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Sample records for genome-scale constraint-based modeling

  1. A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

    Science.gov (United States)

    Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien

    2017-01-01

    Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

  2. A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

    Directory of Open Access Journals (Sweden)

    Marko Budinich

    Full Text Available Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA and multi-objective flux variability analysis (MO-FVA. Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity that take place at the ecosystem scale.

  3. Genome-Scale, Constraint-Based Modeling of Nitrogen Oxide Fluxes during Coculture of Nitrosomonas europaea and Nitrobacter winogradskyi

    Science.gov (United States)

    Giguere, Andrew T.; Murthy, Ganti S.; Bottomley, Peter J.; Sayavedra-Soto, Luis A.

    2018-01-01

    ABSTRACT Nitrification, the aerobic oxidation of ammonia to nitrate via nitrite, emits nitrogen (N) oxide gases (NO, NO2, and N2O), which are potentially hazardous compounds that contribute to global warming. To better understand the dynamics of nitrification-derived N oxide production, we conducted culturing experiments and used an integrative genome-scale, constraint-based approach to model N oxide gas sources and sinks during complete nitrification in an aerobic coculture of two model nitrifying bacteria, the ammonia-oxidizing bacterium Nitrosomonas europaea and the nitrite-oxidizing bacterium Nitrobacter winogradskyi. The model includes biotic genome-scale metabolic models (iFC578 and iFC579) for each nitrifier and abiotic N oxide reactions. Modeling suggested both biotic and abiotic reactions are important sources and sinks of N oxides, particularly under microaerobic conditions predicted to occur in coculture. In particular, integrative modeling suggested that previous models might have underestimated gross NO production during nitrification due to not taking into account its rapid oxidation in both aqueous and gas phases. The integrative model may be found at https://github.com/chaplenf/microBiome-v2.1. IMPORTANCE Modern agriculture is sustained by application of inorganic nitrogen (N) fertilizer in the form of ammonium (NH4+). Up to 60% of NH4+-based fertilizer can be lost through leaching of nitrifier-derived nitrate (NO3−), and through the emission of N oxide gases (i.e., nitric oxide [NO], N dioxide [NO2], and nitrous oxide [N2O] gases), the latter being a potent greenhouse gas. Our approach to modeling of nitrification suggests that both biotic and abiotic mechanisms function as important sources and sinks of N oxides during microaerobic conditions and that previous models might have underestimated gross NO production during nitrification. PMID:29577088

  4. Genome-Scale, Constraint-Based Modeling of Nitrogen Oxide Fluxes during Coculture of Nitrosomonas europaea and Nitrobacter winogradskyi.

    Science.gov (United States)

    Mellbye, Brett L; Giguere, Andrew T; Murthy, Ganti S; Bottomley, Peter J; Sayavedra-Soto, Luis A; Chaplen, Frank W R

    2018-01-01

    Nitrification, the aerobic oxidation of ammonia to nitrate via nitrite, emits nitrogen (N) oxide gases (NO, NO 2 , and N 2 O), which are potentially hazardous compounds that contribute to global warming. To better understand the dynamics of nitrification-derived N oxide production, we conducted culturing experiments and used an integrative genome-scale, constraint-based approach to model N oxide gas sources and sinks during complete nitrification in an aerobic coculture of two model nitrifying bacteria, the ammonia-oxidizing bacterium Nitrosomonas europaea and the nitrite-oxidizing bacterium Nitrobacter winogradskyi . The model includes biotic genome-scale metabolic models (iFC578 and iFC579) for each nitrifier and abiotic N oxide reactions. Modeling suggested both biotic and abiotic reactions are important sources and sinks of N oxides, particularly under microaerobic conditions predicted to occur in coculture. In particular, integrative modeling suggested that previous models might have underestimated gross NO production during nitrification due to not taking into account its rapid oxidation in both aqueous and gas phases. The integrative model may be found at https://github.com/chaplenf/microBiome-v2.1. IMPORTANCE Modern agriculture is sustained by application of inorganic nitrogen (N) fertilizer in the form of ammonium (NH 4 + ). Up to 60% of NH 4 + -based fertilizer can be lost through leaching of nitrifier-derived nitrate (NO 3 - ), and through the emission of N oxide gases (i.e., nitric oxide [NO], N dioxide [NO 2 ], and nitrous oxide [N 2 O] gases), the latter being a potent greenhouse gas. Our approach to modeling of nitrification suggests that both biotic and abiotic mechanisms function as important sources and sinks of N oxides during microaerobic conditions and that previous models might have underestimated gross NO production during nitrification.

  5. 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

  6. Genome-scale comparison and constraint-based metabolic reconstruction of the facultative anaerobic Fe(III-reducer Rhodoferax ferrireducens

    Directory of Open Access Journals (Sweden)

    Daugherty Sean

    2009-09-01

    Full Text Available Abstract Background Rhodoferax ferrireducens is a metabolically versatile, Fe(III-reducing, subsurface microorganism that is likely to play an important role in the carbon and metal cycles in the subsurface. It also has the unique ability to convert sugars to electricity, oxidizing the sugars to carbon dioxide with quantitative electron transfer to graphite electrodes in microbial fuel cells. In order to expand our limited knowledge about R. ferrireducens, the complete genome sequence of this organism was further annotated and then the physiology of R. ferrireducens was investigated with a constraint-based, genome-scale in silico metabolic model and laboratory studies. Results The iterative modeling and experimental approach unveiled exciting, previously unknown physiological features, including an expanded range of substrates that support growth, such as cellobiose and citrate, and provided additional insights into important features such as the stoichiometry of the electron transport chain and the ability to grow via fumarate dismutation. Further analysis explained why R. ferrireducens is unable to grow via photosynthesis or fermentation of sugars like other members of this genus and uncovered novel genes for benzoate metabolism. The genome also revealed that R. ferrireducens is well-adapted for growth in the subsurface because it appears to be capable of dealing with a number of environmental insults, including heavy metals, aromatic compounds, nutrient limitation and oxidative stress. Conclusion This study demonstrates that combining genome-scale modeling with the annotation of a new genome sequence can guide experimental studies and accelerate the understanding of the physiology of under-studied yet environmentally relevant microorganisms.

