the DecideGuide's added value.

  19. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness.

    Science.gov (United States)

    Chennu, Srivas; Annen, Jitka; Wannez, Sarah; Thibaut, Aurore; Chatelle, Camille; Cassol, Helena; Martens, Géraldine; Schnakers, Caroline; Gosseries, Olivia; Menon, David; Laureys, Steven

    2017-08-01

    Recent advances in functional neuroimaging have demonstrated novel potential for informing diagnosis and prognosis in the unresponsive wakeful syndrome and minimally conscious states. However, these technologies come with considerable expense and difficulty, limiting the possibility of wider clinical application in patients. Here, we show that high density electroencephalography, collected from 104 patients measured at rest, can provide valuable information about brain connectivity that correlates with behaviour and functional neuroimaging. Using graph theory, we visualize and quantify spectral connectivity estimated from electroencephalography as a dense brain network. Our findings demonstrate that key quantitative metrics of these networks correlate with the continuum of behavioural recovery in patients, ranging from those diagnosed as unresponsive, through those who have emerged from minimally conscious, to the fully conscious locked-in syndrome. In particular, a network metric indexing the presence of densely interconnected central hubs of connectivity discriminated behavioural consciousness with accuracy comparable to that achieved by expert assessment with positron emission tomography. We also show that this metric correlates strongly with brain metabolism. Further, with classification analysis, we predict the behavioural diagnosis, brain metabolism and 1-year clinical outcome of individual patients. Finally, we demonstrate that assessments of brain networks show robust connectivity in patients diagnosed as unresponsive by clinical consensus, but later rediagnosed as minimally conscious with the Coma Recovery Scale-Revised. Classification analysis of their brain network identified each of these misdiagnosed patients as minimally conscious, corroborating their behavioural diagnoses. If deployed at the bedside in the clinical context, such network measurements could complement systematic behavioural assessment and help reduce the high misdiagnosis rate reported

  20. Cerebral energy metabolism and the brain's functional network architecture: an integrative review

    Science.gov (United States)

    Lord, Louis-David; Expert, Paul; Huckins, Jeremy F; Turkheimer, Federico E

    2013-01-01

    Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain's ‘functional connectivity' is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks. PMID:23756687

  1. Kmt5a Controls Hepatic Metabolic Pathways by Facilitating RNA Pol II Release from Promoter-Proximal Regions.

    Science.gov (United States)

    Nikolaou, Kostas C; Moulos, Panagiotis; Harokopos, Vangelis; Chalepakis, George; Talianidis, Iannis

    2017-07-25

    H4K20 monomethylation maintains genome integrity by regulating proper mitotic condensation, DNA damage response, and replication licensing. Here, we show that, in non-dividing hepatic cells, H4K20Me1 is specifically enriched in active gene bodies and dynamically regulated by the antagonistic action of Kmt5a methylase and Kdm7b demethylase. In liver-specific Kmt5a-deficient mice, reduced levels of H4K20Me 1 correlated with reduced RNA Pol II release from promoter-proximal regions. Genes regulating glucose and fatty acid metabolism were most sensitive to impairment of RNA Pol II release. Downregulation of glycolytic genes resulted in an energy starvation condition partially compensated by AMP-activated protein kinase (AMPK) activation and increased mitochondrial activity. This metabolic reprogramming generated a highly sensitized state that, upon different metabolic stress conditions, quickly aggravated into a senescent phenotype due to ROS overproduction-mediated oxidative DNA damage. The results illustrate how defects in the general process of RNA Pol II transition into a productive elongation phase can trigger specific metabolic changes and genome instability. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  2. Kmt5a Controls Hepatic Metabolic Pathways by Facilitating RNA Pol II Release from Promoter-Proximal Regions

    Directory of Open Access Journals (Sweden)

    Kostas C. Nikolaou

    2017-07-01

    Full Text Available H4K20 monomethylation maintains genome integrity by regulating proper mitotic condensation, DNA damage response, and replication licensing. Here, we show that, in non-dividing hepatic cells, H4K20Me1 is specifically enriched in active gene bodies and dynamically regulated by the antagonistic action of Kmt5a methylase and Kdm7b demethylase. In liver-specific Kmt5a-deficient mice, reduced levels of H4K20Me1 correlated with reduced RNA Pol II release from promoter-proximal regions. Genes regulating glucose and fatty acid metabolism were most sensitive to impairment of RNA Pol II release. Downregulation of glycolytic genes resulted in an energy starvation condition partially compensated by AMP-activated protein kinase (AMPK activation and increased mitochondrial activity. This metabolic reprogramming generated a highly sensitized state that, upon different metabolic stress conditions, quickly aggravated into a senescent phenotype due to ROS overproduction-mediated oxidative DNA damage. The results illustrate how defects in the general process of RNA Pol II transition into a productive elongation phase can trigger specific metabolic changes and genome instability.

  3. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    Science.gov (United States)

    Biggs, Matthew B; Papin, Jason A

    2017-03-01

    Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  4. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    Directory of Open Access Journals (Sweden)

    Matthew B Biggs

    2017-03-01

    Full Text Available Genome-scale metabolic network reconstructions (GENREs are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA. We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  5. A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics

    NARCIS (Netherlands)

    Nikerel, I.E.; Van Winden, W.; Van Gulik, W.M.; Heijnen, J.J.

    2006-01-01

    Background: Dynamic modeling of metabolic reaction networks under in vivo conditions is a crucial step in order to obtain a better understanding of the (dis)functioning of living cells. So far dynamic metabolic models generally have been based on mechanistic rate equations which often contain so

  6. BoostGAPFILL: improving the fidelity of metabolic network reconstructions through integrated constraint and pattern-based methods.

    Science.gov (United States)

    Oyetunde, Tolutola; Zhang, Muhan; Chen, Yixin; Tang, Yinjie; Lo, Cynthia

    2017-02-15

    Metabolic network reconstructions are often incomplete. Constraint-based and pattern-based methodologies have been used for automated gap filling of these networks, each with its own strengths and weaknesses. Moreover, since validation of hypotheses made by gap filling tools require experimentation, it is challenging to benchmark performance and make improvements other than that related to speed and scalability. We present BoostGAPFILL, an open source tool that leverages both constraint-based and machine learning methodologies for hypotheses generation in gap filling and metabolic model refinement. BoostGAPFILL uses metabolite patterns in the incomplete network captured using a matrix factorization formulation to constrain the set of reactions used to fill gaps in a metabolic network. We formulate a testing framework based on the available metabolic reconstructions and demonstrate the superiority of BoostGAPFILL to state-of-the-art gap filling tools. We randomly delete a number of reactions from a metabolic network and rate the different algorithms on their ability to both predict the deleted reactions from a universal set and to fill gaps. For most metabolic network reconstructions tested, BoostGAPFILL shows above 60% precision and recall, which is more than twice that of other existing tools. MATLAB open source implementation ( https://github.com/Tolutola/BoostGAPFILL ). toyetunde@wustl.edu or muhan@wustl.edu . Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  7. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology

    OpenAIRE

    Bordbar, Aarash; Feist, Adam M; Usaite-Black, Renata; Woodcock, Joseph; Palsson, Bernhard O; Famili, Iman

    2011-01-01

    Abstract Background Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown ut...

  8. Genome-scale reconstruction and analysis of the metabolic network in the hyperthermophilic archaeon Sulfolobus solfataricus.

    Directory of Open Access Journals (Sweden)

    Thomas Ulas

    Full Text Available We describe the reconstruction of a genome-scale metabolic model of the crenarchaeon Sulfolobus solfataricus, a hyperthermoacidophilic microorganism. It grows in terrestrial volcanic hot springs with growth occurring at pH 2-4 (optimum 3.5 and a temperature of 75-80°C (optimum 80°C. The genome of Sulfolobus solfataricus P2 contains 2,992,245 bp on a single circular chromosome and encodes 2,977 proteins and a number of RNAs. The network comprises 718 metabolic and 58 transport/exchange reactions and 705 unique metabolites, based on the annotated genome and available biochemical data. Using the model in conjunction with constraint-based methods, we simulated the metabolic fluxes induced by different environmental and genetic conditions. The predictions were compared to experimental measurements and phenotypes of S. solfataricus. Furthermore, the performance of the network for 35 different carbon sources known for S. solfataricus from the literature was simulated. Comparing the growth on different carbon sources revealed that glycerol is the carbon source with the highest biomass flux per imported carbon atom (75% higher than glucose. Experimental data was also used to fit the model to phenotypic observations. In addition to the commonly known heterotrophic growth of S. solfataricus, the crenarchaeon is also able to grow autotrophically using the hydroxypropionate-hydroxybutyrate cycle for bicarbonate fixation. We integrated this pathway into our model and compared bicarbonate fixation with growth on glucose as sole carbon source. Finally, we tested the robustness of the metabolism with respect to gene deletions using the method of Minimization of Metabolic Adjustment (MOMA, which predicted that 18% of all possible single gene deletions would be lethal for the organism.

  9. Polysaccharides utilization in human gut bacterium Bacteroides thetaiotaomicron: comparative genomics reconstruction of metabolic and regulatory networks.

    Science.gov (United States)

    Ravcheev, Dmitry A; Godzik, Adam; Osterman, Andrei L; Rodionov, Dmitry A

    2013-12-12

    Bacteroides thetaiotaomicron, a predominant member of the human gut microbiota, is characterized by its ability to utilize a wide variety of polysaccharides using the extensive saccharolytic machinery that is controlled by an expanded repertoire of transcription factors (TFs). The availability of genomic sequences for multiple Bacteroides species opens an opportunity for their comparative analysis to enable characterization of their metabolic and regulatory networks. A comparative genomics approach was applied for the reconstruction and functional annotation of the carbohydrate utilization regulatory networks in 11 Bacteroides genomes. Bioinformatics analysis of promoter regions revealed putative DNA-binding motifs and regulons for 31 orthologous TFs in the Bacteroides. Among the analyzed TFs there are 4 SusR-like regulators, 16 AraC-like hybrid two-component systems (HTCSs), and 11 regulators from other families. Novel DNA motifs of HTCSs and SusR-like regulators in the Bacteroides have the common structure of direct repeats with a long spacer between two conserved sites. The inferred regulatory network in B. thetaiotaomicron contains 308 genes encoding polysaccharide and sugar catabolic enzymes, carbohydrate-binding and transport systems, and TFs. The analyzed TFs control pathways for utilization of host and dietary glycans to monosaccharides and their further interconversions to intermediates of the central metabolism. The reconstructed regulatory network allowed us to suggest and refine specific functional assignments for sugar catabolic enzymes and transporters, providing a substantial improvement to the existing metabolic models for B. thetaiotaomicron. The obtained collection of reconstructed TF regulons is available in the RegPrecise database (http://regprecise.lbl.gov).

  10. EnzDP: improved enzyme annotation for metabolic network reconstruction based on domain composition profiles.

    Science.gov (United States)

    Nguyen, Nam-Ninh; Srihari, Sriganesh; Leong, Hon Wai; Chong, Ket-Fah

    2015-10-01

    Determining the entire complement of enzymes and their enzymatic functions is a fundamental step for reconstructing the metabolic network of cells. High quality enzyme annotation helps in enhancing metabolic networks reconstructed from the genome, especially by reducing gaps and increasing the enzyme coverage. Currently, structure-based and network-based approaches can only cover a limited number of enzyme families, and the accuracy of homology-based approaches can be further improved. Bottom-up homology-based approach improves the coverage by rebuilding Hidden Markov Model (HMM) profiles for all known enzymes. However, its clustering procedure relies firmly on BLAST similarity score, ignoring protein domains/patterns, and is sensitive to changes in cut-off thresholds. Here, we use functional domain architecture to score the association between domain families and enzyme families (Domain-Enzyme Association Scoring, DEAS). The DEAS score is used to calculate the similarity between proteins, which is then used in clustering procedure, instead of using sequence similarity score. We improve the enzyme annotation protocol using a stringent classification procedure, and by choosing optimal threshold settings and checking for active sites. Our analysis shows that our stringent protocol EnzDP can cover up to 90% of enzyme families available in Swiss-Prot. It achieves a high accuracy of 94.5% based on five-fold cross-validation. EnzDP outperforms existing methods across several testing scenarios. Thus, EnzDP serves as a reliable automated tool for enzyme annotation and metabolic network reconstruction. Available at: www.comp.nus.edu.sg/~nguyennn/EnzDP .

  11. Novel technologies combined with traditional metabolic engineering strategies facilitate the construction of shikimate-producing Escherichia coli.

    Science.gov (United States)

    Gu, Pengfei; Fan, Xiangyu; Liang, Quanfeng; Qi, Qingsheng; Li, Qiang

    2017-09-29

    Shikimate is an important intermediate in the aromatic amino acid pathway, which can be used as a promising building block for the synthesis of biological compounds, such as neuraminidase inhibitor Oseltamivir (Tamiflu ® ). Compared with traditional methods, microbial production of shikimate has the advantages of environmental friendliness, low cost, feed stock renewability, and product selectivity and diversity, thus receiving more and more attentions. The development of metabolic engineering allows for high-efficiency production of shikimate of Escherichia coli by improving the intracellular level of precursors, blocking downstream pathway, releasing negative regulation factors, and overexpressing rate-limiting enzymes. In addition, novel technologies derived from systems and synthetic biology have opened a new avenue towards construction of shikimate-producing strains. This review summarized successful and applicable strategies derived from traditional metabolic engineering and novel technologies for increasing accumulation of shikimate in E. coli.

  12. Transcriptomic coordination in the human metabolic network reveals links between n-3 fat intake, adipose tissue gene expression and metabolic health.

    Science.gov (United States)

    Morine, Melissa J; Tierney, Audrey C; van Ommen, Ben; Daniel, Hannelore; Toomey, Sinead; Gjelstad, Ingrid M F; Gormley, Isobel C; Pérez-Martinez, Pablo; Drevon, Christian A; López-Miranda, Jose; Roche, Helen M

    2011-11-01

    Understanding the molecular link between diet and health is a key goal in nutritional systems biology. As an alternative to pathway analysis, we have developed a joint multivariate and network-based approach to analysis of a dataset of habitual dietary records, adipose tissue transcriptomics and comprehensive plasma marker profiles from human volunteers with the Metabolic Syndrome. With this approach we identified prominent co-expressed sub-networks in the global metabolic network, which showed correlated expression with habitual n-3 PUFA intake and urinary levels of the oxidative stress marker 8-iso-PGF(2α). These sub-networks illustrated inherent cross-talk between distinct metabolic pathways, such as between triglyceride metabolism and production of lipid signalling molecules. In a parallel promoter analysis, we identified several adipogenic transcription factors as potential transcriptional regulators associated with habitual n-3 PUFA intake. Our results illustrate advantages of network-based analysis, and generate novel hypotheses on the transcriptomic link between habitual n-3 PUFA intake, adipose tissue function and oxidative stress.

  13. Metabolic Networks and Metabolites Underlie Associations Between Maternal Glucose During Pregnancy and Newborn Size at Birth.

    Science.gov (United States)

    Scholtens, Denise M; Bain, James R; Reisetter, Anna C; Muehlbauer, Michael J; Nodzenski, Michael; Stevens, Robert D; Ilkayeva, Olga; Lowe, Lynn P; Metzger, Boyd E; Newgard, Christopher B; Lowe, William L

    2016-07-01

    Maternal metabolites and metabolic networks underlying associations between maternal glucose during pregnancy and newborn birth weight and adiposity demand fuller characterization. We performed targeted and nontargeted gas chromatography/mass spectrometry metabolomics on maternal serum collected at fasting and 1 h following glucose beverage consumption during an oral glucose tolerance test (OGTT) for 400 northern European mothers at ∼28 weeks' gestation in the Hyperglycemia and Adverse Pregnancy Outcome Study. Amino acids, fatty acids, acylcarnitines, and products of lipid metabolism decreased and triglycerides increased during the OGTT. Analyses of individual metabolites indicated limited maternal glucose associations at fasting, but broader associations, including amino acids, fatty acids, carbohydrates, and lipids, were found at 1 h. Network analyses modeling metabolite correlations provided context for individual metabolite associations and elucidated collective associations of multiple classes of metabolic fuels with newborn size and adiposity, including acylcarnitines, fatty acids, carbohydrates, and organic acids. Random forest analyses indicated an improved ability to predict newborn size outcomes by using maternal metabolomics data beyond traditional risk factors, including maternal glucose. Broad-scale association of fuel metabolites with maternal glucose is evident during pregnancy, with unique maternal metabolites potentially contributing specifically to newborn birth weight and adiposity. © 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

  14. PPARγ population shift produces disease-related changes in molecular networks associated with metabolic syndrome.

    Science.gov (United States)

    Jurkowski, W; Roomp, K; Crespo, I; Schneider, J G; Del Sol, A

    2011-08-11

    Peroxisome proliferator-activated receptor gamma (PPARγ) is a key regulator of adipocyte differentiation and has an important role in metabolic syndrome. Phosphorylation of the receptor's ligand-binding domain at serine 273 has been shown to change the expression of a large number of genes implicated in obesity. The difference in gene expression seen when comparing wild-type phosphorylated with mutant non-phosphorylated PPARγ may have important consequences for the cellular molecular network, the state of which can be shifted from the healthy to a stable diseased state. We found that a group of differentially expressed genes are involved in bi-stable switches and form a core network, the state of which changes with disease progression. These findings support the idea that bi-stable switches may be a mechanism for locking the core gene network into a diseased state and for efficiently propagating perturbations to more distant regions of the network. A structural analysis of the PPARγ-RXRα dimer complex supports the hypothesis of a major structural change between the two states, and this may represent an important mechanism leading to the differential expression observed in the core network.

  15. Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding.

    Directory of Open Access Journals (Sweden)

    Daniele De Martino

    Full Text Available The uniform sampling of convex polytopes is an interesting computational problem with many applications in inference from linear constraints, but the performances of sampling algorithms can be affected by ill-conditioning. This is the case of inferring the feasible steady states in models of metabolic networks, since they can show heterogeneous time scales. In this work we focus on rounding procedures based on building an ellipsoid that closely matches the sampling space, that can be used to define an efficient hit-and-run (HR Markov Chain Monte Carlo. In this way the uniformity of the sampling of the convex space of interest is rigorously guaranteed, at odds with non markovian methods. We analyze and compare three rounding methods in order to sample the feasible steady states of metabolic networks of three models of growing size up to genomic scale. The first is based on principal component analysis (PCA, the second on linear programming (LP and finally we employ the Lovazs ellipsoid method (LEM. Our results show that a rounding procedure dramatically improves the performances of the HR in these inference problems and suggest that a combination of LEM or LP with a subsequent PCA perform the best. We finally compare the distributions of the HR with that of two heuristics based on the Artificially Centered hit-and-run (ACHR, gpSampler and optGpSampler. They show a good agreement with the results of the HR for the small network, while on genome scale models present inconsistencies.

  16. Web-based metabolic network visualization with a zooming user interface

    Directory of Open Access Journals (Sweden)

    Karp Peter D

    2011-05-01

    Full Text Available Abstract Background Displaying complex metabolic-map diagrams, for Web browsers, and allowing users to interact with them for querying and overlaying expression data over them is challenging. Description We present a Web-based metabolic-map diagram, which can be interactively explored by the user, called the Cellular Overview. The main characteristic of this application is the zooming user interface enabling the user to focus on appropriate granularities of the network at will. Various searching commands are available to visually highlight sets of reactions, pathways, enzymes, metabolites, and so on. Expression data from single or multiple experiments can be overlaid on the diagram, which we call the Omics Viewer capability. The application provides Web services to highlight the diagram and to invoke the Omics Viewer. This application is entirely written in JavaScript for the client browsers and connect to a Pathway Tools Web server to retrieve data and diagrams. It uses the OpenLayers library to display tiled diagrams. Conclusions This new online tool is capable of displaying large and complex metabolic-map diagrams in a very interactive manner. This application is available as part of the Pathway Tools software that powers multiple metabolic databases including Biocyc.org: The Cellular Overview is accessible under the Tools menu.

  17. Genome-scale reconstruction of metabolic network for a halophilic extremophile, Chromohalobacter salexigens DSM 3043.

    Science.gov (United States)

    Ates, Ozlem; Oner, Ebru Toksoy; Arga, Kazim Y

    2011-01-21

    Chromohalobacter salexigens (formerly Halomonas elongata DSM 3043) is a halophilic extremophile with a very broad salinity range and is used as a model organism to elucidate prokaryotic osmoadaptation due to its strong euryhaline phenotype. C. salexigens DSM 3043's metabolism was reconstructed based on genomic, biochemical and physiological information via a non-automated but iterative process. This manually-curated reconstruction accounts for 584 genes, 1386 reactions, and 1411 metabolites. By using flux balance analysis, the model was extensively validated against literature data on the C. salexigens phenotypic features, the transport and use of different substrates for growth as well as against experimental observations on the uptake and accumulation of industrially important organic osmolytes, ectoine, betaine, and its precursor choline, which play important roles in the adaptive response to osmotic stress. This work presents the first comprehensive genome-scale metabolic model of a halophilic bacterium. Being a useful guide for identification and filling of knowledge gaps, the reconstructed metabolic network iOA584 will accelerate the research on halophilic bacteria towards application of systems biology approaches and design of metabolic engineering strategies.

  18. Genome-scale reconstruction of metabolic network for a halophilic extremophile, Chromohalobacter salexigens DSM 3043

    Directory of Open Access Journals (Sweden)

    Oner Ebru

    2011-01-01

    Full Text Available Abstract Background Chromohalobacter salexigens (formerly Halomonas elongata DSM 3043 is a halophilic extremophile with a very broad salinity range and is used as a model organism to elucidate prokaryotic osmoadaptation due to its strong euryhaline phenotype. Results C. salexigens DSM 3043's metabolism was reconstructed based on genomic, biochemical and physiological information via a non-automated but iterative process. This manually-curated reconstruction accounts for 584 genes, 1386 reactions, and 1411 metabolites. By using flux balance analysis, the model was extensively validated against literature data on the C. salexigens phenotypic features, the transport and use of different substrates for growth as well as against experimental observations on the uptake and accumulation of industrially important organic osmolytes, ectoine, betaine, and its precursor choline, which play important roles in the adaptive response to osmotic stress. Conclusions This work presents the first comprehensive genome-scale metabolic model of a halophilic bacterium. Being a useful guide for identification and filling of knowledge gaps, the reconstructed metabolic network iOA584 will accelerate the research on halophilic bacteria towards application of systems biology approaches and design of metabolic engineering strategies.

  19. Network environ perspective for urban metabolism and carbon emissions: a case study of Vienna, Austria.

    Science.gov (United States)

    Chen, Shaoqing; Chen, Bin

    2012-04-17

    Cities are considered major contributors to global warming, where carbon emissions are highly embedded in the overall urban metabolism. To examine urban metabolic processes and emission trajectories we developed a carbon flux model based on Network Environ Analysis (NEA). The mutual interactions and control situation within the urban ecosystem of Vienna were examined, and the system-level properties of the city's carbon metabolism were assessed. Regulatory strategies to minimize carbon emissions were identified through the tracking of the possible pathways that affect these emission trajectories. Our findings suggest that indirect flows have a strong bearing on the mutual and control relationships between urban sectors. The metabolism of a city is considered self-mutualistic and sustainable only when the local and distal environments are embraced. Energy production and construction were found to be two factors with a major impact on carbon emissions, and whose regulation is only effective via ad-hoc pathways. In comparison with the original life-cycle tracking, the application of NEA was better at revealing details from a mechanistic aspect, which is crucial for informed sustainable urban management.

  20. Topological Organization of Metabolic Brain Networks in Pre-Chemotherapy Cancer with Depression: A Resting-State PET Study.

    Science.gov (United States)

    Fang, Lei; Yao, Zhijun; An, Jianping; Chen, Xuejiao; Xie, Yuanwei; Zhao, Hui; Mao, Junfeng; Liang, Wangsheng; Ma, Xiangxing

    2016-01-01

    This study aimed to investigate the metabolic brain network and its relationship with depression symptoms using 18F-fluorodeoxyglucose positron emission tomography data in 78 pre-chemotherapy cancer patients with depression and 80 matched healthy subjects. Functional and structural imbalance or disruption of brain networks frequently occur following chemotherapy in cancer patients. However, few studies have focused on the topological organization of the metabolic brain network in cancer with depression, especially those without chemotherapy. The nodal and global parameters of the metabolic brain network were computed for cancer patients and healthy subjects. Significant decreases in metabolism were found in the frontal and temporal gyri in cancer patients compared with healthy subjects. Negative correlations between depression and metabolism were found predominantly in the inferior frontal and cuneus regions, whereas positive correlations were observed in several regions, primarily including the insula, hippocampus, amygdala, and middle temporal gyri. Furthermore, a higher clustering efficiency, longer path length, and fewer hubs were found in cancer patients compared with healthy subjects. The topological organization of the whole-brain metabolic networks may be disrupted in cancer. Finally, the present findings may provide a new avenue for exploring the neurobiological mechanism, which plays a key role in lessening the depression effects in pre-chemotherapy cancer patients.

  1. Pseudomonas fluorescens induces strain-dependent and strain-independent host plant responses in defense networks, primary metabolism and photosynthesis

    Energy Technology Data Exchange (ETDEWEB)

    Pelletier, Dale A [ORNL; Morrell-Falvey, Jennifer L [ORNL; Karve, Abhijit A [ORNL; Lu, Tse-Yuan S [ORNL; Tschaplinski, Timothy J [ORNL; Tuskan, Gerald A [ORNL; Chen, Jay [ORNL; Martin, Madhavi Z [ORNL; Jawdy, Sara [ORNL; Weston, David [ORNL; Doktycz, Mitchel John [ORNL; Schadt, Christopher Warren [ORNL

    2012-01-01

    Colonization of plants by nonpathogenic Pseudomonas fluorescens strains can confer enhanced defense capacity against a broad spectrum of pathogens. Few studies, however, have linked defense pathway regulation to primary metabolism and physiology. In this study, physiological data, metabolites, and transcript profiles are integrated to elucidate how molecular networks initiated at the root-microbe interface influence shoot metabolism and whole-plant performance. Experiments with Arabidopsis thaliana were performed using the newly identified P. fluorescens GM30 or P. fluorescens Pf-5 strains. Co-expression networks indicated that Pf-5 and GM30 induced a subnetwork specific to roots enriched for genes participating in RNA regulation, protein degradation, and hormonal metabolism. In contrast, only GM30 induced a subnetwork enriched for calcium signaling, sugar and nutrient signaling, and auxin metabolism, suggesting strain dependence in network architecture. In addition, one subnetwork present in shoots was enriched for genes in secondary metabolism, photosynthetic light reactions, and hormone metabolism. Metabolite analysis indicated that this network initiated changes in carbohydrate and amino acid metabolism. Consistent with this, we observed strain-specific responses in tryptophan and phenylalanine abundance. Both strains reduced host plant carbon gain and fitness, yet provided a clear fitness benefit when plants were challenged with the pathogen P. syringae DC3000.