  7. Next-generation genome-scale models for metabolic engineering

    DEFF Research Database (Denmark)

    King, Zachary A.; Lloyd, Colton J.; Feist, Adam M.

    2015-01-01

    Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict...... examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering....

  8. Genome scale metabolic modeling of cancer

    DEFF Research Database (Denmark)

    Nilsson, Avlant; Nielsen, Jens

    2017-01-01

    of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome......Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization...

  9. Using Genome-scale Models to Predict Biological Capabilities

    DEFF Research Database (Denmark)

    O’Brien, Edward J.; Monk, Jonathan M.; Palsson, Bernhard O.

    2015-01-01

    Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellul...

  10. Genome-scale modeling for metabolic engineering.

    Science.gov (United States)

    Simeonidis, Evangelos; Price, Nathan D

    2015-03-01

    We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

  11. Constraints based analysis of extended cybernetic models.

    Science.gov (United States)

    Mandli, Aravinda R; Venkatesh, Kareenhalli V; Modak, Jayant M

    2015-11-01

    The cybernetic modeling framework provides an interesting approach to model the regulatory phenomena occurring in microorganisms. In the present work, we adopt a constraints based approach to analyze the nonlinear behavior of the extended equations of the cybernetic model. We first show that the cybernetic model exhibits linear growth behavior under the constraint of no resource allocation for the induction of the key enzyme. We then quantify the maximum achievable specific growth rate of microorganisms on mixtures of substitutable substrates under various kinds of regulation and show its use in gaining an understanding of the regulatory strategies of microorganisms. Finally, we show that Saccharomyces cerevisiae exhibits suboptimal dynamic growth with a long diauxic lag phase when growing on a mixture of glucose and galactose and discuss on its potential to achieve optimal growth with a significantly reduced diauxic lag period. The analysis carried out in the present study illustrates the utility of adopting a constraints based approach to understand the dynamic growth strategies of microorganisms. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  12. Genome-scale biological models for industrial microbial systems.

    Science.gov (United States)

    Xu, Nan; Ye, Chao; Liu, Liming

    2018-04-01

    The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

  13. Constraint-based modeling in microbial food biotechnology

    Science.gov (United States)

    Rau, Martin H.

    2018-01-01

    Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM. PMID:29588387

  14. Principles of proteome allocation are revealed using proteomic data and genome-scale models

    DEFF Research Database (Denmark)

    Yang, Laurence; Yurkovich, James T.; Lloyd, Colton J.

    2016-01-01

    to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the "generalist" (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions......Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked...... of these sectors for the general stress response sigma factor sigma(S). Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally...

  15. Use of genome-scale microbial models for metabolic engineering

    DEFF Research Database (Denmark)

    Patil, Kiran Raosaheb; Åkesson, M.; Nielsen, Jens

    2004-01-01

    Metabolic engineering serves as an integrated approach to design new cell factories by providing rational design procedures and valuable mathematical and experimental tools. Mathematical models have an important role for phenotypic analysis, but can also be used for the design of optimal metaboli...... network structures. The major challenge for metabolic engineering in the post-genomic era is to broaden its design methodologies to incorporate genome-scale biological data. Genome-scale stoichiometric models of microorganisms represent a first step in this direction....

  16. Modeling Lactococcus lactis using a genome-scale flux model

    Directory of Open Access Journals (Sweden)

    Nielsen Jens

    2005-06-01

    Full Text Available Abstract Background Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA and minimization of metabolic adjustment (MOMA were used as modeling frameworks. Results The metabolic network was reconstructed using the annotated genome sequence from L. lactis ssp. lactis IL1403 together with physiological and biochemical information. The established network comprised a total of 621 reactions and 509 metabolites, representing the overall metabolism of L. lactis. Experimental data reported in the literature was used to fit the model to phenotypic observations. Regulatory constraints had to be included to simulate certain metabolic features, such as the shift from homo to heterolactic fermentation. A minimal medium for in silico growth was identified, indicating the requirement of four amino acids in addition to a sugar. Remarkably, de novo biosynthesis of four other amino acids was observed even when all amino acids were supplied, which is in good agreement with experimental observations. Additionally, enhanced metabolic engineering strategies for improved diacetyl producing strains were designed. Conclusion The L. lactis metabolic network can now be used for a better understanding of lactococcal metabolic capabilities and potential, for the design of enhanced metabolic engineering strategies and for integration with other types of 'omic' data, to assist in finding new information on cellular organization and function.

  17. Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

    DEFF Research Database (Denmark)

    Ma, Ding; Yang, Laurence; Fleming, Ronan M. T.

    2017-01-01

    orders of magnitude. Data values also have greatly varying magnitudes. Standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME......Constraint-