  2. Increasing galactose consumption by Saccharomyces cerevisiae through metabolic engineering of the GAL gene regulatory network

    DEFF Research Database (Denmark)

    Østergaard, Simon; Olsson, Lisbeth; Johnston, M.

    2000-01-01

    in the pathway, and ultimately, increasing metabolic flux through the pathway of interest, By manipulating the GAL gene regulatory network of Saccharomyces cerevisiae, which is a tightly regulated system, we produced prototroph mutant strains, which increased the flux through the galactose utilization pathway...... by eliminating three known negative regulators of the GAL system: Gale, Gal80, and Mig1. This led to a 41% increase in flux through the galactose utilization pathway compared with the wild-type strain. This is of significant interest within the field of biotechnology since galactose is present in many industrial...... media. The improved galactose consumption of the gal mutants did not favor biomass formation, but rather caused excessive respiro-fermentative metabolism, with the ethanol production rate increasing linearly with glycolytic flux....

  3. Major facilitator superfamily domain-containing protein 2a (MFSD2A has roles in body growth, motor function, and lipid metabolism.

    Directory of Open Access Journals (Sweden)

    Justin H Berger

    Full Text Available The metabolic adaptations to fasting in the liver are largely controlled by the nuclear hormone receptor peroxisome proliferator-activated receptor alpha (PPARα, where PPARα upregulates genes encoding the biochemical pathway for β-oxidation of fatty acids and ketogenesis. As part of an effort to identify and characterize nutritionally regulated genes that play physiological roles in the adaptation to fasting, we identified Major facilitator superfamily domain-containing protein 2a (Mfsd2a as a fasting-induced gene regulated by both PPARα and glucagon signaling in the liver. MFSD2A is a cell-surface protein homologous to bacterial sodium-melibiose transporters. Hepatic expression and turnover of MFSD2A is acutely regulated by fasting/refeeding, but expression in the brain is constitutive. Relative to wildtype mice, gene-targeted Mfsd2a knockout mice are smaller, leaner, and have decreased serum, liver and brown adipose triglycerides. Mfsd2a knockout mice have normal liver lipid metabolism but increased whole body energy expenditure, likely due to increased β-oxidation in brown adipose tissue and significantly increased voluntary movement, but surprisingly exhibited a form of ataxia. Together, these results indicate that MFSD2A is a nutritionally regulated gene that plays myriad roles in body growth and development, motor function, and lipid metabolism. Moreover, these data suggest that the ligand(s that are transported by MFSD2A play important roles in these physiological processes and await future identification.

  4. Fast and accurate quantitative organic acid analysis with LC-QTOF/MS facilitates screening of patients for inborn errors of metabolism.

    Science.gov (United States)

    Körver-Keularts, Irene M L W; Wang, Ping; Waterval, Huub W A H; Kluijtmans, Leo A J; Wevers, Ron A; Langhans, Claus-Dieter; Scott, Camilla; Habets, Daphna D J; Bierau, Jörgen

    2018-02-12

    Since organic acid analysis in urine with gaschromatography-mass spectrometry (GC-MS) is a time-consuming technique, we developed a new liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) method to replace the classical analysis for diagnosis of inborn errors of metabolism (IEM). Sample preparation is simple and experimental time short. Targeted mass extraction and automatic calculation of z-scores generated profiles characteristic for the IEMs in our panel consisting of 71 biomarkers for defects in amino acids, neurotransmitters, fatty acids, purine, and pyrimidine metabolism as well as other disorders. In addition, four medication-related metabolites were included in the panel. The method was validated to meet Dutch NEN-EN-ISO 15189 standards. Cross validation of 24 organic acids from 28 urine samples of the ERNDIM scheme showed superiority of the UPLC-QTOF/MS method over the GC-MS method. We applied our method to 99 patient urine samples with 32 different IEMs, and 88 control samples. All IEMs were unambiguously established/diagnosed using this new QTOF method by evaluation of the panel of 71 biomarkers. In conclusion, we present a LC-QTOF/MS method for fast and accurate quantitative organic acid analysis which facilitates screening of patients for IEMs. Extension of the panel of metabolites is easy which makes this application a promising technique in metabolic diagnostics/laboratories.

  5. MetExploreViz: web component for interactive metabolic network visualization.

    Science.gov (United States)

    Chazalviel, Maxime; Frainay, Clément; Poupin, Nathalie; Vinson, Florence; Merlet, Benjamin; Gloaguen, Yoann; Cottret, Ludovic; Jourdan, Fabien

    2017-09-15

    MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyze omics data in a biochemical context. Documentation and link to GIT code repository (GPL 3.0 license)are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc /. Tutorial is available at this URL. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  6. Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli

    DEFF Research Database (Denmark)

    McCloskey, Douglas; Palsson, Bernhard; Feist, Adam

    2013-01-01

    of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective...... on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype-phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges...

  7. iRsp1095: A genome-scale reconstruction of the Rhodobacter sphaeroides metabolic network

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    Gorzalski Alexander S

    2011-07-01

    Full Text Available Abstract Background Rhodobacter sphaeroides is one of the best studied purple non-sulfur photosynthetic bacteria and serves as an excellent model for the study of photosynthesis and the metabolic capabilities of this and related facultative organisms. The ability of R. sphaeroides to produce hydrogen (H2, polyhydroxybutyrate (PHB or other hydrocarbons, as well as its ability to utilize atmospheric carbon dioxide (CO2 as a carbon source under defined conditions, make it an excellent candidate for use in a wide variety of biotechnological applications. A genome-level understanding of its metabolic capabilities should help realize this biotechnological potential. Results Here we present a genome-scale metabolic network model for R. sphaeroides strain 2.4.1, designated iRsp1095, consisting of 1,095 genes, 796 metabolites and 1158 reactions, including R. sphaeroides-specific biomass reactions developed in this study. Constraint-based analysis showed that iRsp1095 agreed well with experimental observations when modeling growth under respiratory and phototrophic conditions. Genes essential for phototrophic growth were predicted by single gene deletion analysis. During pathway-level analyses of R. sphaeroides metabolism, an alternative route for CO2 assimilation was identified. Evaluation of photoheterotrophic H2 production using iRsp1095 indicated that maximal yield would be obtained from growing cells, with this predicted maximum ~50% higher than that observed experimentally from wild type cells. Competing pathways that might prevent the achievement of this theoretical maximum were identified to guide future genetic studies. Conclusions iRsp1095 provides a robust framework for future metabolic engineering efforts to optimize the solar- and nutrient-powered production of biofuels and other valuable products by R. sphaeroides and closely related organisms.

  8. Metabolic network model guided engineering ethylmalonyl-CoA pathway to improve ascomycin production in Streptomyces hygroscopicus var. ascomyceticus.

    Science.gov (United States)

    Wang, Junhua; Wang, Cheng; Song, Kejing; Wen, Jianping

    2017-10-03

    Ascomycin is a 23-membered polyketide macrolide with high immunosuppressant and antifungal activity. As the lower production in bio-fermentation, global metabolic analysis is required to further explore its biosynthetic network and determine the key limiting steps for rationally engineering. To achieve this goal, an engineering approach guided by a metabolic network model was implemented to better understand ascomycin biosynthesis and improve its production. The metabolic conservation of Streptomyces species was first investigated by comparing the metabolic enzymes of Streptomyces coelicolor A3(2) with those of 31 Streptomyces strains, the results showed that more than 72% of the examined proteins had high sequence similarity with counterparts in every surveyed strain. And it was found that metabolic reactions are more highly conserved than the enzymes themselves because of its lower diversity of metabolic functions than that of genes. The main source of the observed metabolic differences was from the diversity of secondary metabolism. According to the high conservation of primary metabolic reactions in Streptomyces species, the metabolic network model of Streptomyces hygroscopicus var. ascomyceticus was constructed based on the latest reported metabolic model of S. coelicolor A3(2) and validated experimentally. By coupling with flux balance analysis and using minimization of metabolic adjustment algorithm, potential targets for ascomycin overproduction were predicted. Since several of the preferred targets were highly associated with ethylmalonyl-CoA biosynthesis, two target genes hcd (encoding 3-hydroxybutyryl-CoA dehydrogenase) and ccr (encoding crotonyl-CoA carboxylase/reductase) were selected for overexpression in S. hygroscopicus var. ascomyceticus FS35. Both the mutants HA-Hcd and HA-Ccr showed higher ascomycin titer, which was consistent with the model predictions. Furthermore, the combined effects of the two genes were evaluated and the strain HA

  9. The communicative and organisational competencies of the librarian in networked learning support: a comparative analysis of the roles of the facilitator and the librarian.

    Directory of Open Access Journals (Sweden)

    Trine Schreiber

    1997-01-01

    Full Text Available The aim of the paper is to compare the role of the facilitator with the role of the librarian. Firstly, a list of the role dimensions of the facilitation is described. Secondly, a case study of a facilitation proces is presented. Thirdly, the intermediary functions of the librarian is considered. The comparison shows that the similarities between the two roles concerns the communication, the identification of information needs and the translation of the user formulations into a systematized terminology. Moreover, we cannot exclude that two elements of the librarian's information seeking process; i.e., the searching activity and the evaluation of the results, may exist in the work of the facilitator. Still, the important difference is, that the information seeking process, carried out of the facilitator, may be based not on the information needs of the user but on the predetermined outcome of the communication process. However, a more explicit work with the functions of the librarian; i.e.. both the searching activity and the evaluation of the results, during a networked communication process, may strengthen the group understanding development. In this way, the role of the librarian could develop the role of the facilitator. At the same time, the attention of the facilitator to the needs of the group could bring an important aspect into the role of the librarian.

  10. Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks.

    Science.gov (United States)

    Yurkovich, James T; Zielinski, Daniel C; Yang, Laurence; Paglia, Giuseppe; Rolfsson, Ottar; Sigurjónsson, Ólafur E; Broddrick, Jared T; Bordbar, Aarash; Wichuk, Kristine; Brynjólfsson, Sigurður; Palsson, Sirus; Gudmundsson, Sveinn; Palsson, Bernhard O

    2017-12-01

    The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology ( e.g. basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model ( Q 10 ) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q 10 coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q 10 coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q 10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  11. Kriging-Based Parameter Estimation Algorithm for Metabolic Networks Combined with Single-Dimensional Optimization and Dynamic Coordinate Perturbation.

    Science.gov (United States)

    Wang, Hong; Wang, Xicheng; Li, Zheng; Li, Keqiu

    2016-01-01

    The metabolic network model allows for an in-depth insight into the molecular mechanism of a particular organism. Because most parameters of the metabolic network cannot be directly measured, they must be estimated by using optimization algorithms. However, three characteristics of the metabolic network model, i.e., high nonlinearity, large amount parameters, and huge variation scopes of parameters, restrict the application of many traditional optimization algorithms. As a result, there is a growing demand to develop efficient optimization approaches to address this complex problem. In this paper, a Kriging-based algorithm aiming at parameter estimation is presented for constructing the metabolic networks. In the algorithm, a new infill sampling criterion, named expected improvement and mutual information (EI&MI), is adopted to improve the modeling accuracy by selecting multiple new sample points at each cycle, and the domain decomposition strategy based on the principal component analysis is introduced to save computing time. Meanwhile, the convergence speed is accelerated by combining a single-dimensional optimization method with the dynamic coordinate perturbation strategy when determining the new sample points. Finally, the algorithm is applied to the arachidonic acid metabolic network to estimate its parameters. The obtained results demonstrate the effectiveness of the proposed algorithm in getting precise parameter values under a limited number of iterations.

  12. The tricarboxylic acid cycle, an ancient metabolic network with a novel twist.

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    Ryan J Mailloux

    Full Text Available The tricarboxylic acid (TCA cycle is an essential metabolic network in all oxidative organisms and provides precursors for anabolic processes and reducing factors (NADH and FADH(2 that drive the generation of energy. Here, we show that this metabolic network is also an integral part of the oxidative defence machinery in living organisms and alpha-ketoglutarate (KG is a key participant in the detoxification of reactive oxygen species (ROS. Its utilization as an anti-oxidant can effectively diminish ROS and curtail the formation of NADH, a situation that further impedes the release of ROS via oxidative phosphorylation. Thus, the increased production of KG mediated by NADP-dependent isocitrate dehydrogenase (NADP-ICDH and its decreased utilization via the TCA cycle confer a unique strategy to modulate the cellular redox environment. Activities of alpha-ketoglutarate dehydrogenase (KGDH, NAD-dependent isocitrate dehydrogenase (NAD-ICDH, and succinate dehydrogenase (SDH were sharply diminished in the cellular systems exposed to conditions conducive to oxidative stress. These findings uncover an intricate link between TCA cycle and ROS homeostasis and may help explain the ineffective TCA cycle that characterizes various pathological conditions and ageing.

  13. Direct calculation of elementary flux modes satisfying several biological constraints in genome-scale metabolic networks.

    Science.gov (United States)

    Pey, Jon; Planes, Francisco J

    2014-08-01

    The concept of Elementary Flux Mode (EFM) has been widely used for the past 20 years. However, its application to genome-scale metabolic networks (GSMNs) is still under development because of methodological limitations. Therefore, novel approaches are demanded to extend the application of EFMs. A novel family of methods based on optimization is emerging that provides us with a subset of EFMs. Because the calculation of the whole set of EFMs goes beyond our capacity, performing a selective search is a proper strategy. Here, we present a novel mathematical approach calculating EFMs fulfilling additional linear constraints. We validated our approach based on two metabolic networks in which all the EFMs can be obtained. Finally, we analyzed the performance of our methodology in the GSMN of the yeast Saccharomyces cerevisiae by calculating EFMs producing ethanol with a given minimum carbon yield. Overall, this new approach opens new avenues for the calculation of EFMs in GSMNs. Matlab code is provided in the supplementary online materials fplanes@ceit.es. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  14. Multiple Substrate Usage of Coxiella burnetii to Feed a Bipartite Metabolic Network

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    Ina Häuslein

    2017-06-01

    Full Text Available The human pathogen Coxiella burnetii causes Q-fever and is classified as a category B bio-weapon. Exploiting the development of the axenic growth medium ACCM-2, we have now used 13C-labeling experiments and isotopolog profiling to investigate the highly diverse metabolic network of C. burnetii. To this aim, C. burnetii RSA 439 NMII was cultured in ACCM-2 containing 5 mM of either [U-13C3]serine, [U-13C6]glucose, or [U-13C3]glycerol until the late-logarithmic phase. GC/MS-based isotopolog profiling of protein-derived amino acids, methanol-soluble polar metabolites, fatty acids, and cell wall components (e.g., diaminopimelate and sugars from the labeled bacteria revealed differential incorporation rates and isotopolog profiles. These data served to decipher the diverse usages of the labeled substrates and the relative carbon fluxes into the core metabolism of the pathogen. Whereas, de novo biosynthesis from any of these substrates could not be found for histidine, isoleucine, leucine, lysine, phenylalanine, proline and valine, the other amino acids and metabolites under study acquired 13C-label at specific rates depending on the nature of the tracer compound. Glucose was directly used for cell wall biosynthesis, but was also converted into pyruvate (and its downstream metabolites through the glycolytic pathway or into erythrose 4-phosphate (e.g., for the biosynthesis of tyrosine via the non-oxidative pentose phosphate pathway. Glycerol efficiently served as a gluconeogenetic substrate and could also be used via phosphoenolpyruvate and diaminopimelate as a major carbon source for cell wall biosynthesis. In contrast, exogenous serine was mainly utilized in downstream metabolic processes, e.g., via acetyl-CoA in a complete citrate cycle with fluxes in the oxidative direction and as a carbon feed for fatty acid biosynthesis. In summary, the data reflect multiple and differential substrate usages by C. burnetii in a bipartite-type metabolic network

  15. Convergent evolution of modularity in metabolic networks through different community structures

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    Zhou Wanding

    2012-09-01

    Full Text Available Abstract Background It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. Results In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxomony. Conclusions We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct enzymes in the organism’s metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability. Further, our results

  16. Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803

    DEFF Research Database (Denmark)

    Montagud, Arnau; Zelezniak, Aleksej; Navarro, Emilio

    2011-01-01

    Synechocystis sp. PCC6803 is a model cyanobacterium capable of producing biofuels with CO2 as carbon source and with its metabolism fueled by light, for which it stands as a potential production platform of socio-economic importance. Compilation and characterization of Synechocystis genome...... networks, surrounded by a stable core of pathways leading to biomass building blocks. This analysis identified potential bottlenecks for hydrogen and ethanol production. Integration of transcriptomic data with the Synechocystis flux coupling networks lead to identification of reporter flux coupling pairs...... and reporter flux coupling groups - regulatory hot spots during metabolic shifts triggered by the availability of light. Overall, flux coupling analysis provided insight into the structural organization of Synechocystis sp. PCC6803 metabolic network toward designing of a photosynthesis-based production...

  17. A systems biology approach to reconcile metabolic network models with application to Synechocystis sp. PCC 6803 for biofuel production.

    Science.gov (United States)

    Mohammadi, Reza; Fallah-Mehrabadi, Jalil; Bidkhori, Gholamreza; Zahiri, Javad; Javad Niroomand, Mohammad; Masoudi-Nejad, Ali

    2016-07-19

    Production of biofuels has been one of the promising efforts in biotechnology in the past few decades. The perspective of these efforts can be reduction of increasing demands for fossil fuels and consequently reducing environmental pollution. Nonetheless, most previous approaches did not succeed in obviating many big challenges in this way. In recent years systems biology with the help of microorganisms has been trying to overcome these challenges. Unicellular cyanobacteria are widespread phototrophic microorganisms that have capabilities such as consuming solar energy and atmospheric carbon dioxide for growth and thus can be a suitable chassis for the production of valuable organic materials such as biofuels. For the ultimate use of metabolic potential of cyanobacteria, it is necessary to understand the reactions that are taking place inside the metabolic network of these microorganisms. In this study, we developed a Java tool to reconstruct an integrated metabolic network of a cyanobacterium (Synechocystis sp. PCC 6803). We merged three existing reconstructed metabolic networks of this microorganism. Then, after modeling for biofuel production, the results from flux balance analysis (FBA) disclosed an increased yield in biofuel production for ethanol, isobutanol, 3-methyl-1-butanol, 2-methyl-1-butanol, and propanol. The numbers of blocked reactions were also decreased for 2-methyl-1-butanol production. In addition, coverage of the metabolic network in terms of the number of metabolites and reactions was increased in the new obtained model.

  18. Metabolism

    Science.gov (United States)

    ... functions: Anabolism (uh-NAB-uh-liz-um), or constructive metabolism, is all about building and storing. It ... in infants and young children. Hypothyroidism slows body processes and causes fatigue (tiredness), slow heart rate, excessive ...

  19. Metabolism

    Science.gov (United States)

    ... a particular food provides to the body. A chocolate bar has more calories than an apple, so ... acid phenylalanine, needed for normal growth and protein production). Inborn errors of metabolism can sometimes lead to ...

  20. A novel strategy involved in [corrected] anti-oxidative defense: the conversion of NADH into NADPH by a metabolic network.

    Directory of Open Access Journals (Sweden)

    Ranji Singh

    Full Text Available The reduced nicotinamide adenine dinucleotide phosphate (NADPH is pivotal to the cellular anti-oxidative defence strategies in most organisms. Although its production mediated by different enzyme systems has been relatively well-studied, metabolic networks dedicated to the biogenesis of NADPH have not been fully characterized. In this report, a metabolic pathway that promotes the conversion of reduced nicotinamide adenine dinucleotide (NADH, a pro-oxidant into NADPH has been uncovered in Pseudomonas fluorescens exposed to oxidative stress. Enzymes such as pyruvate carboxylase (PC, malic enzyme (ME, malate dehydrogenase (MDH, malate synthase (MS, and isocitrate lyase (ICL that are involved in disparate metabolic modules, converged to create a metabolic network aimed at the transformation of NADH into NADPH. The downregulation of phosphoenol carboxykinase (PEPCK and the upregulation of pyruvate kinase (PK ensured that this metabolic cycle fixed NADH into NADPH to combat the oxidative stress triggered by the menadione insult. This is the first demonstration of a metabolic network invoked to generate NADPH from NADH, a process that may be very effective in combating oxidative stress as the increase of an anti-oxidant is coupled to the decrease of a pro-oxidant.

  1. Genes encoding hub and bottleneck enzymes of the Arabidopsis metabolic network preferentially retain homeologs through whole genome duplication

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    Qi Xiaoquan

    2010-05-01

    Full Text Available Abstract Background Whole genome duplication (WGD occurs widely in angiosperm evolution. It raises the intriguing question of how interacting networks of genes cope with this dramatic evolutionary event. Results In study of the Arabidopsis metabolic network, we assigned each enzyme (node with topological centralities (in-degree, out-degree and between-ness to measure quantitatively their centralities in the network. The Arabidopsis metabolic network is highly modular and separated into 11 interconnected modules, which correspond well to the functional metabolic pathways. The enzymes with higher in-out degree and between-ness (defined as hub and bottleneck enzymes, respectively tend to be more conserved and preferentially retain homeologs after WGD. Moreover, the simultaneous retention of homeologs encoding enzymes which catalyze consecutive steps in a pathway is highly favored and easily achieved, and enzyme-enzyme interactions contribute to the retention of one-third of WGD enzymes. Conclusions Our analyses indicate that the hub and bottleneck enzymes of metabolic network obtain great benefits from WGD, and this event grants clear evolutionary advantages in adaptation to different environments.

  2. Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models.

    Science.gov (United States)

    Yuan, Qianqian; Huang, Teng; Li, Peishun; Hao, Tong; Li, Feiran; Ma, Hongwu; Wang, Zhiwen; Zhao, Xueming; Chen, Tao; Goryanin, Igor

    2017-01-01

    Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design.

  3. Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models.

    Directory of Open Access Journals (Sweden)

    Qianqian Yuan

    Full Text Available Over 100 genome-scale metabolic networks (GSMNs have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design.

  4. Integer programming-based method for designing synthetic metabolic networks by Minimum Reaction Insertion in a Boolean model.

    Science.gov (United States)

    Lu, Wei; Tamura, Takeyuki; Song, Jiangning; Akutsu, Tatsuya

    2014-01-01

    In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."

  5. Rhinal hypometabolism on FDG PET in healthy APO-E4 carriers: impact on memory function and metabolic networks

    Energy Technology Data Exchange (ETDEWEB)

    Didic, Mira; Felician, Olivier; Gour, Natalina; Ceccaldi, Mathieu [Pole de Neurosciences Cliniques, Centre Hospitalo-Universitaire de la Timone, AP-HM, Service de Neurologie and Neuropsychologie, Marseille (France); Aix Marseille Universite, Inserm, INS UMRS 1106, Marseille (France); Bernard, Rafaelle; Pecheux, Christophe [Centre Hospitalo-Universitaire de la Timone, AP-HM, et INSERM UMRS 910: ' ' Genetique Medicale et Genomique fonctionnelle' ' , Departement de Genetique Medicale, Marseille (France); Mundler, Olivier; Guedj, Eric [Centre Hospitalo-Universitaire de la Timone, AP-HM, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Aix Marseille Universite, CERIMED, CNRS UMR7289, INT, Marseille (France); Aix Marseille Universite, CNRS UMR7289, INT, Marseille (France)

    2015-09-15

    The ε4 allele of the apolipoprotein E (APO-E4) gene, a genetic risk factor for Alzheimer's disease (AD), also modulates brain metabolism and function in healthy subjects. The aim of the present study was to explore cerebral metabolism using FDG PET in healthy APO-E4 carriers by comparing cognitively normal APO-E4 carriers to noncarriers and to assess if patterns of metabolism are correlated with performance on cognitive tasks. Moreover, metabolic connectivity patterns were established in order to assess if the organization of neural networks is influenced by genetic factors. Whole-brain PET statistical analysis was performed at voxel-level using SPM8 with a threshold of p < 0.005, corrected for volume, with age, gender and level of education as nuisance variables. Significant hypometabolism between APO-E4 carriers (n = 11) and noncarriers (n = 30) was first determined. Mean metabolic values with clinical/neuropsychological data were extracted at the individual level, and correlations were searched using Spearman's rank test in the whole group. To evaluate metabolic connectivity from metabolic cluster(s) previously identified in the intergroup comparison, voxel-wise interregional correlation analysis (IRCA) was performed between groups of subjects. APO-E4 carriers had reduced metabolism within the left anterior medial temporal lobe (MTL), where neuropathological changes first appear in AD, including the entorhinal and perirhinal cortices. A correlation between metabolism in this area and performance on the DMS48 (delayed matching to sample-48 items) was found, in line with converging evidence involving the perirhinal cortex in object-based memory. Finally, a voxel-wise IRCA revealed stronger metabolic connectivity of the MTL cluster with neocortical frontoparietal regions in carriers than in noncarriers, suggesting compensatory metabolic networks. Exploring cerebral metabolism using FDG PET can contribute to a better understanding of the influence of

  6. Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction

    Science.gov (United States)

    Chaudhury, Sidhartha; Abdulhameed, Mohamed Diwan M.; Singh, Narender; Tawa, Gregory J.; D’haeseleer, Patrik M.; Zemla, Adam T.; Navid, Ali; Zhou, Carol E.; Franklin, Matthew C.; Cheung, Jonah; Rudolph, Michael J.; Love, James; Graf, John F.; Rozak, David A.; Dankmeyer, Jennifer L.; Amemiya, Kei; Daefler, Simon; Wallqvist, Anders

    2013-01-01

    In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested

  7. Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction

    Directory of Open Access Journals (Sweden)

    Panda Gurudutta

    2011-05-01

    Full Text Available Abstract Background Burkholderia cenocepacia is a threatening nosocomial epidemic pathogen in patients with cystic fibrosis (CF or a compromised immune system. Its high level of antibiotic resistance is an increasing concern in treatments against its infection. Strain B. cenocepacia J2315 is the most infectious isolate from CF patients. There is a strong demand to reconstruct a genome-scale metabolic network of B. cenocepacia J2315 to systematically analyze its metabolic capabilities and its virulence traits, and to search for potential clinical therapy targets. Results We reconstructed the genome-scale metabolic network of B. cenocepacia J2315. An iterative reconstruction process led to the establishment of a robust model, iKF1028, which accounts for 1,028 genes, 859 internal reactions, and 834 metabolites. The model iKF1028 captures important metabolic capabilities of B. cenocepacia J2315 with a particular focus on the biosyntheses of key metabolic virulence factors to assist in understanding the mechanism of disease infection and identifying potential drug targets. The model was tested through BIOLOG assays. Based on the model, the genome annotation of B. cenocepacia J2315 was refined and 24 genes were properly re-annotated. Gene and enzyme essentiality were analyzed to provide further insights into the genome function and architecture. A total of 45 essential enzymes were identified as potential therapeutic targets. Conclusions As the first genome-scale metabolic network of B. cenocepacia J2315, iKF1028 allows a systematic study of the metabolic properties of B. cenocepacia and its key metabolic virulence factors affecting the CF community. The model can be used as a discovery tool to design novel drugs against diseases caused by this notorious pathogen.

  8. Gender differences of brain glucose metabolic networks revealed by FDG-PET: evidence from a large cohort of 400 young adults.

    Science.gov (United States)

    Hu, Yuxiao; Xu, Qiang; Li, Kai; Zhu, Hong; Qi, Rongfeng; Zhang, Zhiqiang; Lu, Guangming

    2013-01-01

    Gender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample. FDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25:45 years, mean age ± SD: 40.9 ± 3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders. Compared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study. This study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control and experimental individuals or patients.

  9. Regulatory network of secondary metabolism in Brassica rapa: insight into the glucosinolate pathway.

    Science.gov (United States)

    Pino Del Carpio, Dunia; Basnet, Ram Kumar; Arends, Danny; Lin, Ke; De Vos, Ric C H; Muth, Dorota; Kodde, Jan; Boutilier, Kim; Bucher, Johan; Wang, Xiaowu; Jansen, Ritsert; Bonnema, Guusje

    2014-01-01

    Brassica rapa studies towards metabolic variation have largely been focused on the profiling of the diversity of metabolic compounds in specific crop types or regional varieties, but none aimed to identify genes with regulatory function in metabolite composition. Here we followed a genetical genomics approach to identify regulatory genes for six biosynthetic pathways of health-related phytochemicals, i.e carotenoids, tocopherols, folates, glucosinolates, flavonoids and phenylpropanoids. Leaves from six weeks-old plants of a Brassica rapa doubled haploid population, consisting of 92 genotypes, were profiled for their secondary metabolite composition, using both targeted and LC-MS-based untargeted metabolomics approaches. Furthermore, the same population was profiled for transcript variation using a microarray containing EST sequences mainly derived from three Brassica species: B. napus, B. rapa and B. oleracea. The biochemical pathway analysis was based on the network analyses of both metabolite QTLs (mQTLs) and transcript QTLs (eQTLs). Co-localization of mQTLs and eQTLs lead to the identification of candidate regulatory genes involved in the biosynthesis of carotenoids, tocopherols and glucosinolates. We subsequently focused on the well-characterized glucosinolate pathway and revealed two hotspots of co-localization of eQTLs with mQTLs in linkage groups A03 and A09. Our results indicate that such a large-scale genetical genomics approach combining transcriptomics and metabolomics data can provide new insights into the genetic regulation of metabolite composition of Brassica vegetables.

  10. Regulatory network of secondary metabolism in Brassica rapa: insight into the glucosinolate pathway.

    Directory of Open Access Journals (Sweden)

    Dunia Pino Del Carpio

    Full Text Available Brassica rapa studies towards metabolic variation have largely been focused on the profiling of the diversity of metabolic compounds in specific crop types or regional varieties, but none aimed to identify genes with regulatory function in metabolite composition. Here we followed a genetical genomics approach to identify regulatory genes for six biosynthetic pathways of health-related phytochemicals, i.e carotenoids, tocopherols, folates, glucosinolates, flavonoids and phenylpropanoids. Leaves from six weeks-old plants of a Brassica rapa doubled haploid population, consisting of 92 genotypes, were profiled for their secondary metabolite composition, using both targeted and LC-MS-based untargeted metabolomics approaches. Furthermore, the same population was profiled for transcript variation using a microarray containing EST sequences mainly derived from three Brassica species: B. napus, B. rapa and B. oleracea. The biochemical pathway analysis was based on the network analyses of both metabolite QTLs (mQTLs and transcript QTLs (eQTLs. Co-localization of mQTLs and eQTLs lead to the identification of candidate regulatory genes involved in the biosynthesis of carotenoids, tocopherols and glucosinolates. We subsequently focused on the well-characterized glucosinolate pathway and revealed two hotspots of co-localization of eQTLs with mQTLs in linkage groups A03 and A09. Our results indicate that such a large-scale genetical genomics approach combining transcriptomics and metabolomics data can provide new insights into the genetic regulation of metabolite composition of Brassica vegetables.

  11. Identification of a human neonatal immune-metabolic network associated with bacterial infection.

    Science.gov (United States)

    Smith, Claire L; Dickinson, Paul; Forster, Thorsten; Craigon, Marie; Ross, Alan; Khondoker, Mizanur R; France, Rebecca; Ivens, Alasdair; Lynn, David J; Orme, Judith; Jackson, Allan; Lacaze, Paul; Flanagan, Katie L; Stenson, Benjamin J; Ghazal, Peter

    2014-08-14

    Understanding how human neonates respond to infection remains incomplete. Here, a system-level investigation of neonatal systemic responses to infection shows a surprisingly strong but unbalanced homeostatic immune response; developing an elevated set-point of myeloid regulatory signalling and sugar-lipid metabolism with concomitant inhibition of lymphoid responses. Innate immune-negative feedback opposes innate immune activation while suppression of T-cell co-stimulation is coincident with selective upregulation of CD85 co-inhibitory pathways. By deriving modules of co-expressed RNAs, we identify a limited set of networks associated with bacterial infection that exhibit high levels of inter-patient variability. Whereas, by integrating immune and metabolic pathways, we infer a patient-invariant 52-gene-classifier that predicts bacterial infection with high accuracy using a new independent patient population. This is further shown to have predictive value in identifying infection in suspected cases with blood culture-negative tests. Our results lay the foundation for future translation of host pathways in advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.

  12. Energy metabolism and ATP balance in animal cell cultivation using a stoichiometrically based reaction network.

    Science.gov (United States)

    Xie, L; Wang, D I

    1996-12-05

    A metabolic reaction network is developed for the estimation of the stoichiometric production of adenosine triphosphate (ATP) in animal cell culture. By using the material balance data from fed-batch and batch cultures of hybridoma cells, the stoichiometric ATP productions are determined with estimated effective P/O ratios of 2 for NADH and 1.2 for FADH(2). A significant percentage of the ATP requirement (16-41%) in hybridoma cells is generated directly from free energy release without the participation of oxygen. The oxidative phosphorylation of NADH accounts for about 60% of the total ATP production in the fed-batch cultures and about 47% in the batch culture. The oxidative phosphorylation of FADH(2) accounts for less then 20% of the total ATP production in all cases.A fractional model is devised to analyze the contribution of each nutrient to the ATP production. Results show that a majority of the ATP is produced from glucose metabolism (60-76%). Less than 30% of the ATP is derived from glutamine, and less than 11% is derived from other essential amino acids. The analysis also shows that the glycolytic pathway generates more ATP in the batch (41%) than in the fed-batch (demand estimated from the dry cell weight and cell composition is significantly lower than that calculated from the maximum ATP yield, indicating that the non-growth-associated ATP demand may contain other factors than what is considered in the estimation of the biosynthetic ATP demand.

  13. Genome-scale metabolic network guided engineering of Streptomyces tsukubaensis for FK506 production improvement.

    Science.gov (United States)

    Huang, Di; Li, Shanshan; Xia, Menglei; Wen, Jianping; Jia, Xiaoqiang

    2013-05-24

    FK506 is an important immunosuppressant, which can be produced by Streptomyces tsukubaensis. However, the production capacity of the strain is very low. Hereby, a computational guided engineering approach was proposed in order to improve the intracellular precursor and cofactor availability of FK506 in S. tsukubaensis. First, a genome-scale metabolic model of S. tsukubaensis was constructed based on its annotated genome and biochemical information. Subsequently, several potential genetic targets (knockout or overexpression) that guaranteed an improved yield of FK506 were identified by the recently developed methodology. To validate the model predictions, each target gene was manipulated in the parent strain D852, respectively. All the engineered strains showed a higher FK506 production, compared with D852. Furthermore, the combined effect of the genetic modifications was evaluated. Results showed that the strain HT-ΔGDH-DAZ with gdhA-deletion and dahp-, accA2-, zwf2-overexpression enhanced FK506 concentration up to 398.9 mg/L, compared with 143.5 mg/L of the parent strain D852. Finally, fed-batch fermentations of HT-ΔGDH-DAZ were carried out, which led to the FK506 production of 435.9 mg/L, 1.47-fold higher than the parent strain D852 (158.7 mg/L). Results confirmed that the promising targets led to an increase in FK506 titer. The present work is the first attempt to engineer the primary precursor pathways to improve FK506 production in S. tsukubaensis with genome-scale metabolic network guided metabolic engineering. The relationship between model prediction and experimental results demonstrates the rationality and validity of this approach for target identification. This strategy can also be applied to the improvement of other important secondary metabolites.

  14. Reduced Metabolism in Brain 'Control Networks' Following Cocaine-Cues Exposure in Female Cocaine Abusers

    International Nuclear Information System (INIS)

    Volkow, N.D.; Tomasi, D.; Wang, G.-J.; Fowler, J.S.; Telang, F.; Goldstein, R.Z.; Alia-Klein, N.; Wong, C.T.

    2011-01-01

    Gender differences in vulnerability for cocaine addiction have been reported. Though the mechanisms are not understood, here we hypothesize that gender differences in reactivity to conditioned-cues, which contributes to relapse, are involved. To test this we compared brain metabolism (using PET and 18 FDG) between female (n = 10) and male (n = 16) active cocaine abusers when they watched a neutral video (nature scenes) versus a cocaine-cues video. Self-reports of craving increased with the cocaine-cue video but responses did not differ between genders. In contrast, changes in whole brain metabolism with cocaine-cues differed by gender (p<0.05); females significantly decreased metabolism (-8.6% ± 10) whereas males tended to increase it (+5.5% ± 18). SPM analysis (Cocaine-cues vs Neutral) in females revealed decreases in frontal, cingulate and parietal cortices, thalamus and midbrain (p<0.001) whereas males showed increases in right inferior frontal gyrus (BA 44/45) (only at p<0.005). The gender-cue interaction showed greater decrements with Cocaine-cues in females than males (p<0.001) in frontal (BA 8, 9, 10), anterior cingulate (BA 24, 32), posterior cingulate (BA 23, 31), inferior parietal (BA 40) and thalamus (dorsomedial nucleus). Females showed greater brain reactivity to cocaine-cues than males but no differences in craving, suggesting that there may be gender differences in response to cues that are not linked with craving but could affect subsequent drug use. Specifically deactivation of brain regions from 'control networks' (prefrontal, cingulate, inferior parietal, thalamus) in females could increase their vulnerability to relapse since it would interfere with executive function (cognitive inhibition). This highlights the importance of gender tailored interventions for cocaine addiction.

  15. Reduced metabolism in brain "control networks" following cocaine-cues exposure in female cocaine abusers.

    Directory of Open Access Journals (Sweden)

    Nora D Volkow

    2011-02-01

    Full Text Available Gender differences in vulnerability for cocaine addiction have been reported. Though the mechanisms are not understood, here we hypothesize that gender differences in reactivity to conditioned-cues, which contributes to relapse, are involved.To test this we compared brain metabolism (using PET and ¹⁸FDG between female (n = 10 and male (n = 16 active cocaine abusers when they watched a neutral video (nature scenes versus a cocaine-cues video.Self-reports of craving increased with the cocaine-cue video but responses did not differ between genders. In contrast, changes in whole brain metabolism with cocaine-cues differed by gender (p<0.05; females significantly decreased metabolism (-8.6%±10 whereas males tended to increase it (+5.5%±18. SPM analysis (Cocaine-cues vs Neutral in females revealed decreases in frontal, cingulate and parietal cortices, thalamus and midbrain (p<0.001 whereas males showed increases in right inferior frontal gyrus (BA 44/45 (only at p<0.005. The gender-cue interaction showed greater decrements with Cocaine-cues in females than males (p<0.001 in frontal (BA 8, 9, 10, anterior cingulate (BA 24, 32, posterior cingulate (BA 23, 31, inferior parietal (BA 40 and thalamus (dorsomedial nucleus.Females showed greater brain reactivity to cocaine-cues than males but no differences in craving, suggesting that there may be gender differences in response to cues that are not linked with craving but could affect subsequent drug use. Specifically deactivation of brain regions from "control networks" (prefrontal, cingulate, inferior parietal, thalamus in females could increase their vulnerability to relapse since it would interfere with executive function (cognitive inhibition. This highlights the importance of gender tailored interventions for cocaine addiction.

  16. Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes

    DEFF Research Database (Denmark)

    Min, Josine L; Nicholson, George; Halgrimsdottir, Ingileif

    2012-01-01

    Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, ...

  17. Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis–Menten and approximate kinetic equations

    DEFF Research Database (Denmark)

    Costa, Rafael S.; Machado, Daniel; Rocha, Isabel

    2010-01-01

    The construction of dynamic metabolic models at reaction network level requires the use of mechanistic enzymatic rate equations that comprise a large number of parameters. The lack of knowledge on these equations and the difficulty in the experimental identification of their associated parameters...

  18. A genome scale metabolic network for rice and accompanying analysis of tryptophan, auxin and serotonin biosynthesis regulation under biotic stress

    Science.gov (United States)

    Functional annotations of large plant genome projects mostly provide information on gene function and gene families based on the presence of protein domains and gene homology, but not necessarily in association with gene expression or metabolic and regulatory networks. These additional annotations a...

  19. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

    DEFF Research Database (Denmark)

    Nandy, Subir Kumar; Jouhten, Paula; Nielsen, Jens

    2010-01-01

    BACKGROUND: Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different......-sensing and metabolic regulatory signal transduction pathways (STP) operating in Saccharomyces cerevisiae. The reconstructed STP network includes a full protein-protein interaction network including the key nodes Snf1, Tor1, Hog1 and Pka1. The network includes a total of 623 structural open reading frames (ORFs...

  20. Context-specific metabolic network reconstruction of a naphthalene-degrading bacterial community guided by metaproteomic data.

    Science.gov (United States)

    Tobalina, Luis; Bargiela, Rafael; Pey, Jon; Herbst, Florian-Alexander; Lores, Iván; Rojo, David; Barbas, Coral; Peláez, Ana I; Sánchez, Jesús; von Bergen, Martin; Seifert, Jana; Ferrer, Manuel; Planes, Francisco J

    2015-06-01

    With the advent of meta-'omics' data, the use of metabolic networks for the functional analysis of microbial communities became possible. However, while network-based methods are widely developed for single organisms, their application to bacterial communities is currently limited. Herein, we provide a novel, context-specific reconstruction procedure based on metaproteomic and taxonomic data. Without previous knowledge of a high-quality, genome-scale metabolic networks for each different member in a bacterial community, we propose a meta-network approach, where the expression levels and taxonomic assignments of proteins are used as the most relevant clues for inferring an active set of reactions. Our approach was applied to draft the context-specific metabolic networks of two different naphthalene-enriched communities derived from an anthropogenically influenced, polyaromatic hydrocarbon contaminated soil, with (CN2) or without (CN1) bio-stimulation. We were able to capture the overall functional differences between the two conditions at the metabolic level and predict an important activity for the fluorobenzoate degradation pathway in CN1 and for geraniol metabolism in CN2. Experimental validation was conducted, and good agreement with our computational predictions was observed. We also hypothesize different pathway organizations at the organismal level, which is relevant to disentangle the role of each member in the communities. The approach presented here can be easily transferred to the analysis of genomic, transcriptomic and metabolomic data. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  1. Metabolic and protein interaction sub-networks controlling the proliferation rate of cancer cells and their impact on patient survival.

    Science.gov (United States)

    Feizi, Amir; Bordel, Sergio

    2013-10-24

    Cancer cells can have a broad scope of proliferation rates. Here we aim to identify the molecular mechanisms that allow some cancer cell lines to grow up to 4 times faster than other cell lines. The correlation of gene expression profiles with the growth rate in 60 different cell lines has been analyzed using several genome-scale biological networks and new algorithms. New possible regulatory feedback loops have been suggested and the known roles of several cell cycle related transcription factors have been confirmed. Over 100 growth-correlated metabolic sub-networks have been identified, suggesting a key role of simultaneous lipid synthesis and degradation in the energy supply of the cancer cells growth. Many metabolic sub-networks involved in cell line proliferation appeared also to correlate negatively with the survival expectancy of colon cancer patients.

  2. Creative benefits from well-connected leaders? Leader social network ties as facilitators of employee radical creativity

    OpenAIRE

    Venkataramani, V; Richter, Andreas Wilhelm; Clarke, R

    2014-01-01

    Employee radical creativity critically depends on substantive informational resources from others across the wider organization. We propose that the social network ties of employees’ immediate leaders assume a central role in garnering these resources, thereby fostering their employees’ radical creativity both independent of, and interactively with, employees’ own network ties. Drawing on data from 214 employees working in 30 teams of a public technology and environmental services organizatio...

  3. Information technology as a facilitator of suppliers’ collaborative communication, network governance and relationship longevity in supply chains

    Directory of Open Access Journals (Sweden)

    Richard Chinomona

    2013-08-01

    Full Text Available There is an increasing awareness about the paramount importance of information technology within business in the context of large businesses. However, research about the investigation of the role of information technology resources in fostering collaborative communication, network governance and relationship longevity in the small and medium enterprise sector has remained scant. The primary objective of this study was to investigate the influence of information technology on collaborative communication, network governance and relationship longevity in Zimbabwe’s SME sector. Five research hypotheses were posited and sample data from 162 small and medium enterprise suppliers were collected and used to empirically test the hypotheses. The results of this study showed that information technology resources positively influenced small and medium enterprise suppliers’ collaborative communication, network governance and consequential relationship longevity with their buyers in a significant way. Overall, the current study findings provided tentative support to the proposition that information technology resources, collaborative communication and network governance should be recognised as significant antecedents for improved relationship longevity between suppliers and their buyers in the SME setting. Therefore, managers in the small and medium enterprise sector and small and medium enterprise owners need to pay attention to both collaborative communication and network governance in order to optimise information technology resource impact on their relationship longevity with their business counterparts. Limitations and future research directions were also indicated.

  4. Articular chondrocyte network mediated by gap junctions: role in metabolic cartilage homeostasis

    Science.gov (United States)

    Mayan, Maria D; Gago-Fuentes, Raquel; Carpintero-Fernandez, Paula; Fernandez-Puente, Patricia; Filgueira-Fernandez, Purificacion; Goyanes, Noa; Valiunas, Virginijus; Brink, Peter R; Goldberg, Gary S; Blanco, Francisco J

    2017-01-01

    Objective This study investigated whether chondrocytes within the cartilage matrix have the capacity to communicate through intercellular connections mediated by voltage-gated gap junction (GJ) channels. Methods Frozen cartilage samples were used for immunofluorescence and immunohistochemistry assays. Samples were embedded in cacodylate buffer before dehydration for scanning electron microscopy. Co-immunoprecipitation experiments and mass spectrometry (MS) were performed to identify proteins that interact with the C-terminal end of Cx43. GJ communication was studied through in situ electroporation, electrophysiology and dye injection experiments. A transwell layered culture system and MS were used to identify and quantify transferred amino acids. Results Microscopic images revealed the presence of multiple cellular projections connecting chondrocytes within the matrix. These projections were between 5 and 150 μm in length. MS data analysis indicated that the C-terminus of Cx43 interacts with several cytoskeletal proteins implicated in Cx trafficking and GJ assembly, including α-tubulin and β-tubulin, actin, and vinculin. Electrophysiology experiments demonstrated that 12-mer oligonucleotides could be transferred between chondrocytes within 12 min after injection. Glucose was homogeneously distributed within 22 and 35 min. No transfer was detected when glucose was electroporated into A549 cells, which have no GJs. Transwell layered culture systems coupled with MS analysis revealed connexins can mediate the transfer of L-lysine and L-arginine between chondrocytes. Conclusions This study reveals that intercellular connections between chondrocytes contain GJs that play a key role in cell-cell communication and a metabolic function by exchange of nutrients including glucose and essential amino acids. A three-dimensional cellular network mediated through GJs might mediate metabolic and physiological homeostasis to maintain cartilage tissue. PMID:24225059

  5. A kinetic model describes metabolic response to perturbations and distribution of flux control in the benzenoid network of Petunia hybrida.

    Science.gov (United States)

    Colón, Amy Marshall; Sengupta, Neelanjan; Rhodes, David; Dudareva, Natalia; Morgan, John

    2010-04-01

    In recent years there has been much interest in the genetic enhancement of plant metabolism; however, attempts at genetic modification are often unsuccessful due to an incomplete understanding of network dynamics and their regulatory properties. Kinetic modeling of plant metabolic networks can provide predictive information on network control and response to genetic perturbations, which allow estimation of flux at any concentration of intermediate or enzyme in the system. In this research, a kinetic model of the benzenoid network was developed to simulate whole network responses to different concentrations of supplied phenylalanine (Phe) in petunia flowers and capture flux redistributions caused by genetic manipulations. Kinetic parameters were obtained by network decomposition and non-linear least squares optimization of data from petunia flowers supplied with either 75 or 150 mm(2)H(5)-Phe. A single set of kinetic parameters simultaneously accommodated labeling and pool size data obtained for all endogenous and emitted volatiles at the two concentrations of supplied (2)H(5)-Phe. The generated kinetic model was validated using flowers from transgenic petunia plants in which benzyl CoA:benzyl alcohol/phenylethanol benzoyltransferase (BPBT) was down-regulated via RNAi. The determined in vivo kinetic parameters were used for metabolic control analysis, in which flux control coefficients were calculated for fluxes around the key branch point at Phe and revealed that phenylacetaldehyde synthase activity is the primary controlling factor for the phenylacetaldehyde branch of the benzenoid network. In contrast, control of flux through the beta-oxidative and non-beta-oxidative pathways is highly distributed.

  6. Creative benefits from well-connected leaders: leader social network ties as facilitators of employee radical creativity.

    Science.gov (United States)

    Venkataramani, Vijaya; Richter, Andreas W; Clarke, Ronald

    2014-09-01

    Employee radical creativity critically depends on substantive informational resources from others across the wider organization. We propose that the social network ties of employees' immediate leaders assume a central role in garnering these resources, thereby fostering their employees' radical creativity both independent of and interactively with employees' own network ties. Drawing on data from 214 employees working in 30 teams of a public technology and environmental services organization, we find that team leaders' betweenness centrality in the idea network within their teams as well as among their peer leaders provides creative benefits beyond employees' own internal and external ties. Further, employees' and leaders' ties within and external to the team interactively predict employee radical creativity. Implications for theory and practice are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  7. AMBIENT: Active Modules for Bipartite Networks--using high-throughput transcriptomic data to dissect metabolic response.

    Science.gov (United States)

    Bryant, William A; Sternberg, Michael J E; Pinney, John W

    2013-03-25

    With the continued proliferation of high-throughput biological experiments, there is a pressing need for tools to integrate the data produced in ways that produce biologically meaningful conclusions. Many microarray studies have analysed transcriptomic data from a pathway perspective, for instance by testing for KEGG pathway enrichment in sets of upregulated genes. However, the increasing availability of species-specific metabolic models provides the opportunity to analyse these data in a more objective, system-wide manner. Here we introduce ambient (Active Modules for Bipartite Networks), a simulated annealing approach to the discovery of metabolic subnetworks (modules) that are significantly affected by a given genetic or environmental change. The metabolic modules returned by ambient are connected parts of the bipartite network that change coherently between conditions, providing a more detailed view of metabolic changes than standard approaches based on pathway enrichment. ambient is an effective and flexible tool for the analysis of high-throughput data in a metabolic context. The same approach can be applied to any system in which reactions (or metabolites) can be assigned a score based on some biological observation, without the limitation of predefined pathways. A Python implementation of ambient is available at http://www.theosysbio.bio.ic.ac.uk/ambient.

  8. Dissecting and engineering metabolic and regulatory networks of thermophilic bacteria for biofuel production.

    Science.gov (United States)

    Lin, Lu; Xu, Jian

    2013-11-01

    Interest in thermophilic bacteria as live-cell catalysts in biofuel and biochemical industry has surged in recent years, due to their tolerance of high temperature and wide spectrum of carbon-sources that include cellulose. However their direct employment as microbial cellular factories in the highly demanding industrial conditions has been hindered by uncompetitive biofuel productivity, relatively low tolerance to solvent and osmic stresses, and limitation in genome engineering tools. In this work we review recent advances in dissecting and engineering the metabolic and regulatory networks of thermophilic bacteria for improving the traits of key interest in biofuel industry: cellulose degradation, pentose-hexose co-utilization, and tolerance of thermal, osmotic, and solvent stresses. Moreover, new technologies enabling more efficient genetic engineering of thermophiles were discussed, such as improved electroporation, ultrasound-mediated DNA delivery, as well as thermo-stable plasmids and functional selection systems. Expanded applications of such technological advancements in thermophilic microbes promise to substantiate a synthetic biology perspective, where functional parts, module, chassis, cells and consortia were modularly designed and rationally assembled for the many missions at industry and nature that demand the extraordinary talents of these extremophiles. Copyright © 2013 Elsevier Inc. All rights reserved.

  9. Stepwise construction of a metabolic network in Event-B: The heat shock response.

    Science.gov (United States)

    Sanwal, Usman; Petre, Luigia; Petre, Ion

    2017-12-01

    There is a high interest in constructing large, detailed computational models for biological processes. This is often done by putting together existing submodels and adding to them extra details/knowledge. The result of such approaches is usually a model that can only answer questions on a very specific level of detail, and thus, ultimately, is of limited use. We focus instead on an approach to systematically add details to a model, with formal verification of its consistency at each step. In this way, one obtains a set of reusable models, at different levels of abstraction, to be used for different purposes depending on the question to address. We demonstrate this approach using Event-B, a computational framework introduced to develop formal specifications of distributed software systems. We first describe how to model generic metabolic networks in Event-B. Then, we apply this method for modeling the biological heat shock response in eukaryotic cells, using Event-B refinement techniques. The advantage of using Event-B consists in having refinement as an intrinsic feature; this provides as a final result not only a correct model, but a chain of models automatically linked by refinement, each of which is provably correct and reusable. This is a proof-of-concept that refinement in Event-B is suitable for biomodeling, serving for mastering biological complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Different biochemical mechanisms ensure network-wide balancing of reducing equivalents in microbial metabolism.

    Science.gov (United States)

    Fuhrer, Tobias; Sauer, Uwe

    2009-04-01

    To sustain growth, the catabolic formation of the redox equivalent NADPH must be balanced with the anabolic demand. The mechanisms that ensure such network-wide balancing, however, are presently not understood. Based on 13C-detected intracellular fluxes, metabolite concentrations, and cofactor specificities for all relevant central metabolic enzymes, we have quantified catabolic NADPH production in Agrobacterium tumefaciens, Bacillus subtilis, Escherichia coli, Paracoccus versutus, Pseudomonas fluorescens, Rhodobacter sphaeroides, Sinorhizobium meliloti, and Zymomonas mobilis. For six species, the estimated NADPH production from glucose catabolism exceeded the requirements for biomass synthesis. Exceptions were P. fluorescens, with balanced rates, and E. coli, with insufficient catabolic production, in which about one-third of the NADPH is supplied via the membrane-bound transhydrogenase PntAB. P. versutus and B. subtilis were the only species that appear to rely on transhydrogenases for balancing NADPH overproduction during growth on glucose. In the other four species, the main but not exclusive redox-balancing mechanism appears to be the dual cofactor specificities of several catabolic enzymes and/or the existence of isoenzymes with distinct cofactor specificities, in particular glucose 6-phosphate dehydrogenase. An unexpected key finding for all species, except E. coli and B. subtilis, was the lack of cofactor specificity in the oxidative pentose phosphate pathway, which contrasts with the textbook view of the pentose phosphate pathway dehydrogenases as being NADP+ dependent.

  11. Metabolic network analysis on Phaffia rhodozyma yeast using C-13-labeled glucose and gas chromatography-mass spectrometry

    DEFF Research Database (Denmark)

    Cannizzaro, C.; Christensen, B.; Nielsen, Jens

    2004-01-01

    labeling patterns, as determined by GC-MS, were in accordance with a metabolic network consisting of the Embden-Meyerhof-Parnas pathway, the pentose phosphate pathway, and the TCA cycle. Glucose was mainly consumed along the pentose phosphate pathway (similar to65% for wildtype strain), which reflected...... to that of the wildtype strain, though the relative pentose phosphate flux was lower and the TCA cycle flux in accordance with the biomass yield being lower....

  12. Effects of Creatine Monohydrate Augmentation on Brain Metabolic and Network Outcome Measures in Women With Major Depressive Disorder.

    Science.gov (United States)

    Yoon, Sujung; Kim, Jieun E; Hwang, Jaeuk; Kim, Tae-Suk; Kang, Hee Jin; Namgung, Eun; Ban, Soonhyun; Oh, Subin; Yang, Jeongwon; Renshaw, Perry F; Lyoo, In Kyoon

    2016-09-15

    Creatine monohydrate (creatine) augmentation has the potential to accelerate the clinical responses to and enhance the overall efficacy of selective serotonin reuptake inhibitor treatment in women with major depressive disorder (MDD). Although it has been suggested that creatine augmentation may involve the restoration of brain energy metabolism, the mechanisms underlying its antidepressant efficacy are unknown. In a randomized, double-blind, placebo-controlled trial, 52 women with MDD were assigned to receive either creatine augmentation or placebo augmentation of escitalopram; 34 subjects participated in multimodal neuroimaging assessments at baseline and week 8. Age-matched healthy women (n = 39) were also assessed twice at the same intervals. Metabolic and network outcomes were measured for changes in prefrontal N-acetylaspartate and changes in rich club hub connections of the structural brain network using proton magnetic resonance spectroscopy and diffusion tensor imaging, respectively. We found MDD-related metabolic and network dysfunction at baseline. Improvement in depressive symptoms was greater in patients receiving creatine augmentation relative to placebo augmentation. After 8 weeks of treatment, prefrontal N-acetylaspartate levels increased significantly in the creatine augmentation group compared with the placebo augmentation group. Increment in rich club hub connections was also greater in the creatine augmentation group than in the placebo augmentation group. N-acetylaspartate levels and rich club connections increased after creatine augmentation of selective serotonin reuptake inhibitor treatment. Effects of creatine administration on brain energy metabolism and network organization may partly underlie its efficacy in treating women with MDD. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  13. Construction of a genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum provides new strategies for bactericide discovery.

    Science.gov (United States)

    Wang, Cheng; Deng, Zhi-Luo; Xie, Zhi-Ming; Chu, Xin-Yi; Chang, Ji-Wei; Kong, De-Xin; Li, Bao-Ju; Zhang, Hong-Yu; Chen, Ling-Ling

    2015-01-30

    We reconstructed the first genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum subsp. carotovorum PC1 based on its genomic sequence, annotation, and physiological data. Metabolic characteristics were analyzed using flux balance analysis (FBA), and the results were afterwards validated by phenotype microarray (PM) experiments. The reconstructed genome-scale metabolic model, iPC1209, contains 2235 reactions, 1113 metabolites and 1209 genes. We identified 19 potential bactericide targets through a comprehensive in silico gene-deletion study. Next, we performed virtual screening to identify candidate inhibitors for an important potential drug target, alkaline phosphatase, and experimentally verified that three lead compounds were able to inhibit both bacterial cell viability and the activity of alkaline phosphatase in vitro. This study illustrates a new strategy for the discovery of agricultural bactericides. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  14. Metabolic network analysis on Phaffia rhodozyma yeast using C-13-labeled glucose and gas chromatography-mass spectrometry

    DEFF Research Database (Denmark)

    Cannizzaro, C.; Christensen, B.; Nielsen, Jens

    2004-01-01

    high NADPH requirements for lipid biosynthesis. Although common to other oleaginous yeast, there was no, or very little, malic enzyme activity for carbon-limited growth. In addition, there was no evidence of phosphoketolase activity. The central carbon metabolism of the mutant strain was similar......Carotenoid production by micro organisms, as opposed to chemical synthesis, could fulfill an ever-increasing demand for 'all natural' products. The yeast Phaffia rhodozyma has received considerable attention because it produces the red pigment astaxanthin, commonly used as an animal feed supplement....... In order to have a better understanding of its metabolism, labeling experiments with [1-C-13]glucose were conducted with the wildtype strain (CBS5905T) and a hyper-producing carotenoid strain (J4-3) in order to determine their metabolic network structure and estimate intracellular fluxes. Amino acid...

  15. MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks.

    Science.gov (United States)

    Kelley, James J; Maor, Shay; Kim, Min Kyung; Lane, Anatoliy; Lun, Desmond S

    2017-08-15

    Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: (i) automation, since GEMs can be quite large; (ii) production of understandable maps that provide ease in identification of pathways, reactions and metabolites; and (iii) visualization of the entire network to show how pathways are interconnected. No software currently exists for visualizing GEMs that satisfies all three characteristics, but MOST-Visualization, an extension of the software package MOST (Metabolic Optimization and Simulation Tool), satisfies (i), and by using a pre-drawn overview map of metabolism based on the Roche map satisfies (ii) and comes close to satisfying (iii). MOST is distributed for free on the GNU General Public License. The software and full documentation are available at http://most.ccib.rutgers.edu/. dslun@rutgers.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  16. Dissimilatory Metabolism of Nitrogen Oxides in Bacteria:Comparative Reconstruction of Transcriptional Networks

    Energy Technology Data Exchange (ETDEWEB)

    Rodionov, Dmitry A.; Dubchak, Inna L.; Arkin, Adam P.; Alm, EricJ.; Gelfand, Mikhail S.

    2005-09-01

    Bacterial response to nitric oxide (NO) is of major importance since NO is an obligatory intermediate of the nitrogen cycle. Transcriptional regulation of the dissimilatory nitric oxides metabolism in bacteria is diverse and involves FNR-like transcription factors HcpR, DNR and NnrR, two-component systems NarXL and NarQP, NO-responsive activator NorR, and nitrite sensitive repressor NsrR. Using comparative genomics approaches we predict DNA-binding signals for these transcriptional factors and describe corresponding regulons in available bacterial genomes. Within the FNR family of regulators, we observed a correlation of two specificity-determining amino acids and contacting bases in corresponding DNA signal. Highly conserved regulon HcpR for the hybrid cluster protein and some other redox enzymes is present in diverse anaerobic bacteria including Clostridia, Thermotogales and delta-proteobacteria. NnrR and DNR control denitrification in alpha- and beta-proteobacteria, respectively. Sigma-54-dependent NorR regulon found in some gamma- and beta-proteobacteria contains various enzymes involved in the NO detoxification. Repressor NsrR, which was previously known to control only nitrite reductase operon in Nitrosomonas spp., appears to be the master regulator of the nitric oxides metabolism not only in most gamma- and beta-proteobacteria (including well-studied species like Escherichia coli), but also in Gram-positive Bacillus and Streptomyces species. Positional analysis and comparison of regulatory regions of NO detoxification genes allows us to propose the candidate NsrR-binding signal. The most conserved member of the predicted NsrR regulon is the NO-detoxifying flavohemoglobin Hmp. In enterobacteria, the regulon includes also two nitrite-responsive loci, nipAB (hcp-hcr) and nipC(dnrN), thus confirming the identity of the effector, i.e., nitrite. The proposed NsrR regulons in Neisseria and some other species are extended to include denitrification genes. As the

  17. Dissimilatory metabolism of nitrogen oxides in bacteria: comparative reconstruction of transcriptional networks.

    Directory of Open Access Journals (Sweden)

    2005-10-01

    Full Text Available Bacterial response to nitric oxide (NO is of major importance since NO is an obligatory intermediate of the nitrogen cycle. Transcriptional regulation of the dissimilatory nitric oxides metabolism in bacteria is diverse and involves FNR-like transcription factors HcpR, DNR, and NnrR; two-component systems NarXL and NarQP; NO-responsive activator NorR; and nitrite-sensitive repressor NsrR. Using comparative genomics approaches, we predict DNA-binding motifs for these transcriptional factors and describe corresponding regulons in available bacterial genomes. Within the FNR family of regulators, we observed a correlation of two specificity-determining amino acids and contacting bases in corresponding DNA recognition motif. Highly conserved regulon HcpR for the hybrid cluster protein and some other redox enzymes is present in diverse anaerobic bacteria, including Clostridia, Thermotogales, and delta-proteobacteria. NnrR and DNR control denitrification in alpha- and beta-proteobacteria, respectively. Sigma-54-dependent NorR regulon found in some gamma- and beta-proteobacteria contains various enzymes involved in the NO detoxification. Repressor NsrR, which was previously known to control only nitrite reductase operon in Nitrosomonas spp., appears to be the master regulator of the nitric oxides' metabolism, not only in most gamma- and beta-proteobacteria (including well-studied species such as Escherichia coli, but also in Gram-positive Bacillus and Streptomyces species. Positional analysis and comparison of regulatory regions of NO detoxification genes allows us to propose the candidate NsrR-binding motif. The most conserved member of the predicted NsrR regulon is the NO-detoxifying flavohemoglobin Hmp. In enterobacteria, the regulon also includes two nitrite-responsive loci, nipAB (hcp-hcr and nipC (dnrN, thus confirming the identity of the effector, i.e. nitrite. The proposed NsrR regulons in Neisseria and some other species are extended to include

  18. Mapping the brain's orchestration during speech comprehension: task-specific facilitation of regional synchrony in neural networks

    Directory of Open Access Journals (Sweden)

    Keil Andreas

    2004-10-01

    Full Text Available Abstract Background How does the brain convert sounds and phonemes into comprehensible speech? In the present magnetoencephalographic study we examined the hypothesis that the coherence of electromagnetic oscillatory activity within and across brain areas indicates neurophysiological processes linked to speech comprehension. Results Amplitude-modulated (sinusoidal 41.5 Hz auditory verbal and nonverbal stimuli served to drive steady-state oscillations in neural networks involved in speech comprehension. Stimuli were presented to 12 subjects in the following conditions (a an incomprehensible string of words, (b the same string of words after being introduced as a comprehensible sentence by proper articulation, and (c nonverbal stimulations that included a 600-Hz tone, a scale, and a melody. Coherence, defined as correlated activation of magnetic steady state fields across brain areas and measured as simultaneous activation of current dipoles in source space (Minimum-Norm-Estimates, increased within left- temporal-posterior areas when the sound string was perceived as a comprehensible sentence. Intra-hemispheric coherence was larger within the left than the right hemisphere for the sentence (condition (b relative to all other conditions, and tended to be larger within the right than the left hemisphere for nonverbal stimuli (condition (c, tone and melody relative to the other conditions, leading to a more pronounced hemispheric asymmetry for nonverbal than verbal material. Conclusions We conclude that coherent neuronal network activity may index encoding of verbal information on the sentence level and can be used as a tool to investigate auditory speech comprehension.

  19. Thermodynamic analysis of computed pathways integrated into the metabolic networks of E. coli and Synechocystis reveals contrasting expansion potential.

    Science.gov (United States)

    Asplund-Samuelsson, Johannes; Janasch, Markus; Hudson, Elton P

    2018-01-01

    Introducing biosynthetic pathways into an organism is both reliant on and challenged by endogenous biochemistry. Here we compared the expansion potential of the metabolic network in the photoautotroph Synechocystis with that of the heterotroph E. coli using the novel workflow POPPY (Prospecting Optimal Pathways with PYthon). First, E. coli and Synechocystis metabolomic and fluxomic data were combined with metabolic models to identify thermodynamic constraints on metabolite concentrations (NET analysis). Then, thousands of automatically constructed pathways were placed within each network and subjected to a network-embedded variant of the max-min driving force analysis (NEM). We found that the networks had different capabilities for imparting thermodynamic driving forces toward certain compounds. Key metabolites were constrained differently in Synechocystis due to opposing flux directions in glycolysis and carbon fixation, the forked tri-carboxylic acid cycle, and photorespiration. Furthermore, the lysine biosynthesis pathway in Synechocystis was identified as thermodynamically constrained, impacting both endogenous and heterologous reactions through low 2-oxoglutarate levels. Our study also identified important yet poorly covered areas in existing metabolomics data and provides a reference for future thermodynamics-based engineering in Synechocystis and beyond. The POPPY methodology represents a step in making optimal pathway-host matches, which is likely to become important as the practical range of host organisms is diversified. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  20. Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria.

    Science.gov (United States)

    Hernández-Prieto, Miguel A; Semeniuk, Trudi A; Futschik, Matthias E

    2014-01-01

    Cyanobacteria are essential primary producers in marine ecosystems, playing an important role in both carbon and nitrogen cycles. In the last decade, various genome sequencing and metagenomic projects have generated large amounts of genetic data for cyanobacteria. This wealth of data provides researchers with a new basis for the study of molecular adaptation, ecology and evolution of cyanobacteria, as well as for developing biotechnological applications. It also facilitates the use of multiplex techniques, i.e., expression profiling by high-throughput technologies such as microarrays, RNA-seq, and proteomics. However, exploration and analysis of these data is challenging, and often requires advanced computational methods. Also, they need to be integrated into our existing framework of knowledge to use them to draw reliable biological conclusions. Here, systems biology provides important tools. Especially, the construction and analysis of molecular networks has emerged as a powerful systems-level framework, with which to integrate such data, and to better understand biological relevant processes in these organisms. In this review, we provide an overview of the advances and experimental approaches undertaken using multiplex data from genomic, transcriptomic, proteomic, and metabolomic studies in cyanobacteria. Furthermore, we summarize currently available web-based tools dedicated to cyanobacteria, i.e., CyanoBase, CyanoEXpress, ProPortal, Cyanorak, CyanoBIKE, and CINPER. Finally, we present a case study for the freshwater model cyanobacteria, Synechocystis sp. PCC6803, to show the power of meta-analysis, and the potential to extrapolate acquired knowledge to the ecologically important marine cyanobacteria genus, Prochlorococcus.

  1. Robust Regression Analysis of GCMS Data Reveals Differential Rewiring of Metabolic Networks in Hepatitis B and C Patients

    Directory of Open Access Journals (Sweden)

    Cedric Simillion

    2017-10-01

    Full Text Available About one in 15 of the world’s population is chronically infected with either hepatitis virus B (HBV or C (HCV, with enormous public health consequences. The metabolic alterations caused by these infections have never been directly compared and contrasted. We investigated groups of HBV-positive, HCV-positive, and uninfected healthy controls using gas chromatography-mass spectrometry analyses of their plasma and urine. A robust regression analysis of the metabolite data was conducted to reveal correlations between metabolite pairs. Ten metabolite correlations appeared for HBV plasma and urine, with 18 for HCV plasma and urine, none of which were present in the controls. Metabolic perturbation networks were constructed, which permitted a differential view of the HBV- and HCV-infected liver. HBV hepatitis was consistent with enhanced glucose uptake, glycolysis, and pentose phosphate pathway metabolism, the latter using xylitol and producing threonic acid, which may also be imported by glucose transporters. HCV hepatitis was consistent with impaired glucose uptake, glycolysis, and pentose phosphate pathway metabolism, with the tricarboxylic acid pathway fueled by branched-chain amino acids feeding gluconeogenesis and the hepatocellular loss of glucose, which most probably contributed to hyperglycemia. It is concluded that robust regression analyses can uncover metabolic rewiring in disease states.

  2. The origin of modern metabolic networks inferred from phylogenomic analysis of protein architecture.

    Science.gov (United States)

    Caetano-Anollés, Gustavo; Kim, Hee Shin; Mittenthal, Jay E

    2007-05-29

    Metabolism represents a complex collection of enzymatic reactions and transport processes that convert metabolites into molecules capable of supporting cellular life. Here we explore the origins and evolution of modern metabolism. Using phylogenomic information linked to the structure of metabolic enzymes, we sort out recruitment processes and discover that most enzymatic activities were associated with the nine most ancient and widely distributed protein fold architectures. An analysis of newly discovered functions showed enzymatic diversification occurred early, during the onset of the modern protein world. Most importantly, phylogenetic reconstruction exercises and other evidence suggest strongly that metabolism originated in enzymes with the P-loop hydrolase fold in nucleotide metabolism, probably in pathways linked to the purine metabolic subnetwork. Consequently, the first enzymatic takeover of an ancient biochemistry or prebiotic chemistry was related to the synthesis of nucleotides for the RNA world.

  3. Genome-scale reconstruction of the metabolic network in Yersinia pestis CO92

    Science.gov (United States)

    Navid, Ali; Almaas, Eivind

    2007-03-01

    The gram-negative bacterium Yersinia pestis is the causative agent of bubonic plague. Using publicly available genomic, biochemical and physiological data, we have developed a constraint-based flux balance model of metabolism in the CO92 strain (biovar Orientalis) of this organism. The metabolic reactions were appropriately compartmentalized, and the model accounts for the exchange of metabolites, as well as the import of nutrients and export of waste products. We have characterized the metabolic capabilities and phenotypes of this organism, after comparing the model predictions with available experimental observations to evaluate accuracy and completeness. We have also begun preliminary studies into how cellular metabolism affects virulence.

  4. Addressing unknown constants and metabolic network behaviors through petascale computing: understanding H2 production in green algae

    International Nuclear Information System (INIS)

    Chang, Christopher; Alber, David; Graf, Peter; Kim, Kwiseon; Seibert, Michael

    2007-01-01

    The Genomics Revolution has resulted in a massive and growing quantity of whole-genome DNA sequences, which encode the metabolic catalysts necessary for life. However, gene annotations can rarely be complete, and measurement of the kinetic constants associated with the encoded enzymes can not possibly keep pace, necessitating the use of careful modeling to explore plausible network behaviors. Key challenges are (1) quantitatively formulating kinetic laws governing each transformation in a fixed model network; (2) characterizing the stable solution (if any) of the associated ordinary differential equations (ODEs); (3) fitting the latter to metabolomics data as it becomes available; and (4) optimizing a model output against the possible space of kinetic parameters, with respect to properties such as robustness of network response, or maximum consumption/production. This SciDAC-2 project addresses this large-scale uncertainty in the genome-scale metabolic network of the water-splitting, H 2 -producing green alga Chlamydomonas reinhardtii. Each metabolic transformation is formulated as an irreversible steady-state process, such that the vast literature on known enzyme mechanisms may be incorporated directly. To start, glycolysis, the tricarboxylic acid cycle, and basic fermentation pathways have been encoded in Systems Biology Markup Language (SBML) with careful annotation and consistency with the KEGG database, yielding a model with 3 compartments, 95 species, 38 reactions, and 109 kinetic constants. To study and optimize such models with a view toward larger models, we have developed a system which takes as input an SBML model, and automatically produces C code that when compiled and executed optimizes the model's kinetic parameters according to test criteria. We describe the system and present numerical results. Further development, including overlaying of a parallel multistart algorithm, will allow optimization of thousands of parameters on high-performance systems

  5. Implementing a web-based home monitoring system within an academic health care network: barriers and facilitators to innovation diffusion.

    Science.gov (United States)

    Pelletier, Alexandra C; Jethwani, Kamal; Bello, Heather; Kvedar, Joseph; Grant, Richard W

    2011-01-01

    The practice of outpatient type 2 diabetes management is gradually moving from the traditional visit-based, fee-for-service model to a new, health information communication technology (ICT)-supported model that can enable non-visit-based diabetes care. To date, adoption of innovative health ICT tools for diabetes management has been slowed by numerous barriers, such as capital investment costs, lack of reliable reimbursement mechanisms, design defects that have made some systems time-consuming and inefficient to use, and the need to integrate new ICT tools into a system not primarily designed for their use. Effective implementation of innovative diabetes health ICT interventions must address local practice heterogeneity and the interaction of this heterogeneity with clinical care delivery. The Center for Connected Health at Partners Healthcare has implemented a new ICT intervention, Diabetes Connect (DC), a Web-based glucose home monitoring and clinical messaging system. Using the framework of the diffusion of innovation theory, we review the implementation and examine lessons learned as we continue to deploy DC across the health care network. © 2010 Diabetes Technology Society.

  6. Loss of variation of state detected in soybean metabolic and human myelomonocytic leukaemia cell transcriptional networks under external stimuli

    KAUST Repository

    Sakata, Katsumi

    2016-10-24

    Soybean (Glycine max) is sensitive to flooding stress, and flood damage at the seedling stage is a barrier to growth. We constructed two mathematical models of the soybean metabolic network, a control model and a flooded model, from metabolic profiles in soybean plants. We simulated the metabolic profiles with perturbations before and after the flooding stimulus using the two models. We measured the variation of state that the system could maintain from a state–space description of the simulated profiles. The results showed a loss of variation of state during the flooding response in the soybean plants. Loss of variation of state was also observed in a human myelomonocytic leukaemia cell transcriptional network in response to a phorbol-ester stimulus. Thus, we detected a loss of variation of state under external stimuli in two biological systems, regardless of the regulation and stimulus types. Our results suggest that a loss of robustness may occur concurrently with the loss of variation of state in biological systems. We describe the possible applications of the quantity of variation of state in plant genetic engineering and cell biology. Finally, we present a hypothetical “external stimulus-induced information loss” model of biological systems.

  7. Barriers, Facilitators and Priorities for Implementation of WHO Maternal and Perinatal Health Guidelines in Four Lower-Income Countries: A GREAT Network Research Activity.

    Directory of Open Access Journals (Sweden)

    Joshua P Vogel

    Full Text Available Health systems often fail to use evidence in clinical practice. In maternal and perinatal health, the majority of maternal, fetal and newborn mortality is preventable through implementing effective interventions. To meet this challenge, WHO's Department of Reproductive Health and Research partnered with the Knowledge Translation Program at St. Michael's Hospital (SMH, University of Toronto, Canada to establish a collaboration on knowledge translation (KT in maternal and perinatal health, called the GREAT Network (Guideline-driven, Research priorities, Evidence synthesis, Application of evidence, and Transfer of knowledge. We applied a systematic approach incorporating evidence and theory to identifying barriers and facilitators to implementation of WHO maternal heath recommendations in four lower-income countries and to identifying implementation strategies to address these.We conducted a mixed-methods study in Myanmar, Uganda, Tanzania and Ethiopia. In each country, stakeholder surveys, focus group discussions and prioritization exercises were used, involving multiple groups of health system stakeholders (including administrators, policymakers, NGOs, professional associations, frontline healthcare providers and researchers.Despite differences in guideline priorities and contexts, barriers identified across countries were often similar. Health system level factors, including health workforce shortages, and need for strengthened drug and equipment procurement, distribution and management systems, were consistently highlighted as limiting the capacity of providers to deliver high-quality care. Evidence-based health policies to support implementation, and improve the knowledge and skills of healthcare providers were also identified. Stakeholders identified a range of tailored strategies to address local barriers and leverage facilitators.This approach to identifying barriers, facilitators and potential strategies for improving implementation proved

  8. Regulatory network of secondary metabolism in Brassica rapa : insight into the glucosinolate pathway

    NARCIS (Netherlands)

    Pino Del Carpio, Dunia; Basnet, Ram Kumar; Arends, Danny; Lin, Ke; De Vos, Ric C H; Muth, Dorota; Kodde, Jan; Boutilier, Kim; Bucher, Johan; Wang, Xiaowu; Jansen, Ritsert; Bonnema, Guusje

    2014-01-01

    Brassica rapa studies towards metabolic variation have largely been focused on the profiling of the diversity of metabolic compounds in specific crop types or regional varieties, but none aimed to identify genes with regulatory function in metabolite composition. Here we followed a genetical

  9. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders

    OpenAIRE

    Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M.; Zhu, Jun

    2016-01-01

    Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient–host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcri...

  10. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

    DEFF Research Database (Denmark)

    Bordbar, Aarash; Yurkovich, James T.; Paglia, Giuseppe

    2017-01-01

    metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C...

  11. In Silico Genome-Scale Reconstruction and Validation of the Staphylococcus aureus Metabolic Network

    NARCIS (Netherlands)

    Heinemann, Matthias; Kümmel, Anne; Ruinatscha, Reto; Panke, Sven

    2005-01-01

    A genome-scale metabolic model of the Gram-positive, facultative anaerobic opportunistic pathogen Staphylococcus aureus N315 was constructed based on current genomic data, literature, and physiological information. The model comprises 774 metabolic processes representing approximately 23% of all

  12. Gene regulatory networking reveals the molecular cue to lysophosphatidic acid-induced metabolic adaptations in ovarian cancer cells.

    Science.gov (United States)

    Ray, Upasana; Roy Chowdhury, Shreya; Vasudevan, Madavan; Bankar, Kiran; Roychoudhury, Susanta; Roy, Sib Sankar

    2017-05-01

    Extravasation and metastatic progression are two main reasons for the high mortality rate associated with cancer. The metastatic potential of cancer cells depends on a plethora of metabolic challenges prevailing within the tumor microenvironment. To achieve higher rates of proliferation, cancer cells reprogram their metabolism, increasing glycolysis and biosynthetic activities. Just why this metabolic reprogramming predisposes cells towards increased oncogenesis remains elusive. The accumulation of myriad oncolipids in the tumor microenvironment has been shown to promote the invasiveness of cancer cells, with lysophosphatidic acid (LPA) being one such critical factor enriched in ovarian cancer patients. Cellular bioenergetic studies confirm that oxidative phosphorylation is suppressed and glycolysis is increased with long exposure to LPA in ovarian cancer cells compared with non-transformed epithelial cells. We sought to uncover the regulatory complexity underlying this oncolipid-induced metabolic perturbation. Gene regulatory networking using RNA-Seq analysis identified the oncogene ETS-1 as a critical mediator of LPA-induced metabolic alterations for the maintenance of invasive phenotype. Moreover, LPA receptor-2 specific PtdIns3K-AKT signaling induces ETS-1 and its target matrix metalloproteases. Abrogation of ETS-1 restores cellular bioenergetics towards increased oxidative phosphorylation and reduced glycolysis, and this effect was reversed by the presence of LPA. Furthermore, the bioenergetic status of LPA-treated ovarian cancer cells mimics hypoxia through induction of hypoxia-inducible factor-1α, which was found to transactivate ets-1. Studies in primary tumors generated in syngeneic mice corroborated the in vitro findings. Thus, our study highlights the phenotypic changes induced by the pro-metastatic factor ETS-1 in ovarian cancer cells. The relationship between enhanced invasiveness and metabolic plasticity further illustrates the critical role of

  13. Using isotopic tracers to assess the impact of tillage and straw management on the microbial metabolic network in soil

    Science.gov (United States)

    Van Groenigen, K.; Forristal, D.; Jones, M. B.; Schwartz, E.; Hungate, B. A.; Dijkstra, P.

    2013-12-01

    By decomposing soil organic matter, microbes gain energy and building blocks for biosynthesis and release CO2 to the atmosphere. Therefore, insight into the effect of management practices on microbial metabolic pathways and C use efficiency (CUE; microbial C produced per substrate C utilized) may help to predict long term changes in soil C stocks. We studied the effects of reduced (RT) and conventional tillage (CT) on the microbial central C metabolic network, using soil samples from a 12-year-old field experiment in an Irish winter wheat cropping system. Each year after harvest, straw was removed from half of the RT and CT plots or incorporated into the soil in the other half, resulting in four treatment combinations. We added 1-13C and 2,3-13C pyruvate and 1-13C and U-13C glucose as metabolic tracer isotopomers to composite soil samples taken at two depths (0-15 cm and 15-30 cm) from each treatment and used the rate of position-specific respired 13CO2 to parameterize a metabolic model. Model outcomes were then used to calculate CUE of the microbial community. We found that the composite samples differed in CUE, but the changes were small, with values ranging between 0.757-0.783 across treatments and soil depth. Increases in CUE were associated with a decrease in tricarboxylic acid cycle and reductive pentose phosphate pathway activity and increased consumption of metabolic intermediates for biosynthesis. Our results indicate that RT and straw incorporation promote soil C storage without substantially changing CUE or any of the microbial metabolic pathways. This suggests that at our site, RT and straw incorporation promote soil C storage mostly through direct effects such as increased soil C input and physical protection from decomposition, rather than by feedback responses of the microbial community.

  14. Cholesteryl ester transfer protein alters liver and plasma triglyceride metabolism through two liver networks in female mice[S

    Science.gov (United States)

    Palmisano, Brian T.; Le, Thao D.; Zhu, Lin; Lee, Yoon Kwang; Stafford, John M.

    2016-01-01

    Elevated plasma TGs increase risk of cardiovascular disease in women. Estrogen treatment raises plasma TGs in women, but molecular mechanisms remain poorly understood. Here we explore the role of cholesteryl ester transfer protein (CETP) in the regulation of TG metabolism in female mice, which naturally lack CETP. In transgenic CETP females, acute estrogen treatment raised plasma TGs 50%, increased TG production, and increased expression of genes involved in VLDL synthesis, but not in nontransgenic littermate females. In CETP females, estrogen enhanced expression of small heterodimer partner (SHP), a nuclear receptor regulating VLDL production. Deletion of liver SHP prevented increases in TG production and expression of genes involved in VLDL synthesis in CETP mice with estrogen treatment. We also examined whether CETP expression had effects on TG metabolism independent of estrogen treatment. CETP increased liver β-oxidation and reduced liver TG content by 60%. Liver estrogen receptor α (ERα) was required for CETP expression to enhance β-oxidation and reduce liver TG content. Thus, CETP alters at least two networks governing TG metabolism, one involving SHP to increase VLDL-TG production in response to estrogen, and another involving ERα to enhance β-oxidation and lower liver TG content. These findings demonstrate a novel role for CETP in estrogen-mediated increases in TG production and a broader role for CETP in TG metabolism. PMID:27354419

  15. Cholesteryl ester transfer protein alters liver and plasma triglyceride metabolism through two liver networks in female mice.

    Science.gov (United States)

    Palmisano, Brian T; Le, Thao D; Zhu, Lin; Lee, Yoon Kwang; Stafford, John M

    2016-08-01

    Elevated plasma TGs increase risk of cardiovascular disease in women. Estrogen treatment raises plasma TGs in women, but molecular mechanisms remain poorly understood. Here we explore the role of cholesteryl ester transfer protein (CETP) in the regulation of TG metabolism in female mice, which naturally lack CETP. In transgenic CETP females, acute estrogen treatment raised plasma TGs 50%, increased TG production, and increased expression of genes involved in VLDL synthesis, but not in nontransgenic littermate females. In CETP females, estrogen enhanced expression of small heterodimer partner (SHP), a nuclear receptor regulating VLDL production. Deletion of liver SHP prevented increases in TG production and expression of genes involved in VLDL synthesis in CETP mice with estrogen treatment. We also examined whether CETP expression had effects on TG metabolism independent of estrogen treatment. CETP increased liver β-oxidation and reduced liver TG content by 60%. Liver estrogen receptor α (ERα) was required for CETP expression to enhance β-oxidation and reduce liver TG content. Thus, CETP alters at least two networks governing TG metabolism, one involving SHP to increase VLDL-TG production in response to estrogen, and another involving ERα to enhance β-oxidation and lower liver TG content. These findings demonstrate a novel role for CETP in estrogen-mediated increases in TG production and a broader role for CETP in TG metabolism. Copyright © 2016 by the American Society for Biochemistry and Molecular Biology, Inc.

  16. Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

    Science.gov (United States)

    Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; Overall, Christopher C.; Hill, Eric A.; Beliaev, Alexander S.

    2015-01-01

    Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as “topologically important.” Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as “functionally important” genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles. PMID:25826650

  17. Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

    Directory of Open Access Journals (Sweden)

    Hyun-Seob Song

    2015-03-01

    Full Text Available Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as “topologically important.” Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as “functionally important” genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.

  18. Drought, salt, and temperature stress-induced metabolic rearrangements and regulatory networks

    Science.gov (United States)

    Krasensky, Julia; Jonak, Claudia

    2015-01-01

    Plants regularly face adverse growth conditions, such as drought, salinity, chilling, freezing, and high temperatures. These stresses can delay growth and development, reduce productivity, and, in extreme cases, cause plant death. Plant stress responses are dynamic and involve complex cross-talk between different regulatory levels, including adjustment of metabolism and gene expression for physiological and morphological adaptation. In this review, information about metabolic regulation in response to drought, extreme temperature, and salinity stress is summarized and the signalling events involved in mediating stress-induced metabolic changes are presented. PMID:22291134

  19. Drought, salt, and temperature stress-induced metabolic rearrangements and regulatory networks.

    Science.gov (United States)

    Krasensky, Julia; Jonak, Claudia

    2012-02-01

    Plants regularly face adverse growth conditions, such as drought, salinity, chilling, freezing, and high temperatures. These stresses can delay growth and development, reduce productivity, and, in extreme cases, cause plant death. Plant stress responses are dynamic and involve complex cross-talk between different regulatory levels, including adjustment of metabolism and gene expression for physiological and morphological adaptation. In this review, information about metabolic regulation in response to drought, extreme temperature, and salinity stress is summarized and the signalling events involved in mediating stress-induced metabolic changes are presented.

  20. Integrating large-scale functional genomics data to dissect metabolic networks for hydrogen production

    Energy Technology Data Exchange (ETDEWEB)

    Harwood, Caroline S

    2012-12-17

    The goal of this project is to identify gene networks that are critical for efficient biohydrogen production by leveraging variation in gene content and gene expression in independently isolated Rhodopseudomonas palustris strains. Coexpression methods were applied to large data sets that we have collected to define probabilistic causal gene networks. To our knowledge this a first systems level approach that takes advantage of strain-to strain variability to computationally define networks critical for a particular bacterial phenotypic trait.

  1. A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks.

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    Hojung Nam

    2014-09-01

    Full Text Available Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH, succinate dehydrogenase (SDH, and fumarate hydratase (FH that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes, expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.

  2. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

    NARCIS (Netherlands)

    He, F.; Murabito, E.; Westerhoff, H.V.

    2016-01-01

    Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out throughin silicotheoretical studies with the aim to guide and complement furtherin vitroandin vivoexperimental

  3. Metabolic network modeling of redox balancing and biohydrogen production in purple nonsulfur bacteria

    Directory of Open Access Journals (Sweden)

    Grammel Hartmut

    2011-09-01

    Full Text Available Abstract Background Purple nonsulfur bacteria (PNSB are facultative photosynthetic bacteria and exhibit an extremely versatile metabolism. A central focus of research on PNSB dealt with the elucidation of mechanisms by which they manage to balance cellular redox under diverse conditions, in particular under photoheterotrophic growth. Results Given the complexity of the central metabolism of PNSB, metabolic modeling becomes crucial for an integrated analysis of the accumulated biological knowledge. We reconstructed a stoichiometric model capturing the central metabolism of three important representatives of PNSB (Rhodospirillum rubrum, Rhodobacter sphaeroides and Rhodopseudomonas palustris. Using flux variability analysis, the model reveals key metabolic constraints related to redox homeostasis in these bacteria. With the help of the model we can (i give quantitative explanations for non-intuitive, partially species-specific phenomena of photoheterotrophic growth of PNSB, (ii reproduce various quantitative experimental data, and (iii formulate several new hypotheses. For example, model analysis of photoheterotrophic growth reveals that - despite a large number of utilizable catabolic pathways - substrate-specific biomass and CO2 yields are fixed constraints, irrespective of the assumption of optimal growth. Furthermore, our model explains quantitatively why a CO2 fixing pathway such as the Calvin cycle is required by PNSB for many substrates (even if CO2 is released. We also analyze the role of other pathways potentially involved in redox metabolism and how they affect quantitatively the required capacity of the Calvin cycle. Our model also enables us to discriminate between different acetate assimilation pathways that were proposed recently for R. sphaeroides and R. rubrum, both lacking the isocitrate lyase. Finally, we demonstrate the value of the metabolic model also for potential biotechnological applications: we examine the theoretical

  4. Metabolic network modeling of redox balancing and biohydrogen production in purple nonsulfur bacteria.

    Science.gov (United States)

    Hädicke, Oliver; Grammel, Hartmut; Klamt, Steffen

    2011-09-25

    Purple nonsulfur bacteria (PNSB) are facultative photosynthetic bacteria and exhibit an extremely versatile metabolism. A central focus of research on PNSB dealt with the elucidation of mechanisms by which they manage to balance cellular redox under diverse conditions, in particular under photoheterotrophic growth. Given the complexity of the central metabolism of PNSB, metabolic modeling becomes crucial for an integrated analysis of the accumulated biological knowledge. We reconstructed a stoichiometric model capturing the central metabolism of three important representatives of PNSB (Rhodospirillum rubrum, Rhodobacter sphaeroides and Rhodopseudomonas palustris). Using flux variability analysis, the model reveals key metabolic constraints related to redox homeostasis in these bacteria. With the help of the model we can (i) give quantitative explanations for non-intuitive, partially species-specific phenomena of photoheterotrophic growth of PNSB, (ii) reproduce various quantitative experimental data, and (iii) formulate several new hypotheses. For example, model analysis of photoheterotrophic growth reveals that--despite a large number of utilizable catabolic pathways--substrate-specific biomass and CO₂ yields are fixed constraints, irrespective of the assumption of optimal growth. Furthermore, our model explains quantitatively why a CO₂ fixing pathway such as the Calvin cycle is required by PNSB for many substrates (even if CO₂ is released). We also analyze the role of other pathways potentially involved in redox metabolism and how they affect quantitatively the required capacity of the Calvin cycle. Our model also enables us to discriminate between different acetate assimilation pathways that were proposed recently for R. sphaeroides and R. rubrum, both lacking the isocitrate lyase. Finally, we demonstrate the value of the metabolic model also for potential biotechnological applications: we examine the theoretical capabilities of PNSB for photoheterotrophic

  5. Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity.

    Science.gov (United States)

    Mintz-Oron, Shira; Meir, Sagit; Malitsky, Sergey; Ruppin, Eytan; Aharoni, Asaph; Shlomi, Tomer

    2012-01-03

    Plant metabolic engineering is commonly used in the production of functional foods and quality trait improvement. However, to date, computational model-based approaches have only been scarcely used in this important endeavor, in marked contrast to their prominent success in microbial metabolic engineering. In this study we present a computational pipeline for the reconstruction of fully compartmentalized tissue-specific models of Arabidopsis thaliana on a genome scale. This reconstruction involves automatic extraction of known biochemical reactions in Arabidopsis for both primary and secondary metabolism, automatic gap-filling, and the implementation of methods for determining subcellular localization and tissue assignment of enzymes. The reconstructed tissue models are amenable for constraint-based modeling analysis, and significantly extend upon previous model reconstructions. A set of computational validations (i.e., cross-validation tests, simulations of known metabolic functionalities) and experimental validations (comparison with experimental metabolomics datasets under various compartments and tissues) strongly testify to the predictive ability of the models. The utility of the derived models was demonstrated in the prediction of measured fluxes in metabolically engineered seed strains and the design of genetic manipulations that are expected to increase vitamin E content, a significant nutrient for human health. Overall, the reconstructed tissue models are expected to lay down the foundations for computational-based rational design of plant metabolic engineering. The reconstructed compartmentalized Arabidopsis tissue models are MIRIAM-compliant and are available upon request.

  6. PPARγ isoforms differentially regulate metabolic networks to mediate mouse prostatic epithelial differentiation.

    Science.gov (United States)

    Strand, D W; Jiang, M; Murphy, T A; Yi, Y; Konvinse, K C; Franco, O E; Wang, Y; Young, J D; Hayward, S W

    2012-08-09

    Recent observations indicate prostatic diseases are comorbidities of systemic metabolic dysfunction. These discoveries revealed fundamental questions regarding the nature of prostate metabolism. We previously showed that prostate-specific ablation of PPARγ in mice resulted in tumorigenesis and active autophagy. Here, we demonstrate control of overlapping and distinct aspects of prostate epithelial metabolism by ectopic expression of individual PPARγ isoforms in PPARγ knockout prostate epithelial cells. Expression and activation of either PPARγ 1 or 2 reduced de novo lipogenesis and oxidative stress and mediated a switch from glucose to fatty acid oxidation through regulation of genes including Pdk4, Fabp4, Lpl, Acot1 and Cd36. Differential effects of PPARγ isoforms included decreased basal cell differentiation, Scd1 expression and triglyceride fatty acid desaturation and increased tumorigenicity by PPARγ1. In contrast, PPARγ2 expression significantly increased basal cell differentiation, Scd1 expression and AR expression and responsiveness. Finally, in confirmation of in vitro data, a PPARγ agonist versus high-fat diet (HFD) regimen in vivo confirmed that PPARγ agonization increased prostatic differentiation markers, whereas HFD downregulated PPARγ-regulated genes and decreased prostate differentiation. These data provide a rationale for pursuing a fundamental metabolic understanding of changes to glucose and fatty acid metabolism in benign and malignant prostatic diseases associated with systemic metabolic stress.

  7. Pool size measurements facilitate the determination of fluxes at branching points in nonstationary metabolic flux analysis: The case of Arabidopsis thaliana

    Directory of Open Access Journals (Sweden)

    Robert eHeise

    2015-06-01

    Full Text Available Pool size measurements are important for the estimation of absolute intracellular fluxes in particular scenarios based on data from heavy carbon isotope experiments. Recently, steady-state fluxes estimates were obtained for central carbon metabolism in an intact illuminated rosette of Arabidopsis thaliana grown photoautotrophically (Szecowka et al., 2013; Heise et al., 2014. Fluxes were estimated therein by integrating mass-spectrometric data of the dynamics of the unlabeled metabolic fraction, data on metabolic pool sizes, partitioning of metabolic pools between cellular compartments and estimates of photosynthetically inactive pools, with a simplified model of plant central carbon metabolism. However, the fluxes were determined by treating the pool sizes as fixed parameters. Here we investigated whether and, if so, to what extent the treatment of pool sizes as parameters to be optimized in three scenarios may affect the flux estimates. The results are discussed in terms of benchmark values for canonical pathways and reactions, including starch and sucrose synthesis as well as the ribulose-1,5-bisphosphate carboxylation and oxygenation reactions. In addition, we discuss pathways emerging from a divergent branch point for which pool sizes are required for flux estimation, irrespective of the computational approach used for the simulation of the observable labelling pattern. Therefore, our findings indicate the necessity for development of techniques for accurate pool size measurements to improve the quality of flux estimates from nonstationary flux estimates in intact plant cells in the absence of alternative flux measurements.

  8. Metabolic network capacity of Escherichia coli for Krebs cycle-dependent proline hydroxylation.

    Science.gov (United States)

    Theodosiou, Eleni; Frick, Oliver; Bühler, Bruno; Schmid, Andreas

    2015-07-29

    Understanding the metabolism of the microbial host is essential for the development and optimization of whole-cell based biocatalytic processes, as it dictates production efficiency. This is especially true for redox biocatalysis where metabolically active cells are employed because of the cofactor/cosubstrate regenerative capacity endogenous in the host. Recombinant Escherichia coli was used for overproducing proline-4-hydroxylase (P4H), a dioxygenase catalyzing the hydroxylation of free L-proline into trans-4-hydroxy-L-proline with a-ketoglutarate (a-KG) as cosubstrate. In this whole-cell biocatalyst, central carbon metabolism provides the required cosubstrate a-KG, coupling P4H biocatalytic performance directly to carbon metabolism and metabolic activity. By applying both experimental and computational biology tools, such as metabolic engineering and (13)C-metabolic flux analysis ((13)C-MFA), we investigated and quantitatively described the physiological, metabolic, and bioenergetic response of the whole-cell biocatalyst to the targeted bioconversion and identified possible metabolic bottlenecks for further rational pathway engineering. A proline degradation-deficient E. coli strain was constructed by deleting the putA gene encoding proline dehydrogenase. Whole-cell biotransformations with this mutant strain led not only to quantitative proline hydroxylation but also to a doubling of the specific trans-4-L-hydroxyproline (hyp) formation rate, compared to the wild type. Analysis of carbon flux through central metabolism of the mutant strain revealed that the increased a-KG demand for P4H activity did not enhance the a-KG generating flux, indicating a tightly regulated TCA cycle operation under the conditions studied. In the wild type strain, P4H synthesis and catalysis caused a reduction in biomass yield. Interestingly, the ΔputA strain additionally compensated the associated ATP and NADH loss by reducing maintenance energy demands at comparably low glucose

  9. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

    Energy Technology Data Exchange (ETDEWEB)

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-03-27

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.

  10. Metal availability and the expanding network of microbial metabolisms in the Archaean eon

    Science.gov (United States)

    Moore, Eli K.; Jelen, Benjamin I.; Giovannelli, Donato; Raanan, Hagai; Falkowski, Paul G.

    2017-09-01

    Life is based on energy gained by electron-transfer processes; these processes rely on oxidoreductase enzymes, which often contain transition metals in their structures. The availability of different metals and substrates has changed over the course of Earth's history as a result of secular changes in redox conditions, particularly global oxygenation. New metabolic pathways using different transition metals co-evolved alongside changing redox conditions. Sulfur reduction, sulfate reduction, methanogenesis and anoxygenic photosynthesis appeared between about 3.8 and 3.4 billion years ago. The oxidoreductases responsible for these metabolisms incorporated metals that were readily available in Archaean oceans, chiefly iron and iron-sulfur clusters. Oxygenic photosynthesis appeared between 3.2 and 2.5 billion years ago, as did methane oxidation, nitrogen fixation, nitrification and denitrification. These metabolisms rely on an expanded range of transition metals presumably made available by the build-up of molecular oxygen in soil crusts and marine microbial mats. The appropriation of copper in enzymes before the Great Oxidation Event is particularly important, as copper is key to nitrogen and methane cycling and was later incorporated into numerous aerobic metabolisms. We find that the diversity of metals used in oxidoreductases has increased through time, suggesting that surface redox potential and metal incorporation influenced the evolution of metabolism, biological electron transfer and microbial ecology.

  11. QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells.

    Science.gov (United States)

    Fisher, Ciarán P; Plant, Nicholas J; Moore, J Bernadette; Kierzek, Andrzej M

    2013-12-15

    Dynamic simulation of genome-scale molecular interaction networks will enable the mechanistic prediction of genotype-phenotype relationships. Despite advances in quantitative biology, full parameterization of whole-cell models is not yet possible. Simulation methods capable of using available qualitative data are required to develop dynamic whole-cell models through an iterative process of modelling and experimental validation. We formulate quasi-steady state Petri nets (QSSPN), a novel method integrating Petri nets and constraint-based analysis to predict the feasibility of qualitative dynamic behaviours in qualitative models of gene regulation, signalling and whole-cell metabolism. We present the first dynamic simulations including regulatory mechanisms and a genome-scale metabolic network in human cell, using bile acid homeostasis in human hepatocytes as a case study. QSSPN simulations reproduce experimentally determined qualitative dynamic behaviours and permit mechanistic analysis of genotype-phenotype relationships. The model and simulation software implemented in C++ are available in supplementary material and at http://sysbio3.fhms.surrey.ac.uk/qsspn/.

  12. A novel proposal of a simplified bacterial gene set and the neo-construction of a general minimized metabolic network.

    Science.gov (United States)

    Ye, Yuan-Nong; Ma, Bin-Guang; Dong, Chuan; Zhang, Hong; Chen, Ling-Ling; Guo, Feng-Biao

    2016-10-07

    A minimal gene set (MGS) is critical for the assembly of a minimal artificial cell. We have developed a proposal of simplifying bacterial gene set to approximate a bacterial MGS by the following procedure. First, we base our simplified bacterial gene set (SBGS) on experimentally determined essential genes to ensure that the genes included in the SBGS are critical. Second, we introduced a half-retaining strategy to extract persistent essential genes to ensure stability. Third, we constructed a viable metabolic network to supplement SBGS. The proposed SBGS includes 327 genes and required 431 reactions. This report describes an SBGS that preserves both self-replication and self-maintenance systems. In the minimized metabolic network, we identified five novel hub metabolites and confirmed 20 known hubs. Highly essential genes were found to distribute the connecting metabolites into more reactions. Based on our SBGS, we expanded the pool of targets for designing broad-spectrum antibacterial drugs to reduce pathogen resistance. We also suggested a rough semi-de novo strategy to synthesize an artificial cell, with potential applications in industry.

  13. ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.

    Directory of Open Access Journals (Sweden)

    Zixiang Xu

    Full Text Available Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.

  14. Deletion of the Mitochondrial Chaperone TRAP-1 Uncovers Global Reprogramming of Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Sofia Lisanti

    2014-08-01

    Full Text Available Reprogramming of metabolic pathways contributes to human disease, especially cancer, but the regulators of this process are unknown. Here, we have generated a mouse knockout for the mitochondrial chaperone TRAP-1, a regulator of bioenergetics in tumors. TRAP-1−/− mice are viable and showed reduced incidence of age-associated pathologies, including obesity, inflammatory tissue degeneration, dysplasia, and spontaneous tumor formation. This was accompanied by global upregulation of oxidative phosphorylation and glycolysis transcriptomes, causing deregulated mitochondrial respiration, oxidative stress, impaired cell proliferation, and a switch to glycolytic metabolism in vivo. These data identify TRAP-1 as a central regulator of mitochondrial bioenergetics, and this pathway could contribute to metabolic rewiring in tumors.

  15. NF-E2-related factor 2 deletion facilitates hepatic fatty acids metabolism disorder induced by high-fat diet via regulating related genes in mice.

    Science.gov (United States)

    Wang, Xinghe; Li, Chunyan; Xu, Shang; Ishfaq, Muhammad; Zhang, Xiuying

    2016-08-01

    There is increasing evidence that Nrf2 participates in hepatic fatty acid metabolism in non-alcoholic fatty liver disease; however, the mechanism remains unclear. We investigated the role of Nrf2 in hepatic fatty acid metabolism disorder induced by high-fat diet (HFD). Mice fed HFD developed hepatic steatosis and exhibited Nrf2 deficiency. Change of fatty acid composition mediated by Nrf2 deletion was observed predominantly in the liver and not the serum. HFD-induced variations in hepatic 18-carbon and 22-carbon fatty acids were enhanced by Nrf2 deficiency. In the HFD group, Nrf2 deficiency led to increases in the mRNA expression of PPARα, FXR, FAS, LXR and ACC-1, while levels of PGC-1α and Srebp-1c mRNA were decreased. Nrf2 mRNA expression was enhanced in the liver of HFD-induced wild type mice, whereas it was undetectable in Nrf2-null mice. These results suggest that Nrf2 deficiency induced by HFD promoted hepatic fatty acid metabolism disorder by altering 18-carbon and 22-carbon fatty acid composition. Changes in fatty acid content were also associated with alteration of the transcription of genes involved in hepatic fatty acid metabolism. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network.

    Science.gov (United States)

    Salazar, Diego A; Rodríguez-López, Alexander; Herreño, Angélica; Barbosa, Hector; Herrera, Juliana; Ardila, Andrea; Barreto, George E; González, Janneth; Alméciga-Díaz, Carlos J

    2016-02-01

    Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS. Copyright © 2015. Published by Elsevier Inc.

  17. Urban infrastructure influences dissolved organic matter quality and bacterial metabolism in an urban stream network

    Science.gov (United States)

    Urban streams are degraded by a suite of factors, including burial beneath urban infrastructure (i.e., roads, parking lots) that eliminates light and reduces direct organic matter inputs to streams, with likely consequences for organic matter metabolism by microbes and carbon lim...

  18. Metabolic Networks Underlying Cognitive Reserve in Prodromal Alzheimer Disease: A European Alzheimer Disease Consortium Project

    NARCIS (Netherlands)

    Morbelli, S.; Perneczky, R.; Drzezga, A.; Frisoni, G. B.; Caroli, A.; van Berckel, B.N.M.; Ossenkoppele, R.; Guedj, E.; Didic, M.; Brugnolo, A.; Naseri, M.; Sambuceti, G.; Pagani, M.; Nobili, F.

    2013-01-01

    This project aimed to investigate the metabolic basis for resilience to neurodegeneration (cognitive reserve) in highly educated patients with prodromal Alzheimer disease (AD). Methods: Sixty-four patients with amnestic mild cognitive impairment who later converted to AD dementia during follow-up,

  19. Artificial neural network analysis of factors controlling ecosystem metabolism in coastal systems

    NARCIS (Netherlands)

    Rochelle-Newall, E.J.; Winter, C.; Barrón, C.; Borges, A.V.; Duarte, C.M.; Elliott, M.; Frankignoulle, M.; Gazeau, F.P.H.; Middelburg, J.J.; Pizay, M-D.; Thioulouse, J.; Gattuso, J.P.

    2007-01-01

    Knowing the metabolic balance of an ecosystem is of utmost importance in determining whether the system is a net source or net sink of carbon dioxide to the atmosphere. However, obtaining these estimates often demands significant amounts of time and manpower. Here we present a simplified way to

  20. An experimentally-supported genome-scale metabolic network reconstruction for Yersinia pestis CO92

    Directory of Open Access Journals (Sweden)

    Motin Vladimir L

    2011-10-01

    Full Text Available Abstract Background Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas. Results Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain. Conclusions Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, provides an in silico platform with which to investigate the metabolism of this important human pathogen.

  1. Enzyme allocation problems in kinetic metabolic networks: Optimal solutions are elementary flux modes

    Czech Academy of Sciences Publication Activity Database

    Müller, Stefan; Regensburger, G.; Steuer, Ralf

    2014-01-01

    Roč. 347, APR 2014 (2014), s. 182-190 ISSN 0022-5193 R&D Projects: GA MŠk(CZ) EE2.3.20.0256 Institutional support: RVO:67179843 Keywords : metabolic optimization * enzyme kinetics * oriented matroid * elementary vector * conformal sum Subject RIV: EI - Biotechnology ; Bionics Impact factor: 2.116, year: 2014

  2. Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic

    Science.gov (United States)

    Demongeot, Jacques; Ben Amor, Hedi; Elena, Adrien; Gillois, Pierre; Noual, Mathilde; Sené, Sylvain

    2009-01-01

    Regulatory interaction networks are often studied on their dynamical side (existence of attractors, study of their stability). We focus here also on their robustness, that is their ability to offer the same spatiotemporal patterns and to resist to external perturbations such as losses of nodes or edges in the networks interactions architecture, changes in their environmental boundary conditions as well as changes in the update schedule (or updating mode) of the states of their elements (e.g., if these elements are genes, their synchronous coexpression mode versus their sequential expression). We define the generic notions of boundary, core, and critical vertex or edge of the underlying interaction graph of the regulatory network, whose disappearance causes dramatic changes in the number and nature of attractors (e.g., passage from a bistable behaviour to a unique periodic regime) or in the range of their basins of stability. The dynamic transition of states will be presented in the framework of threshold Boolean automata rules. A panorama of applications at different levels will be given: brain and plant morphogenesis, bulbar cardio-respiratory regulation, glycolytic/oxidative metabolic coupling, and eventually cell cycle and feather morphogenesis genetic control. PMID:20057955

  3. Metabolic network analysis and experimental study of lipid production in Rhodosporidium toruloides grown on single and mixed substrates.

    Science.gov (United States)

    Bommareddy, Rajesh Reddy; Sabra, Wael; Maheshwari, Garima; Zeng, An-Ping

    2015-03-18

    Microbial lipids (triacylglycerols, TAG) have received large attention for a sustainable production of oleochemicals and biofuels. Rhodosporidium toruloides can accumulate lipids up to 70% of its cell mass under certain conditions. However, our understanding of lipid production in this yeast is still much limited, especially for growth with mixed substrates at the level of metabolic network. In this work, the potentials of several important carbon sources for TAG production in R.toruloides are first comparatively studied in silico by means of elementary mode analysis followed by experimental validation. A simplified metabolic network of R.toruloides was reconstructed based on a combination of genome and proteome annotations. Optimal metabolic space was studied using elementary mode analysis for growth on glycerol, glucose, xylose and arabinose or in mixtures. The in silico model predictions of growth and lipid production are in agreement with experimental results. Both the in silico and experimental studies revealed that glycerol is an attractive substrate for lipid synthesis in R. toruloides either alone or in blend with sugars. A lipid yield as high as 0.53 (C-mol TAG/C-mol) has been experimentally obtained for growth on glycerol, compared to a theoretical maximum of 0.63 (C-mol TAG/C-mol). The lipid yield on glucose is much lower (0.29 (experimental) vs. 0.58 (predicted) C-mol TAG/C-mol). The blend of glucose with glycerol decreased the lipid yield on substrate but can significantly increase the overall volumetric productivity. Experimental studies revealed catabolite repression of glycerol by the presence of glucose for the first time. Significant influence of oxygen concentration on the yield and composition of lipids were observed which have not been quantitatively studied before. This study provides for the first time a simplified metabolic model of R.toruloides and its detailed in silico analysis for growth on different carbon sources for their potential of

  4. Exogenous auxin regulates multi-metabolic network and embryo development, controlling seed secondary dormancy and germination in Nicotiana tabacum L.

    Science.gov (United States)

    Li, Zhenhua; Zhang, Jie; Liu, Yiling; Zhao, Jiehong; Fu, Junjie; Ren, Xueliang; Wang, Guoying; Wang, Jianhua

    2016-02-09

    Auxin was recognized as a secondary dormancy phytohormone, controlling seed dormancy and germination. However, the exogenous auxin-controlled seed dormancy and germination remain unclear in physiological process and gene network. Tobacco seeds soaked in 1000 mg/l auxin solution showed markedly decreased germination compared with that in low concentration of auxin solutions and ddH2O. Using an electron microscope, observations were made on the seeds which did not unfold properly in comparison to those submerged in ddH2O. The radicle traits measured by WinRHIZO, were found to be also weaker than the other treatment groups. Quantified by ELISA, there was no significant difference found in β-1,3glucanase activity and abscisic acid (ABA) content between the seeds imbibed in gradient concentration of auxin solution and those soaked in ddH2O. However, gibberellic acid (GA) and auxin contents were significantly higher at the time of exogenous auxin imbibition and were gradually reduced at germination. RNA sequencing (RNA-seq), revealed that the transcriptome of auxin-responsive dormancy seeds were more similar to that of the imbibed seeds when compared with primary dormancy seeds by principal component analysis. The results of gene differential expression analysis revealed that auxin-controlled seed secondary dormancy was associated with flavonol biosynthetic process, gibberellin metabolic process, adenylyl-sulfate reductase activity, thioredoxin activity, glutamate synthase (NADH) activity and chromatin regulation. In addition, auxin-responsive germination responded to ABA, auxin, jasmonic acid (JA) and salicylic acid (SA) mediated signaling pathway (red, far red and blue light), glutathione and methionine (Met) metabolism. In this study, exogenous auxin-mediated seed secondary dormancy is an environmental model that prevents seed germination in an unfavorable condition. Seeds of which could not imbibe normally, and radicles of which also could not develop normally and

  5. Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes

    DEFF Research Database (Denmark)

    Väremo, Leif; Scheele, Camilla; Broholm, Christa

    2015-01-01

    -analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism......Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome...

  6. Facilitating innovations

    NARCIS (Netherlands)

    Buurma, J.S.; Visser, A.J.; Migchels, G.

    2011-01-01

    Many innovations involve changes which transcend the individual business or are only achievable when various businesses and/or interested parties take up the challenge together. In System Innovation Programmes, the necessary innovations are facilitated by means of workshops related to specific areas

  7. AMBIENT: Active Modules for Bipartite Networks - using high-throughput transcriptomic data to dissect metabolic response

    OpenAIRE

    Bryant, William A; Sternberg, Michael JE; Pinney, John W

    2013-01-01

    Background With the continued proliferation of high-throughput biological experiments, there is a pressing need for tools to integrate the data produced in ways that produce biologically meaningful conclusions. Many microarray studies have analysed transcriptomic data from a pathway perspective, for instance by testing for KEGG pathway enrichment in sets of upregulated genes. However, the increasing availability of species-specific metabolic models provides the opportunity to analyse these da...

  8. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

    Science.gov (United States)

    He, Fei; Murabito, Ettore; Westerhoff, Hans V

    2016-04-01

    Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways. © 2016 The Author(s).

  9. Flavonoids: A Metabolic Network Mediating Plants Adaptation to Their Real Estate

    Directory of Open Access Journals (Sweden)

    Aidyn eMouradov

    2014-11-01

    Full Text Available From an evolutionary perspective, the emergence of the sophisticated chemical scaffolds of flavonoid molecules represents a key step in the colonization of Earth’s terrestrial environment by vascular plants nearly 500 million years ago. The subsequent evolution of flavonoids through recruitment and modification of ancestors involved in primary metabolism has allowed vascular plants to cope with pathogen invasion and damaging UV light. The functional properties of flavonoids as a unique combination of different classes of compounds vary significantly depending on the demands of their local real estate. Apart from geographical location, the composition of flavonoids is largely dependent on the plant species, their developmental stage, tissue type, subcellular localization, and key ecological influences of both biotic and abiotic origin. Molecular and metabolic cross-talk between flavonoid and other pathways as a result of the re-direction of intermediate molecules have been well investigated. This metabolic plasticity is a key factor in plant adaptive strength and is of paramount importance for early land plants adaptation to their local ecosystems. In human and animal health the biological and pharmacological activities of flavonoids have been investigated in great depth and have shown a wide range of anti-inflammatory, anti-oxidant, anti-microbial and anti-cancer properties. In this paper we review the application of advanced gene technologies for targeted reprogramming of the flavonoid pathway in plants to understand its molecular functions and explore opportunities for major improvements in forage plants enhancing animal health and production.

  10. Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal.

    Science.gov (United States)

    Fritzemeier, Claus Jonathan; Hartleb, Daniel; Szappanos, Balázs; Papp, Balázs; Lercher, Martin J

    2017-04-01

    Energy metabolism is central to cellular biology. Thus, genome-scale models of heterotrophic unicellular species must account appropriately for the utilization of external nutrients to synthesize energy metabolites such as ATP. However, metabolic models designed for flux-balance analysis (FBA) may contain thermodynamically impossible energy-generating cycles: without nutrient consumption, these models are still capable of charging energy metabolites (such as ADP→ATP or NADP+→NADPH). Here, we show that energy-generating cycles occur in over 85% of metabolic models without extensive manual curation, such as those contained in the ModelSEED and MetaNetX databases; in contrast, such cycles are rare in the manually curated models of the BiGG database. Energy generating cycles may represent model errors, e.g., erroneous assumptions on reaction reversibilities. Alternatively, part of the cycle may be thermodynamically feasible in one environment, while the remainder is thermodynamically feasible in another environment; as standard FBA does not account for thermodynamics, combining these into an FBA model allows erroneous energy generation. The presence of energy-generating cycles typically inflates maximal biomass production rates by 25%, and may lead to biases in evolutionary simulations. We present efficient computational methods (i) to identify energy generating cycles, using FBA, and (ii) to identify minimal sets of model changes that eliminate them, using a variant of the GlobalFit algorithm.

  11. Flavonoids: a metabolic network mediating plants adaptation to their real estate.

    Science.gov (United States)

    Mouradov, Aidyn; Spangenberg, German

    2014-01-01

    From an evolutionary perspective, the emergence of the sophisticated chemical scaffolds of flavonoid molecules represents a key step in the colonization of Earth's terrestrial environment by vascular plants nearly 500 million years ago. The subsequent evolution of flavonoids through recruitment and modification of ancestors involved in primary metabolism has allowed vascular plants to cope with pathogen invasion and damaging UV light. The functional properties of flavonoids as a unique combination of different classes of compounds vary significantly depending on the demands of their local real estate. Apart from geographical location, the composition of flavonoids is largely dependent on the plant species, their developmental stage, tissue type, subcellular localization, and key ecological influences of both biotic and abiotic origin. Molecular and metabolic cross-talk between flavonoid and other pathways as a result of the re-direction of intermediate molecules have been well investigated. This metabolic plasticity is a key factor in plant adaptive strength and is of paramount importance for early land plants adaptation to their local ecosystems. In human and animal health the biological and pharmacological activities of flavonoids have been investigated in great depth and have shown a wide range of anti-inflammatory, anti-oxidant, anti-microbial, and anti-cancer properties. In this paper we review the application of advanced gene technologies for targeted reprogramming of the flavonoid pathway in plants to understand its molecular functions and explore opportunities for major improvements in forage plants enhancing animal health and production.

  12. Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes.

    Directory of Open Access Journals (Sweden)

    Josine L Min

    Full Text Available Metabolic Syndrome (MetS is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD and gluteal (GLU adipose tissue, and whole blood (WB, from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS-associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6% were expressed in ABD and 51 (0.6% in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (D(ABD-GLU = 0.89, seven of which were associated with MetS (FDR P100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin, was associated with body mass index (BMI (P = 6.0×10(-4; and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (P = 8.7×10(-4 and BMI-adjusted waist-to-hip ratio (P = 2.4×10(-4. Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations.

  13. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming.

    Science.gov (United States)

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-08-01

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. The software is implemented in Matlab, and is provided as supplementary information . hyunseob.song@pnnl.gov. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2017. This work is written by US Government employees and are in the public domain in the US.

  14. Enhancing Carbon Fixation by Metabolic Engineering: A Model System of Complex Network Modulation

    Energy Technology Data Exchange (ETDEWEB)

    Dr. Gregory Stephanopoulos

    2008-04-10

    In the first two years of this research we focused on the development of a DNA microarray for transcriptional studies in the photosynthetic organism Synechocystis and the elucidation of the metabolic pathway for biopolymer synthesis in this organism. In addition we also advanced the molecular biological tools for metabolic engineering of biopolymer synthesis in Synechocystis and initiated a series of physiological studies for the elucidation of the carbon fixing pathways and basic central carbon metabolism of these organisms. During the last two-year period we focused our attention on the continuation and completion of the last task, namely, the development of tools for basic investigations of the physiology of these cells through, primarily, the determination of their metabolic fluxes. The reason for this decision lies in the importance of fluxes as key indicators of physiology and the high level of information content they carry in terms of identifying rate limiting steps in a metabolic pathway. While flux determination is a well-advanced subject for heterotrophic organisms, for the case of autotrophic bacteria, like Synechocystis, some special challenges had to be overcome. These challenges stem mostly from the fact that if one uses {sup 13}C labeled CO{sub 2} for flux determination, the {sup 13}C label will mark, at steady state, all carbon atoms of all cellular metabolites, thus eliminating the necessary differentiation required for flux determination. This peculiarity of autotrophic organisms makes it imperative to carry out flux determination under transient conditions, something that had not been accomplished before. We are pleased to report that we have solved this problem and we are now able to determine fluxes in photosynthetic organisms from stable isotope labeling experiments followed by measurements of label enrichment in cellular metabolites using Gas Chromatography-Mass Spectrometry. We have conducted extensive simulations to test the method and

  15. Modules in the metabolic network of E.coli with regulatory interactions

    Czech Academy of Sciences Publication Activity Database

    Geryk, J.; Slanina, František

    2013-01-01

    Roč. 8, č. 2 (2013), s. 188-202 ISSN 1748-5673 R&D Projects: GA MŠk OC09078 Institutional support: RVO:68378271 Keywords : networks * modularity * biophysics Subject RIV: BO - Biophysics Impact factor: 0.655, year: 2013 http://www.inderscience.com/info/inarticle.php?artid=55500

  16. Change in network connectivity during fictive-gasping generation in hypoxia: Prevention by a metabolic intermediate

    Directory of Open Access Journals (Sweden)

    Andrés eNieto-Posadas

    2014-07-01

    Full Text Available The neuronal circuit in charge of generating the respiratory rhythms, localized in the pre-Bötzinger complex (preBötC, is configured to produce fictive-eupnea during normoxia and reconfigures to produce fictive-gasping during hypoxic conditions in vitro. The mechanisms involved in such reconfiguration have been extensively investigated by cell-focused studies, but the actual changes at the network level remain elusive. Since a failure to generate gasping has been linked to Sudden Infant Death Syndrome, the study of gasping generation and pharmacological approaches to promote it may have clinical relevance. Here, we study the changes in network dynamics and circuit reconfiguration that occur during the transition to fictive-gasping generation in the brainstem slice preparation by recording the preBötC with multi-electrode arrays and assessing correlated firing among respiratory neurons or clusters of respiratory neurons (multiunits. We studied whether the respiratory network reconfiguration in hypoxia involves changes in either the number of active respiratory elements, the number of functional connections among elements, or the strength of these connections. Moreover, we tested the influence of isocitrate, a Krebs cycle intermediate that has recently been shown to promote breathing, on the configuration of the preBötC circuit during normoxia and on its reconfiguration during hypoxia. We found that, in contrast to previous suggestions based on cell-focused studies, the number and the overall activity of respiratory neurons change only slightly during hypoxia. However, hypoxia induces a reduction in the strength of functional connectivity within the circuit without reducing the number of connections. Isocitrate prevented this reduction during hypoxia while increasing the strength of network connectivity. In conclusion, we provide an overview of the configuration of the respiratory network under control conditions and how it is reconfigured

  17. The Sexual Advantage of Looking, Smelling, and Tasting Good: The Metabolic Network that Produces Signals for Pollinators.

    Science.gov (United States)

    Borghi, Monica; Fernie, Alisdair R; Schiestl, Florian P; Bouwmeester, Harro J

    2017-04-01

    A striking feature of the angiosperms that use animals as pollen carriers to sexually reproduce is the great diversity of their flowers with regard to morphology and traits such as color, odor, and nectar. These traits are underpinned by the synthesis of secondary metabolites such as pigments and volatiles, as well as carbohydrates and amino acids, which are used by plants to lure and reward animal pollinators. We review here the knowledge of the metabolic network that supports the biosynthesis of these compounds and the behavioral responses that these molecules elicit in the animal pollinators. Such knowledge provides us with a deeper insight into the ecology and evolution of plant-pollinator interactions, and should help us to better manage these ecologically essential interactions in agricultural ecosystems. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. TRFBA: an algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data.

    Science.gov (United States)

    Motamedian, Ehsan; Mohammadi, Maryam; Shojaosadati, Seyed Abbas; Heydari, Mona

    2017-04-01

    Integration of different biological networks and data-types has been a major challenge in systems biology. The present study introduces the transcriptional regulated flux balance analysis (TRFBA) algorithm that integrates transcriptional regulatory and metabolic models using a set of expression data for various perturbations. TRFBA considers the expression levels of genes as a new continuous variable and introduces two new linear constraints. The first constraint limits the rate of reaction(s) supported by a metabolic gene using a constant parameter (C) that converts the expression levels to the upper bounds of the reactions. Considering the concept of constraint-based modeling, the second set of constraints correlates the expression level of each target gene with that of its regulating genes. A set of constraints and binary variables was also added to prevent the second set of constraints from overlapping. TRFBA was implemented on Escherichia coli and Saccharomyces cerevisiae models to estimate growth rates under various environmental and genetic perturbations. The error sensitivity to the algorithm parameter was evaluated to find the best value of C. The results indicate a significant improvement in the quantitative prediction of growth in comparison with previously presented algorithms. The robustness of the algorithm to change in the expression data and the regulatory network was tested to evaluate the effect of noisy and incomplete data. Furthermore, the use of added constraints for perturbations without their gene expression profile demonstrates that these constraints can be applied to improve the growth prediction of FBA. TRFBA is implemented in Matlab software and requires COBRA toolbox. Source code is freely available at http://sbme.modares.ac.ir . : motamedian@modares.ac.ir. Supplementary data are available at Bioinformatics online.

  19. Playing facilitator

    DEFF Research Database (Denmark)

    Houmøller, Ellen; Marchetti, Emanuela

    2015-01-01

    workshops based on two classic role-play games: The Silent Game (Brandt, 2006) and The Six Thinking Hats (de Bono, 1985). These games were created to support students in learning design thinking in groups and are assigned positive values in literature, hence we expected a smooth process. However, our......t: This paper presents reflections on the role of teachers as facilitators, in a context of role-play targeting learning of design thinking skills. Our study was conducted according to the method of visual ethnography. We acted as facilitators for 50 students through the yearly six-day competitive...... event called InnoEvent, addressed to students in the fields of multimedia and healthcare. Being interested in studying games and role-play as tools to support independent learning in the field of design thinking and team-building, following Dewey’s (1938) theory of learning experience, we ran two...

  20. Regulatory and metabolic networks for the adaptation of Pseudomonas aeruginosa biofilms to urinary tract-like conditions.

    Directory of Open Access Journals (Sweden)

    Petra Tielen

    Full Text Available Biofilms of the Gram-negative bacterium Pseudomonas aeruginosa are one of the major causes of complicated urinary tract infections with detrimental outcome. To develop novel therapeutic strategies the molecular adaption strategies of P. aeruginosa biofilms to the conditions of the urinary tract were investigated thoroughly at the systems level using transcriptome, proteome, metabolome and enzyme activity analyses. For this purpose biofilms were grown anaerobically in artificial urine medium (AUM. Obtained data were integrated bioinformatically into gene regulatory and metabolic networks. The dominating response at the transcriptome and proteome level was the adaptation to iron limitation via the broad Fur regulon including 19 sigma factors and up to 80 regulated target genes or operons. In agreement, reduction of the iron cofactor-dependent nitrate respiratory metabolism was detected. An adaptation of the central metabolism to lactate, citrate and amino acid as carbon sources with the induction of the glyoxylate bypass was observed, while other components of AUM like urea and creatinine were not used. Amino acid utilization pathways were found induced, while fatty acid biosynthesis was reduced. The high amounts of phosphate found in AUM explain the reduction of phosphate assimilation systems. Increased quorum sensing activity with the parallel reduction of chemotaxis and flagellum assembly underscored the importance of the biofilm life style. However, reduced formation of the extracellular polysaccharide alginate, typical for P. aeruginosa biofilms in lungs, indicated a different biofilm type for urinary tract infections. Furthermore, the obtained quorum sensing response results in an increased production of virulence factors like the extracellular lipase LipA and protease LasB and AprA explaining the harmful cause of these infections.

  1. A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics

    Directory of Open Access Journals (Sweden)

    van Gulik Walter M

    2006-12-01

    Full Text Available Abstract Background Dynamic modeling of metabolic reaction networks under in vivo conditions is a crucial step in order to obtain a better understanding of the (disfunctioning of living cells. So far dynamic metabolic models generally have been based on mechanistic rate equations which often contain so many parameters that their identifiability from experimental data forms a serious problem. Recently, approximative rate equations, based on the linear logarithmic (linlog format have been proposed as a suitable alternative with fewer parameters. Results In this paper we present a method for estimation of the kinetic model parameters, which are equal to the elasticities defined in Metabolic Control Analysis, from metabolite data obtained from dynamic as well as steady state perturbations, using the linlog kinetic format. Additionally, we address the question of parameter identifiability from dynamic perturbation data in the presence of noise. The method is illustrated using metabolite data generated with a dynamic model of the glycolytic pathway of Saccharomyces cerevisiae based on mechanistic rate equations. Elasticities are estimated from the generated data, which define the complete linlog kinetic model of the glycolysis. The effect of data noise on the accuracy of the estimated elasticities is presented. Finally, identifiable subset of parameters is determined using information on the standard deviations of the estimated elasticities through Monte Carlo (MC simulations. Conclusion The parameter estimation within the linlog kinetic framework as presented here allows the determination of the elasticities directly from experimental data from typical dynamic and/or steady state experiments. These elasticities allow the reconstruction of the full kinetic model of Saccharomyces cerevisiae, and the determination of the control coefficients. MC simulations revealed that certain elasticities are potentially unidentifiable from dynamic data only

  2. Genome-scale reconstruction of the metabolic network in Yersinia pestis, strain 91001

    Energy Technology Data Exchange (ETDEWEB)

    Navid, A; Almaas, E

    2009-01-13

    The gram-negative bacterium Yersinia pestis, the aetiological agent of bubonic plague, is one the deadliest pathogens known to man. Despite its historical reputation, plague is a modern disease which annually afflicts thousands of people. Public safety considerations greatly limit clinical experimentation on this organism and thus development of theoretical tools to analyze the capabilities of this pathogen is of utmost importance. Here, we report the first genome-scale metabolic model of Yersinia pestis biovar Mediaevalis based both on its recently annotated genome, and physiological and biochemical data from literature. Our model demonstrates excellent agreement with Y. pestis known metabolic needs and capabilities. Since Y. pestis is a meiotrophic organism, we have developed CryptFind, a systematic approach to identify all candidate cryptic genes responsible for known and theoretical meiotrophic phenomena. In addition to uncovering every known cryptic gene for Y. pestis, our analysis of the rhamnose fermentation pathway suggests that betB is the responsible cryptic gene. Despite all of our medical advances, we still do not have a vaccine for bubonic plague. Recent discoveries of antibiotic resistant strains of Yersinia pestis coupled with the threat of plague being used as a bioterrorism weapon compel us to develop new tools for studying the physiology of this deadly pathogen. Using our theoretical model, we can study the cell's phenotypic behavior under different circumstances and identify metabolic weaknesses which may be harnessed for the development of therapeutics. Additionally, the automatic identification of cryptic genes expands the usage of genomic data for pharmaceutical purposes.

  3. Reconciled Rat and Human Metabolic Networks for Comparative Toxicogenomics and Biomarker Predictions

    Science.gov (United States)

    2017-02-08

    reactions. ETC, electron transport chain; PPP, pentose phosphate pathway. NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14250 ARTICLE NATURE COMMUNICATIONS...CDCA) Alpha-muricholic acid (αMCA) x Beta-muricholic acid (βMCA) x Secondary bile acids Deoxycholic acid (DCA) Lithocholic acid ( LCA ) Hyocholic acid...than rats Neu5Ac Lewisa ManNAc Microbial metabolism MDCA Cyp3a18 Cyp3a18 CDCA LCA CDCA LCA SharedRat-specific Human-specific Figure 3 | Functional

  4. Multi-OMIC profiling of survival and metabolic signaling networks in cells subjected to photodynamic therapy.

    Science.gov (United States)

    Weijer, Ruud; Clavier, Séverine; Zaal, Esther A; Pijls, Maud M E; van Kooten, Robert T; Vermaas, Klaas; Leen, René; Jongejan, Aldo; Moerland, Perry D; van Kampen, Antoine H C; van Kuilenburg, André B P; Berkers, Celia R; Lemeer, Simone; Heger, Michal

    2017-03-01

    Photodynamic therapy (PDT) is an established palliative treatment for perihilar cholangiocarcinoma that is clinically promising. However, tumors tend to regrow after PDT, which may result from the PDT-induced activation of survival pathways in sublethally afflicted tumor cells. In this study, tumor-comprising cells (i.e., vascular endothelial cells, macrophages, perihilar cholangiocarcinoma cells, and EGFR-overexpressing epidermoid cancer cells) were treated with the photosensitizer zinc phthalocyanine that was encapsulated in cationic liposomes (ZPCLs). The post-PDT survival pathways and metabolism were studied following sublethal (LC 50 ) and supralethal (LC 90 ) PDT. Sublethal PDT induced survival signaling in perihilar cholangiocarcinoma (SK-ChA-1) cells via mainly HIF-1-, NF-кB-, AP-1-, and heat shock factor (HSF)-mediated pathways. In contrast, supralethal PDT damage was associated with a dampened survival response. PDT-subjected SK-ChA-1 cells downregulated proteins associated with EGFR signaling, particularly at LC 90 . PDT also affected various components of glycolysis and the tricarboxylic acid cycle as well as metabolites involved in redox signaling. In conclusion, sublethal PDT activates multiple pathways in tumor-associated cell types that transcriptionally regulate cell survival, proliferation, energy metabolism, detoxification, inflammation/angiogenesis, and metastasis. Accordingly, tumor cells sublethally afflicted by PDT are a major therapeutic culprit. Our multi-omic analysis further unveiled multiple druggable targets for pharmacological co-intervention.

  5. Identifying genes and gene networks involved in chromium metabolism and detoxification in Crambe abyssinica

    Energy Technology Data Exchange (ETDEWEB)

    Zulfiqar, Asma, E-mail: asmazulfiqar08@yahoo.com [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Paulose, Bibin, E-mail: bpaulose@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Chhikara, Sudesh, E-mail: sudesh@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Dhankher, Om Parkash, E-mail: parkash@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States)

    2011-10-15

    Chromium pollution is a serious environmental problem with few cost-effective remediation strategies available. Crambe abyssinica (a member of Brassicaseae), a non-food, fast growing high biomass crop, is an ideal candidate for phytoremediation of heavy metals contaminated soils. The present study used a PCR-Select Suppression Subtraction Hybridization approach in C. abyssinica to isolate differentially expressed genes in response to Cr exposure. A total of 72 differentially expressed subtracted cDNAs were sequenced and found to represent 43 genes. The subtracted cDNAs suggest that Cr stress significantly affects pathways related to stress/defense, ion transporters, sulfur assimilation, cell signaling, protein degradation, photosynthesis and cell metabolism. The regulation of these genes in response to Cr exposure was further confirmed by semi-quantitative RT-PCR. Characterization of these differentially expressed genes may enable the engineering of non-food, high-biomass plants, including C. abyssinica, for phytoremediation of Cr-contaminated soils and sediments. - Highlights: > Molecular mechanism of Cr uptake and detoxification in plants is not well known. > We identified differentially regulated genes upon Cr exposure in Crambe abyssinica. > 72 Cr-induced subtracted cDNAs were sequenced and found to represent 43 genes. > Pathways linked to stress, ion transport, and sulfur assimilation were affected. > This is the first Cr transcriptome study in a crop with phytoremediation potential. - This study describes the identification and isolation of differentially expressed genes involved in chromium metabolism and detoxification in a non-food industrial oil crop Crambe abyssinica.

  6. Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.

    Science.gov (United States)

    Megchelenbrink, Wout; Katzir, Rotem; Lu, Xiaowen; Ruppin, Eytan; Notebaart, Richard A

    2015-09-29

    Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.

  7. Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

    Science.gov (United States)

    Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Riera-Fernández, Pablo; López-Díaz, Antonio; Pazos, Alejandro; González-Díaz, Humberto

    2014-01-27

    The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W

  8. Genealogy profiling through strain improvement by using metabolic network analysis: metabolic flux genealogy of several generations of lysine-producing corynebacteria.

    Science.gov (United States)

    Wittmann, Christoph; Heinzle, Elmar

    2002-12-01

    A comprehensive approach of metabolite balancing, (13)C tracer studies, gas chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time of flight mass spectrometry, and isotopomer modeling was applied for comparative metabolic network analysis of a genealogy of five successive generations of lysine-producing Corynebacterium glutamicum. The five strains examined (C. glutamicum ATCC 13032, 13287, 21253, 21526, and 21543) were previously obtained by random mutagenesis and selection. Throughout the genealogy, the lysine yield in batch cultures increased markedly from 1.2 to 24.9% relative to the glucose uptake flux. Strain optimization was accompanied by significant changes in intracellular flux distributions. The relative pentose phosphate pathway (PPP) flux successively increased, clearly corresponding to the product yield. Moreover, the anaplerotic net flux increased almost twofold as a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes to cover the increased demand for lysine formation; thus, the overall increase was a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes. The relative flux through isocitrate dehydrogenase dropped from 82.7% in the wild type to 59.9% in the lysine-producing mutants. In contrast to the NADPH demand, which increased from 109 to 172% due to the increasing lysine yield, the overall NADPH supply remained constant between 185 and 196%, resulting in a decrease in the apparent NADPH excess through strain optimization. Extrapolated to industrial lysine producers, the NADPH supply might become a limiting factor. The relative contributions of PPP and the tricarboxylic acid cycle to NADPH generation changed markedly, indicating that C. glutamicum is able to maintain a constant supply of NADPH under completely different flux conditions. Statistical analysis by a Monte Carlo approach revealed high precision for the estimated fluxes, underlining the

  9. In Search of Practitioner-Based Social Capital: A Social Network Analysis Tool for Understanding and Facilitating Teacher Collaboration in a US-Based STEM Professional Development Program

    Science.gov (United States)

    Baker-Doyle, Kira J.; Yoon, Susan A.

    2011-01-01

    This paper presents the first in a series of studies on the informal advice networks of a community of teachers in an in-service professional development program. The aim of the research was to use Social Network Analysis as a methodological tool to reveal the social networks developed by the teachers, and to examine whether these networks…

  10. Extension of life span by impaired glucose metabolism in Caenorhabditis elegans is accompanied by structural rearrangements of the transcriptomic network.

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    Steffen Priebe

    Full Text Available Glucose restriction mimicked by feeding the roundworm Caenorhabditis elegans with 2-deoxy-D-glucose (DOG - a glucose molecule that lacks the ability to undergo glycolysis - has been found to increase the life span of the nematodes considerably. To facilitate understanding of the molecular mechanisms behind this life extension, we analyzed transcriptomes of DOG-treated and untreated roundworms obtained by RNA-seq at different ages. We found that, depending on age, DOG changes the magnitude of the expression values of about 2 to 24 percent of the genes significantly, although our results reveal that the gross changes introduced by DOG are small compared to the age-induced changes. We found that 27 genes are constantly either up- or down-regulated by DOG over the whole life span, among them several members of the cytochrome P450 family. The monotonic change with age of the temporal expression patterns of the genes was investigated, leading to the result that 21 genes reverse their monotonic behaviour under impaired glycolysis. Put simply, the DOG-treatment reduces the gross transcriptional activity but increases the interconnectedness of gene expression. However, a detailed analysis of network parameters discloses that the introduced changes differ remarkably between individual signalling pathways. We found a reorganization of the hubs of the mTOR pathway when standard diet is replaced by DOG feeding. By constructing correlation based difference networks, we identified those signalling pathways that are most vigorously changed by impaired glycolysis. Taken together, we have found a number of genes and pathways that are potentially involved in the DOG-driven extension of life span of C. elegans. Furthermore, our results demonstrate how the network structure of ageing-relevant signalling pathways is reorganised under impaired glycolysis.

  11. Learning Metabolic Brain Networks in MCI and AD by Robustness and Leave-One-Out Analysis: An FDG-PET Study.

    Science.gov (United States)

    Yao, Zhijun; Hu, Bin; Chen, Xuejiao; Xie, Yuanwei; Gutknecht, Jürg; Majoe, Dennis

    2018-02-01

    This study attempted to better understand the properties associated with the metabolic brain network in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Graph theory was employed to investigate the topological organization of metabolic brain network among 86 patients with MCI, 89 patients with AD, and 97 normal controls (NCs) using 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) data. The whole brain was divided into 82 areas by Brodmann atlas to construct networks. We found that MCI and AD showed a loss of small-world properties and topological aberrations, and MCI showed an intermediate measurement between NC and AD. The networks of MCI and AD were vulnerable to attacks resulting from the altered topological pattern. Furthermore, individual contributions were correlated with Mini-Mental State Examination and Clinical Dementia Rating. The present study indicated that the topological patterns of the metabolic networks were aberrant in patients with MCI and AD, which may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI and AD.

  12. Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.

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    Dániel Bánky

    Full Text Available Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks that compensates for the low degree (non-hub vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well, but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus, and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures

  13. Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.

    Science.gov (United States)

    Bánky, Dániel; Iván, Gábor; Grolmusz, Vince

    2013-01-01

    Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well), but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus), and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures importance in the

  14. Facilitators and Barriers to Pre-Exposure Prophylaxis Willingness Among Young Men Who Have Sex with Men Who Use Geosocial Networking Applications in California.

    Science.gov (United States)

    Holloway, Ian W; Tan, Diane; Gildner, Jennifer L; Beougher, Sean C; Pulsipher, Craig; Montoya, Jorge A; Plant, Aaron; Leibowitz, Arleen

    2017-12-01

    While correlates of pre-exposure prophylaxis (PrEP) uptake have been explored among older men who have sex with men (MSM), less is known about the facilitators and barriers that encourage uptake among younger MSM (YMSM). This study explores the association between willingness to take PrEP and demographic characteristics, sexual risk, and substance use, and attitudinal factors among YMSM in California who use geosocial networking applications (GSN apps). Based on survey data from YMSM recruited through GSN apps (n = 687), PrEP willingness was positively associated with Hispanic ethnicity [adjusted odds ratio (aOR): 1.73; confidence interval (CI): 1.01-2.98; p = 0.046], concerns about drug effects (aOR: 0.46; CI: 0.33-0.65; p < 0.001), medical mistrust (aOR: 0.71; CI: 0.53-0.96; p < 0.001), and concerns about adherence (aOR: 0.65; CI: 0.49-0.89; p = 0.005). PrEP willingness was positively associated with medium (aOR: 1.87; CI: 1.14-3.07; p = 0.014) and high concern (aOR: 1.84; CI: 1.13-3.01; p < 0.001) about contracting HIV and perceived benefits of taking PrEP (aOR: 2.59; CI: 1.78-3.78; p < 0.001). In addition to emphasizing the benefits of using PrEP, campaigns that address concerns regarding adherence and side effects may increase interest in and demand for PrEP among YMSM. More opportunities are needed to educate YMSM about PrEP, including addressing their concerns about this new prevention strategy. Providers should speak openly and honestly to YMSM considering PrEP about what to do if side effects occur and how to handle missed doses. Outreach using GSN apps for PrEP education and screening may be an effective way to reach YMSM.

  15. GEMSiRV: a software platform for GEnome-scale metabolic model simulation, reconstruction and visualization.

    Science.gov (United States)

    Liao, Yu-Chieh; Tsai, Ming-Hsin; Chen, Feng-Chi; Hsiung, Chao A

    2012-07-01

    Genome-scale metabolic network models have become an indispensable part of the increasingly important field of systems biology. Metabolic systems biology studies usually include three major components-network model construction, objective- and experiment-guided model editing and visualization, and simulation studies based mainly on flux balance analyses. Bioinformatics tools are required to facilitate these complicated analyses. Although some of the required functions have been served separately by existing tools, a free software resource that simultaneously serves the needs of the three major components is not yet available. Here we present a software platform, GEMSiRV (GEnome-scale Metabolic model Simulation, Reconstruction and Visualization), to provide functionalities of easy metabolic network drafting and editing, amenable network visualization for experimental data integration and flux balance analysis tools for simulation studies. GEMSiRV comes with downloadable, ready-to-use public-domain metabolic models, reference metabolite/reaction databases and metabolic network maps, all of which can be input into GEMSiRV as the starting materials for network construction or simulation analyses. Furthermore, all of the GEMSiRV-generated metabolic models and analysis results, including projects in progress, can be easily exchanged in the research community. GEMSiRV is a powerful integrative resource that may facilitate the development of systems biology studies. The software is freely available on the web at http://sb.nhri.org.tw/GEMSiRV.

  16. Novel insights into E. coli's hexuronate metabolism: KduI facilitates the conversion of galacturonate and glucuronate under osmotic stress conditions.

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    Monique Rothe

    Full Text Available Using a gnotobiotic mouse model, we previously observed the upregulation of 2-deoxy-D-gluconate 3-dehydrogenase (KduD in intestinal E. coli of mice fed a lactose-rich diet and the downregulation of this enzyme and of 5-keto 4-deoxyuronate isomerase (KduI on a casein-rich diet. The present study aimed to define the role of the so far poorly characterized E. coli proteins KduD and KduI in vitro. Galacturonate and glucuronate induced kduD and kduI gene expression 3-fold and 7 to 11-fold, respectively, under aerobic conditions as well as 9 to 20-fold and 19 to 54-fold, respectively, under anaerobic conditions. KduI facilitated the breakdown of these hexuronates. In E. coli, galacturonate and glucuronate are normally degraded by UxaABC and UxuAB. However, osmotic stress represses the expression of the corresponding genes in an OxyR-dependent manner. When grown in the presence of galacturonate or glucuronate, kduID-deficient E. coli had a 30% to 80% lower maximal cell density and 1.5 to 2-fold longer doubling times under osmotic stress conditions than wild type E. coli. Growth on lactose promoted the intracellular formation of hexuronates, which possibly explain the induction of KduD on a lactose-rich diet. These results indicate a novel function of KduI and KduD in E. coli and demonstrate the crucial influence of osmotic stress on the gene expression of hexuronate degrading enzymes.

  17. Metagenomic signatures of a tropical mining-impacted stream reveal complex microbial and metabolic networks.

    Science.gov (United States)

    Reis, Mariana P; Dias, Marcela F; Costa, Patrícia S; Ávila, Marcelo P; Leite, Laura R; de Araújo, Flávio M G; Salim, Anna C M; Bucciarelli-Rodriguez, Mônica; Oliveira, Guilherme; Chartone-Souza, Edmar; Nascimento, Andréa M A

    2016-10-01

    Bacteria from aquatic ecosystems significantly contribute to biogeochemical cycles, but details of their community structure in tropical mining-impacted environments remain unexplored. In this study, we analyzed a bacterial community from circumneutral-pH tropical stream sediment by 16S rRNA and shotgun deep sequencing. Carrapatos stream sediment, which has been exposed to metal stress due to gold and iron mining (21 [g Fe]/kg), revealed a diverse community, with predominance of Proteobacteria (39.4%), Bacteroidetes (12.2%), and Parcubacteria (11.4%). Among Proteobacteria, the most abundant reads were assigned to neutrophilic iron-oxidizing taxa, such as Gallionella, Sideroxydans, and Mariprofundus, which are involved in Fe cycling and harbor several metal resistance genes. Functional analysis revealed a large number of genes participating in nitrogen and methane metabolic pathways despite the low concentrations of inorganic nitrogen in the Carrapatos stream. Our findings provide important insights into bacterial community interactions in a mining-impacted environment. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Genome-scale metabolic network validation of Shewanella oneidensis using transposon insertion frequency analysis.

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    Hong Yang

    2014-09-01

    Full Text Available Transposon mutagenesis, in combination with parallel sequencing, is becoming a powerful tool for en-masse mutant analysis. A probability generating function was used to explain observed miniHimar transposon insertion patterns, and gene essentiality calls were made by transposon insertion frequency analysis (TIFA. TIFA incorporated the observed genome and sequence motif bias of the miniHimar transposon. The gene essentiality calls were compared to: 1 previous genome-wide direct gene-essentiality assignments; and, 2 flux balance analysis (FBA predictions from an existing genome-scale metabolic model of Shewanella oneidensis MR-1. A three-way comparison between FBA, TIFA, and the direct essentiality calls was made to validate the TIFA approach. The refinement in the interpretation of observed transposon insertions demonstrated that genes without insertions are not necessarily essential, and that genes that contain insertions are not always nonessential. The TIFA calls were in reasonable agreement with direct essentiality calls for S. oneidensis, but agreed more closely with E. coli essentiality calls for orthologs. The TIFA gene essentiality calls were in good agreement with the MR-1 FBA essentiality predictions, and the agreement between TIFA and FBA predictions was substantially better than between the FBA and the direct gene essentiality predictions.

  19. Correlation-Based Network Generation, Visualization, and Analysis as a Powerful Tool in Biological Studies: A Case Study in Cancer Cell Metabolism

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    Albert Batushansky

    2016-01-01

    Full Text Available In the last decade vast data sets are being generated in biological and medical studies. The challenge lies in their summary, complexity reduction, and interpretation. Correlation-based networks and graph-theory based properties of this type of networks can be successfully used during this process. However, the procedure has its pitfalls and requires specific knowledge that often lays beyond classical biology and includes many computational tools and software. Here we introduce one of a series of methods for correlation-based network generation and analysis using freely available software. The pipeline allows the user to control each step of the network generation and provides flexibility in selection of correlation methods and thresholds. The pipeline was implemented on published metabolomics data of a population of human breast carcinoma cell lines MDA-MB-231 under two conditions: normal and hypoxia. The analysis revealed significant differences between the metabolic networks in response to the tested conditions. The network under hypoxia had 1.7 times more significant correlations between metabolites, compared to normal conditions. Unique metabolic interactions were identified which could lead to the identification of improved markers or aid in elucidating the mechanism of regulation between distantly related metabolites induced by the cancer growth.

  20. Characterization of the periplasmic redox network that sustains the versatile anaerobic metabolism of Shewanella oneidensis MR-1

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    Mónica N. Alves

    2015-06-01

    Full Text Available The versatile anaerobic metabolism of the Gram-negative bacterium Shewanella oneidensis MR-1 (SOMR-1 relies on a multitude of redox proteins found in its periplasm. Most are multiheme cytochromes that carry electrons to terminal reductases of insoluble electron acceptors located at the cell surface, or bona fide terminal reductases of soluble electron acceptors. In this study, the interaction network of several multiheme cytochromes was explored by a combination of NMR spectroscopy, activity assays followed by UV-visible spectroscopy and comparison of surface electrostatic potentials. From these data the small tetraheme cytochrome (STC emerges as the main periplasmic redox shuttle in SOMR-1. It accepts electrons from CymA and distributes them to a number of terminal oxidoreductases involved in the respiration of various compounds. STC is also involved in the electron transfer pathway to reduce nitrite by interaction with the octaheme tetrathionate reductase (OTR, but not with cytochrome c nitrite reductase (ccNiR. In the main pathway leading the metal respiration STC pairs with flavocytochrome c (FccA, the other major periplasmic cytochrome, which provides redundancy in this important pathway. The data reveals that the two proteins compete for the binding site at the surface of MtrA, the decaheme cytochrome inserted on the periplasmic side of the MtrCAB-OmcA outer-membrane complex. However, this is not observed for the MtrA homologues. Indeed, neither STC nor FccA interact with MtrD, the best replacement for MtrA, and only STC is able to interact with the decaheme cytochrome DmsE of the outer-membrane complex DmsEFABGH. Overall, these results shown that STC plays a central role in the anaerobic respiratory metabolism of SOMR-1. Nonetheless, the trans-periplasmic electron transfer chain is functionally resilient as a consequence of redundancies that arise from the presence of alternative pathways that bypass/compete with STC.

  1. Expression profiling of Crambe abyssinica under arsenate stress identifies genes and gene networks involved in arsenic metabolism and detoxification

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    Kandasamy Suganthi

    2010-06-01

    Full Text Available Abstract Background Arsenic contamination is widespread throughout the world and this toxic metalloid is known to cause cancers of organs such as liver, kidney, skin, and lung in human. In spite of a recent surge in arsenic related studies, we are still far from a comprehensive understanding of arsenic uptake, detoxification, and sequestration in plants. Crambe abyssinica, commonly known as 'abyssinian mustard', is a non-food, high biomass oil seed crop that is naturally tolerant to heavy metals. Moreover, it accumulates significantly higher levels of arsenic as compared to other species of the Brassicaceae family. Thus, C. abyssinica has great potential to be utilized as an ideal inedible crop for phytoremediation of heavy metals and metalloids. However, the mechanism of arsenic metabolism in higher plants, including C. abyssinica, remains elusive. Results To identify the differentially expressed transcripts and the pathways involved in arsenic metabolism and detoxification, C. abyssinica plants were subjected to arsenate stress and a PCR-Select Suppression Subtraction Hybridization (SSH approach was employed. A total of 105 differentially expressed subtracted cDNAs were sequenced which were found to represent 38 genes. Those genes encode proteins functioning as antioxidants, metal transporters, reductases, enzymes involved in the protein degradation pathway, and several novel uncharacterized proteins. The transcripts corresponding to the subtracted cDNAs showed strong upregulation by arsenate stress as confirmed by the semi-quantitative RT-PCR. Conclusions Our study revealed novel insights into the plant defense mechanisms and the regulation of genes and gene networks in response to arsenate toxicity. The differential expression of transcripts encoding glutathione-S-transferases, antioxidants, sulfur metabolism, heat-shock proteins, metal transporters, and enzymes in the ubiquitination pathway of protein degradation as well as several unknown

  2. A bacterial quercetin oxidoreductase QuoA-mediated perturbation in the phenylpropanoid metabolic network increases lignification with a concomitant decrease in phenolamides in Arabidopsis

    Science.gov (United States)

    Swarup, Sanjay

    2013-01-01

    Metabolic perturbations by a gain-of-function approach provide a means to alter steady states of metabolites and query network properties, while keeping enzyme complexes intact. A combination of genetic and targeted metabolomics approach was used to understand the network properties of phenylpropanoid secondary metabolism pathways. A novel quercetin oxidoreductase, QuoA, from Pseudomonas putida, which converts quercetin to naringenin, thus effectively reversing the biosynthesis of quercetin through a de novo pathway, was expressed in Arabidopsis thaliana. QuoA transgenic lines selected for low, medium, and high expression levels of QuoA RNA had corresponding levels of QuoA activity and hypocotyl coloration resulting from increased anthocyanin accumulation. Stems of all three QuoA lines had increased tensile strength resulting from increased lignification. Sixteen metabolic intermediates from anthocyanin, lignin, and shikimate pathways had increased accumulation, of which 11 paralleled QuoA expression levels in the transgenic lines. The concomitant upregulation of the above pathways was explained by a significant downregulation of the phenolamide pathway and its precursor, spermidine. In a tt6 mutant line, lignifications as well as levels of the lignin pathway metabolites were much lower than those of QuoA transgenic lines. Unlike QuoA lines, phenolamides and spermidine were not affected in the tt6 line. Taken together, these results suggest that phenolamide pathway plays a major role in directing metabolic intermediates into the lignin pathway. Metabolic perturbations were accompanied by downregulation of five genes associated with branch-point enzymes and upregulation of their corresponding products. These results suggest that gene–metabolite pairs are likely to be co-ordinately regulated at critical branch points. Thus, these perturbations by a gain-of-function approach have uncovered novel properties of the phenylpropanoid metabolic network. PMID:24085580

  3. A bacterial quercetin oxidoreductase QuoA-mediated perturbation in the phenylpropanoid metabolic network increases lignification with a concomitant decrease in phenolamides in Arabidopsis.

    Science.gov (United States)

    Reuben, Sheela; Rai, Amit; Pillai, Bhinu V S; Rodrigues, Amrith; Swarup, Sanjay

    2013-11-01

    Metabolic perturbations by a gain-of-function approach provide a means to alter steady states of metabolites and query network properties, while keeping enzyme complexes intact. A combination of genetic and targeted metabolomics approach was used to understand the network properties of phenylpropanoid secondary metabolism pathways. A novel quercetin oxidoreductase, QuoA, from Pseudomonas putida, which converts quercetin to naringenin, thus effectively reversing the biosynthesis of quercetin through a de novo pathway, was expressed in Arabidopsis thaliana. QuoA transgenic lines selected for low, medium, and high expression levels of QuoA RNA had corresponding levels of QuoA activity and hypocotyl coloration resulting from increased anthocyanin accumulation. Stems of all three QuoA lines had increased tensile strength resulting from increased lignification. Sixteen metabolic intermediates from anthocyanin, lignin, and shikimate pathways had increased accumulation, of which 11 paralleled QuoA expression levels in the transgenic lines. The concomitant upregulation of the above pathways was explained by a significant downregulation of the phenolamide pathway and its precursor, spermidine. In a tt6 mutant line, lignifications as well as levels of the lignin pathway metabolites were much lower than those of QuoA transgenic lines. Unlike QuoA lines, phenolamides and spermidine were not affected in the tt6 line. Taken together, these results suggest that phenolamide pathway plays a major role in directing metabolic intermediates into the lignin pathway. Metabolic perturbations were accompanied by downregulation of five genes associated with branch-point enzymes and upregulation of their corresponding products. These results suggest that gene-metabolite pairs are likely to be co-ordinately regulated at critical branch points. Thus, these perturbations by a gain-of-function approach have uncovered novel properties of the phenylpropanoid metabolic network.

  4. De Novo transcriptome sequencing reveals important molecular networks and metabolic pathways of the plant, Chlorophytum borivilianum.

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    Shikha Kalra

    Full Text Available Chlorophytum borivilianum, an endangered medicinal plant species is highly recognized for its aphrodisiac properties provided by saponins present in the plant. The transcriptome information of this species is limited and only few hundred expressed sequence tags (ESTs are available in the public databases. To gain molecular insight of this plant, high throughput transcriptome sequencing of leaf RNA was carried out using Illumina's HiSeq 2000 sequencing platform. A total of 22,161,444 single end reads were retrieved after quality filtering. Available (e.g., De-Bruijn/Eulerian graph and in-house developed bioinformatics tools were used for assembly and annotation of transcriptome. A total of 101,141 assembled transcripts were obtained, with coverage size of 22.42 Mb and average length of 221 bp. Guanine-cytosine (GC content was found to be 44%. Bioinformatics analysis, using non-redundant proteins, gene ontology (GO, enzyme commission (EC and kyoto encyclopedia of genes and genomes (KEGG databases, extracted all the known enzymes involved in saponin and flavonoid biosynthesis. Few genes of the alkaloid biosynthesis, along with anticancer and plant defense genes, were also discovered. Additionally, several cytochrome P450 (CYP450 and glycosyltransferase unique sequences were also found. We identified simple sequence repeat motifs in transcripts with an abundance of di-nucleotide simple sequence repeat (SSR; 43.1% markers. Large scale expression profiling through Reads per Kilobase per Million mapped reads (RPKM showed major genes involved in different metabolic pathways of the plant. Genes, expressed sequence tags (ESTs and unique sequences from this study provide an important resource for the scientific community, interested in the molecular genetics and functional genomics of C. borivilianum.

  5. De Novo transcriptome sequencing reveals important molecular networks and metabolic pathways of the plant, Chlorophytum borivilianum.

    Science.gov (United States)

    Kalra, Shikha; Puniya, Bhanwar Lal; Kulshreshtha, Deepika; Kumar, Sunil; Kaur, Jagdeep; Ramachandran, Srinivasan; Singh, Kashmir

    2013-01-01

    Chlorophytum borivilianum, an endangered medicinal plant species is highly recognized for its aphrodisiac properties provided by saponins present in the plant. The transcriptome information of this species is limited and only few hundred expressed sequence tags (ESTs) are available in the public databases. To gain molecular insight of this plant, high throughput transcriptome sequencing of leaf RNA was carried out using Illumina's HiSeq 2000 sequencing platform. A total of 22,161,444 single end reads were retrieved after quality filtering. Available (e.g., De-Bruijn/Eulerian graph) and in-house developed bioinformatics tools were used for assembly and annotation of transcriptome. A total of 101,141 assembled transcripts were obtained, with coverage size of 22.42 Mb and average length of 221 bp. Guanine-cytosine (GC) content was found to be 44%. Bioinformatics analysis, using non-redundant proteins, gene ontology (GO), enzyme commission (EC) and kyoto encyclopedia of genes and genomes (KEGG) databases, extracted all the known enzymes involved in saponin and flavonoid biosynthesis. Few genes of the alkaloid biosynthesis, along with anticancer and plant defense genes, were also discovered. Additionally, several cytochrome P450 (CYP450) and glycosyltransferase unique sequences were also found. We identified simple sequence repeat motifs in transcripts with an abundance of di-nucleotide simple sequence repeat (SSR; 43.1%) markers. Large scale expression profiling through Reads per Kilobase per Million mapped reads (RPKM) showed major genes involved in different metabolic pathways of the plant. Genes, expressed sequence tags (ESTs) and unique sequences from this study provide an important resource for the scientific community, interested in the molecular genetics and functional genomics of C. borivilianum.

  6. IdentiCS – Identification of coding sequence and in silico reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence

    Directory of Open Access Journals (Sweden)

    Zeng An-Ping

    2004-08-01

    Full Text Available Abstract Background A necessary step for a genome level analysis of the cellular metabolism is the in silico reconstruction of the metabolic network from genome sequences. The available methods are mainly based on the annotation of genome sequences including two successive steps, the prediction of coding sequences (CDS and their function assignment. The annotation process takes time. The available methods often encounter difficulties when dealing with unfinished error-containing genomic sequence. Results In this work a fast method is proposed to use unannotated genome sequence for predicting CDSs and for an in silico reconstruction of metabolic networks. Instead of using predicted genes or CDSs to query public databases, entries from public DNA or protein databases are used as queries to search a local database of the unannotated genome sequence to predict CDSs. Functions are assigned to the predicted CDSs simultaneously. The well-annotated genome of Salmonella typhimurium LT2 is used as an example to demonstrate the applicability of the method. 97.7% of the CDSs in the original annotation are correctly identified. The use of SWISS-PROT-TrEMBL databases resulted in an identification of 98.9% of CDSs that have EC-numbers in the published annotation. Furthermore, two versions of sequences of the bacterium Klebsiella pneumoniae with different genome coverage (3.9 and 7.9 fold, respectively are examined. The results suggest that a 3.9-fold coverage of the bacterial genome could be sufficiently used for the in silico reconstruction of the metabolic network. Compared to other gene finding methods such as CRITICA our method is more suitable for exploiting sequences of low genome coverage. Based on the new method, a program called IdentiCS (Identification of Coding Sequences from Unfinished Genome Sequences is delivered that combines the identification of CDSs with the reconstruction, comparison and visualization of metabolic networks (free to download

  7. Creating a peer-driven learning network in higher education – using Web 2.0 tools to facilitate online dialogue and collaboration

    DEFF Research Database (Denmark)

    Nicolajsen, Hanne Westh; Ryberg, Thomas

    2014-01-01

    learning networks or engaging in web-based activities particularly related to learning or academia (Clark et al. 2009, Luckin et al. 2009). We argue that learning networks based on social media and employed for academic purposes may challenge the traditional norms and practices for both teachers...

  8. Identifying essential genes in bacterial metabolic networks with machine learning methods

    Science.gov (United States)

    2010-01-01

    Background Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective. Results We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway. Conclusions Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism. PMID:20438628

  9. Identifying essential genes in bacterial metabolic networks with machine learning methods.

    Science.gov (United States)

    Plaimas, Kitiporn; Eils, Roland; König, Rainer

    2010-05-03

    Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective. We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway. Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism.

  10. Extraction of elementary rate constants from global network analysis of E. coli central metabolism

    Science.gov (United States)

    Zhao, Jiao; Ridgway, Douglas; Broderick, Gordon; Kovalenko, Andriy; Ellison, Michael

    2008-01-01

    Background As computational performance steadily increases, so does interest in extending one-particle-per-molecule models to larger physiological problems. Such models however require elementary rate constants to calculate time-dependent rate coefficients under physiological conditions. Unfortunately, even when in vivo kinetic data is available, it is often in the form of aggregated rate laws (ARL) that do not specify the required elementary rate constants corresponding to mass-action rate laws (MRL). There is therefore a need to develop a method which is capable of automatically transforming ARL kinetic information into more detailed MRL rate constants. Results By incorporating proteomic data related to enzyme abundance into an MRL modelling framework, here we present an efficient method operating at a global network level for extracting elementary rate constants from experiment-based aggregated rate law (ARL) models. The method combines two techniques that can be used to overcome the difficult properties in parameterization. The first, a hybrid MRL/ARL modelling technique, is used to divide the parameter estimation problem into sub-problems, so that the parameters of the mass action rate laws for each enzyme are estimated in separate steps. This reduces the number of parameters that have to be optimized simultaneously. The second, a hybrid algebraic-numerical simulation and optimization approach, is used to render some rate constants identifiable, as well as to greatly narrow the bounds of the other rate constants that remain unidentifiable. This is done by incorporating equality constraints derived from the King-Altman and Cleland method into the simulated annealing algorithm. We apply these two techniques to estimate the rate constants of a model of E. coli glycolytic pathways. The simulation and statistical results show that our innovative method performs well in dealing with the issues of high computation cost, stiffness, local minima and uncertainty

  11. Energetic funnel facilitates facilitated diffusion.

    Science.gov (United States)

    Cencini, Massimo; Pigolotti, Simone

    2018-01-25

    Transcription factors (TFs) are able to associate to their binding sites on DNA faster than the physical limit posed by diffusion. Such high association rates can be achieved by alternating between three-dimensional diffusion and one-dimensional sliding along the DNA chain, a mechanism-dubbed facilitated diffusion. By studying a collection of TF binding sites of Escherichia coli from the RegulonDB database and of Bacillus subtilis from DBTBS, we reveal a funnel in the binding energy landscape around the target sequences. We show that such a funnel is linked to the presence of gradients of AT in the base composition of the DNA region around the binding sites. An extensive computational study of the stochastic sliding process along the energetic landscapes obtained from the database shows that the funnel can significantly enhance the probability of TFs to find their target sequences when sliding in their proximity. We demonstrate that this enhancement leads to a speed-up of the association process. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  12. Facilitating the use of evidence for decision-making – a review of 64 WHO Health Evidence Network synthesis reports and its impact

    DEFF Research Database (Denmark)

    Nguen, Tim; Takahashi, Ryoko; Kuchenmueller, Tanja

    in selecting an appropriate literature search and synthesis method and writing specifically for policy-makers in mind. To facilitate the uptake of evidence in policy-making, HEN collaborates with decision-makers in identifying priority health policy areas, framing a synthesis question and disseminating...

  13. Abnormal metabolic brain network associated with Parkinson's disease: replication on a new European sample

    Energy Technology Data Exchange (ETDEWEB)

    Tomse, Petra; Jensterle, Luka; Grmek, Marko; Zaletel, Katja [University Medical Centre Ljubljana, Department of Nuclear Medicine, Ljubljana (Slovenia); Pirtosek, Zvezdan; Trost, Maja [University Medical Centre Ljubljana, Department of Neurology, 1000 Ljubljana (Slovenia); Dhawan, Vijay; Peng, Shichun; Eidelberg, David; Ma, Yilong [The Feinstein Institute for Medical Research, Center for Neurosciences, Manhasset, NY (United States)

    2017-05-15

    The purpose of this study was to identify the specific metabolic brain pattern characteristic for Parkinson's disease (PD): Parkinson's disease-related pattern (PDRP), using network analysis of [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images in a cohort of Slovenian PD patients. Twenty PD patients (age 70.1 ± 7.8 years, Movement Disorder Society Unified Parkinson's Disease Motor Rating Scale (MDS-UPDRS-III) 38.3 ± 12.2; disease duration 4.3 ± 4.1 years) and 20 age-matched normal controls (NCs) underwent FDG-PET brain imaging. An automatic voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was applied to these scans for PDRP-Slovenia identification. The pattern was characterized by relative hypermetabolism in pallidum, putamen, thalamus, brain stem, and cerebellum associated with hypometabolism in sensorimotor cortex, posterior parietal, occipital, and frontal cortices. The expression of PDRP-Slovenia discriminated PD patients from NCs (p < 0.0001) and correlated positively with patients' clinical score (MDS-UPDRS-III, p = 0.03). Additionally, its topography agrees well with the original PDRP (p < 0.001) identified in American cohort of PD patients. We validated the PDRP-Slovenia expression on additional FDG-PET scans of 20 PD patients, 20 NCs, and 25 patients with atypical parkinsonism (AP). We confirmed that the expression of PDRP-Slovenia manifests good diagnostic accuracy with specificity and sensitivity of 85-90% at optimal pattern expression cutoff for discrimination of PD patients and NCs and is not expressed in AP. PDRP-Slovenia proves to be a robust and reproducible functional imaging biomarker independent of patient population. It accurately differentiates PD patients from NCs and AP and correlates well with the clinical measure of PD progression. (orig.)

  14. Abnormal metabolic brain network associated with Parkinson's disease: replication on a new European sample

    International Nuclear Information System (INIS)

    Tomse, Petra; Jensterle, Luka; Grmek, Marko; Zaletel, Katja; Pirtosek, Zvezdan; Trost, Maja; Dhawan, Vijay; Peng, Shichun; Eidelberg, David; Ma, Yilong

    2017-01-01

    The purpose of this study was to identify the specific metabolic brain pattern characteristic for Parkinson's disease (PD): Parkinson's disease-related pattern (PDRP), using network analysis of [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images in a cohort of Slovenian PD patients. Twenty PD patients (age 70.1 ± 7.8 years, Movement Disorder Society Unified Parkinson's Disease Motor Rating Scale (MDS-UPDRS-III) 38.3 ± 12.2; disease duration 4.3 ± 4.1 years) and 20 age-matched normal controls (NCs) underwent FDG-PET brain imaging. An automatic voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was applied to these scans for PDRP-Slovenia identification. The pattern was characterized by relative hypermetabolism in pallidum, putamen, thalamus, brain stem, and cerebellum associated with hypometabolism in sensorimotor cortex, posterior parietal, occipital, and frontal cortices. The expression of PDRP-Slovenia discriminated PD patients from NCs (p < 0.0001) and correlated positively with patients' clinical score (MDS-UPDRS-III, p = 0.03). Additionally, its topography agrees well with the original PDRP (p < 0.001) identified in American cohort of PD patients. We validated the PDRP-Slovenia expression on additional FDG-PET scans of 20 PD patients, 20 NCs, and 25 patients with atypical parkinsonism (AP). We confirmed that the expression of PDRP-Slovenia manifests good diagnostic accuracy with specificity and sensitivity of 85-90% at optimal pattern expression cutoff for discrimination of PD patients and NCs and is not expressed in AP. PDRP-Slovenia proves to be a robust and reproducible functional imaging biomarker independent of patient population. It accurately differentiates PD patients from NCs and AP and correlates well with the clinical measure of PD progression. (orig.)

  15. Differential RNA-seq, Multi-Network Analysis and Metabolic Regulation Analysis of Kluyveromyces marxianus Reveals a Compartmentalised Response to Xylose.

    Directory of Open Access Journals (Sweden)

    Du Toit W P Schabort

    Full Text Available We investigated the transcriptomic response of a new strain of the yeast Kluyveromyces marxianus, in glucose and xylose media using RNA-seq. The data were explored in a number of innovative ways using a variety of networks types, pathway maps, enrichment statistics, reporter metabolites and a flux simulation model, revealing different aspects of the genome-scale response in an integrative systems biology manner. The importance of the subcellular localisation in the transcriptomic response is emphasised here, revealing new insights. As was previously reported by others using a rich medium, we show that peroxisomal fatty acid catabolism was dramatically up-regulated in a defined xylose mineral medium without fatty acids, along with mechanisms to activate fatty acids and transfer products of β-oxidation to the mitochondria. Notably, we observed a strong up-regulation of the 2-methylcitrate pathway, supporting capacity for odd-chain fatty acid catabolism. Next we asked which pathways would respond to the additional requirement for NADPH for xylose utilisation, and rationalised the unexpected results using simulations with Flux Balance Analysis. On a fundamental level, we investigated the contribution of the hierarchical and metabolic regulation levels to the regulation of metabolic fluxes. Metabolic regulation analysis suggested that genetic level regulation plays a major role in regulating metabolic fluxes in adaptation to xylose, even for the high capacity reactions, which is unexpected. In addition, isozyme switching may play an important role in re-routing of metabolic fluxes in subcellular compartments in K. marxianus.

  16. BioMet Toolbox: genome-wide analysis of metabolism

    DEFF Research Database (Denmark)

    Cvijovic, M.; Olivares Hernandez, Roberto; Agren, R.

    2010-01-01

    standardized simulations. Model files for various model organisms (fungi and bacteria) are included. Overall, the BioMet Toolbox serves as a valuable resource for exploring the capabilities of these metabolic networks. BioMet Toolbox is freely available at www.sysbio.se/BioMet/....... models. Systematic analysis of biological processes by means of modelling and simulations has made the identification of metabolic networks and prediction of metabolic capabilities under different conditions possible. For facilitating such systemic analysis, we have developed the BioMet Toolbox, a web......-based resource for stoichiometric analysis and for integration of transcriptome and interactome data, thereby exploiting the capabilities of genome-scale metabolic models. The BioMet Toolbox provides an effective user-friendly way to perform linear programming simulations towards maximized or minimized growth...

  17. Investigating Moorella thermoacetica metabolism with a genome-scale constraint-based metabolic model.

    Science.gov (United States)

    Islam, M Ahsanul; Zengler, Karsten; Edwards, Elizabeth A; Mahadevan, Radhakrishnan; Stephanopoulos, Gregory

    2015-08-01

    Moorella thermoacetica is a strictly anaerobic, endospore-forming, and metabolically versatile acetogenic bacterium capable of conserving energy by both autotrophic (acetogenesis) and heterotrophic (homoacetogenesis) modes of metabolism. Its metabolic diversity and the ability to efficiently convert a wide range of compounds, including syngas (CO + H2) into acetyl-CoA have made this thermophilic bacterium a promising host for industrial biotechnology applications. However, lack of detailed information on M. thermoacetica's metabolism is a major impediment to its use as a microbial cell factory. In order to overcome this issue, a genome-scale constraint-based metabolic model of Moorella thermoacetica, iAI558, has been developed using its genome sequence and physiological data from published literature. The reconstructed metabolic network of M. thermoacetica comprises 558 metabolic genes, 705 biochemical reactions, and 698 metabolites. Of the total 705 model reactions, 680 are gene-associated while the rest are non-gene associated reactions. The model, in addition to simulating both autotrophic and heterotrophic growth of M. thermoacetica, revealed degeneracy in its TCA-cycle, a common characteristic of anaerobic metabolism. Furthermore, the model helped elucidate the poorly understood energy conservation mechanism of M. thermoacetica during autotrophy. Thus, in addition to generating experimentally testable hypotheses regarding its physiology, such a detailed model will facilitate rapid strain designing and metabolic engineering of M. thermoacetica for industrial applications.

  18. Correlation network analysis reveals relationships between diet-induced changes in human gut microbiota and metabolic health

    NARCIS (Netherlands)

    Kelder, T.; Stroeve, J.H.M.; Bijlsma, S.; Radonjic, M.; Roeselers, G.

    2014-01-01

    BACKGROUND: Recent evidence suggests that the gut microbiota plays an important role in human metabolism and energy homeostasis and is therefore a relevant factor in the assessment of metabolic health and flexibility. Understanding of these host–microbiome interactions aids the design of nutritional

  19. Soft Cysteine Signaling Network: The Functional Significance of Cysteine in Protein Function and the Soft Acids/Bases Thiol Chemistry That Facilitates Cysteine Modification.

    Science.gov (United States)

    Wible, Ryan S; Sutter, Thomas R

    2017-03-20

    The unique biophysical and electronic properties of cysteine make this molecule one of the most biologically critical amino acids in the proteome. The defining sulfur atom in cysteine is much larger than the oxygen and nitrogen atoms more commonly found in the other amino acids. As a result of its size, the valence electrons of sulfur are highly polarizable. Unique protein microenvironments favor the polarization of sulfur, thus increasing the overt reactivity of cysteine. Here, we provide a brief overview of the endogenous generation of reactive oxygen and electrophilic species and specific examples of enzymes and transcription factors in which the oxidation or covalent modification of cysteine in those proteins modulates their function. The perspective concludes with a discussion of cysteine chemistry and biophysics, the hard and soft acids and bases model, and the proposal of the Soft Cysteine Signaling Network: a hypothesis proposing the existence of a complex signaling network governed by layered chemical reactivity and cross-talk in which the chemical modification of reactive cysteine in biological networks triggers the reorganization of intracellular biochemistry to mitigate spikes in endogenous or exogenous oxidative or electrophilic stress.

  20. Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?

    Directory of Open Access Journals (Sweden)

    Parizad Babaei

    2014-01-01

    Full Text Available To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

  1. Modeling the differences in biochemical capabilities of pseudomonas species by flux balance analysis: how good are genome-scale metabolic networks at predicting the differences?

    Science.gov (United States)

    Babaei, Parizad; Ghasemi-Kahrizsangi, Tahereh; Marashi, Sayed-Amir

    2014-01-01

    To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

  2. Ecological network analysis for carbon metabolism of eco-industrial parks: a case study of a typical eco-industrial park in Beijing.

    Science.gov (United States)

    Lu, Yi; Chen, Bin; Feng, Kuishuang; Hubacek, Klaus

    2015-06-16

    Energy production and industrial processes are crucial economic sectors accounting for about 62% of greenhouse gas (GHG) emissions globally in 2012. Eco-industrial parks are practical attempts to mitigate GHG emissions through cooperation among businesses and the local community in order to reduce waste and pollution, efficiently share resources, and help with the pursuit of sustainable development. This work developed a framework based on ecological network analysis to trace carbon metabolic processes in eco-industrial parks and applied it to a typical eco-industrial park in Beijing. Our findings show that the entire metabolic system is dominated by supply of primary goods from the external environment and final demand. The more carbon flows through a sector, the more influence it would exert upon the whole system. External environment and energy providers are the most active and dominating part of the carbon metabolic system, which should be the first target to mitigate emissions by increasing efficiencies. The carbon metabolism of the eco-industrial park can be seen as an evolutionary system with high levels of efficiency, but this may come at the expense of larger levels of resilience. This work may provide a useful modeling framework for low-carbon design and management of industrial parks.

  3. Networking

    OpenAIRE

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; Fries, de; Skovlund, Louise

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social qualificati...

  4. The power of collaboration: using internet-based tools to facilitate networking and benchmarking within a consortium of academic health centers.

    Science.gov (United States)

    Korner, Eli J; Oinonen, Michael J; Browne, Robert C

    2003-02-01

    The University HealthSystem Consortium (UHC) represents a strategic alliance of 169 academic health centers and associated institutions engaged in knowledge sharing and idea-generation. The use of the Internet as a tool in the delivery of UHC's products and services has increased dramatically over the past year and will continue to increase during the foreseeable future. This paper examines the current state of UHC-member institution driven tools and services that utilize the Web as a fundamental component in their delivery. The evolution of knowledge management at UHC, its management information and reporting tools, and expansion of e-commerce provide real world examples of Internet use in health care delivery and management. Health care workers are using these Web-based tools to help manage rising costs and optimize patient outcomes. Policy, technical, and organizational issues must be resolved to facilitate rapid adoption of Internet applications.

  5. A new dynamical layout algorithm for complex biochemical reaction networks

    OpenAIRE

    Kummer Ursula; Wegner Katja

    2005-01-01

    Abstract Background To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every k...

  6. Heat-stabilised rice bran consumption by colorectal cancer survivors modulates stool metabolite profiles and metabolic networks: a randomised controlled trial.

    Science.gov (United States)

    Brown, Dustin G; Borresen, Erica C; Brown, Regina J; Ryan, Elizabeth P

    2017-05-01

    Rice bran (RB) consumption has been shown to reduce colorectal cancer (CRC) growth in mice and modify the human stool microbiome. Changes in host and microbial metabolism induced by RB consumption was hypothesised to modulate the stool metabolite profile in favour of promoting gut health and inhibiting CRC growth. The objective was to integrate gut microbial metabolite profiles and identify metabolic pathway networks for CRC chemoprevention using non-targeted metabolomics. In all, nineteen CRC survivors participated in a parallel randomised controlled dietary intervention trial that included daily consumption of study-provided foods with heat-stabilised RB (30 g/d) or no additional ingredient (control). Stool samples were collected at baseline and 4 weeks and analysed using GC-MS and ultra-performance liquid chromatography-MS. Stool metabolomics revealed 93 significantly different metabolites in individuals consuming RB. A 264-fold increase in β-hydroxyisovaleroylcarnitine and 18-fold increase in β-hydroxyisovalerate exemplified changes in leucine, isoleucine and valine metabolism in the RB group. A total of thirty-nine stool metabolites were significantly different between RB and control groups, including increased hesperidin (28-fold) and narirutin (14-fold). Metabolic pathways impacted in the RB group over time included advanced glycation end products, steroids and bile acids. Fatty acid, leucine/valine and vitamin B6 metabolic pathways were increased in RB compared with control. There were 453 metabolites identified in the RB food metabolome, thirty-nine of which were identified in stool from RB consumers. RB consumption favourably modulated the stool metabolome of CRC survivors and these findings suggest the need for continued dietary CRC chemoprevention efforts.

  7. A single-component multidrug transporter of the major facilitator superfamily is part of a network that protects Escherichia coli from bile salt stress.

    Science.gov (United States)

    Paul, Stephanie; Alegre, Kamela O; Holdsworth, Scarlett R; Rice, Matthew; Brown, James A; McVeigh, Paul; Kelly, Sharon M; Law, Christopher J

    2014-05-01

    Resistance to high concentrations of bile salts in the human intestinal tract is vital for the survival of enteric bacteria such as Escherichia coli. Although the tripartite AcrAB-TolC efflux system plays a significant role in this resistance, it is purported that other efflux pumps must also be involved. We provide evidence from a comprehensive suite of experiments performed at two different pH values (7.2 and 6.0) that reflect pH conditions that E. coli may encounter in human gut that MdtM, a single-component multidrug resistance transporter of the major facilitator superfamily, functions in bile salt resistance in E. coli by catalysing secondary active transport of bile salts out of the cell cytoplasm. Furthermore, assays performed on a chromosomal ΔacrB mutant transformed with multicopy plasmid encoding MdtM suggested a functional synergism between the single-component MdtM transporter and the tripartite AcrAB-TolC system that results in a multiplicative effect on resistance. Substrate binding experiments performed on purified MdtM demonstrated that the transporter binds to cholate and deoxycholate with micromolar affinity, and transport assays performed on inverted vesicles confirmed the capacity of MdtM to catalyse electrogenic bile salt/H(+) antiport. © 2014 The Authors. Molecular Microbiology published by John Wiley & Sons Ltd.

  8. LakeMetabolizer: An R package for estimating lake metabolism from free-water oxygen using diverse statistical models

    Science.gov (United States)

    Winslow, Luke; Zwart, Jacob A.; Batt, Ryan D.; Dugan, Hilary; Woolway, R. Iestyn; Corman, Jessica; Hanson, Paul C.; Read, Jordan S.

    2016-01-01

    Metabolism is a fundamental process in ecosystems that crosses multiple scales of organization from individual organisms to whole ecosystems. To improve sharing and reuse of published metabolism models, we developed LakeMetabolizer, an R package for estimating lake metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMet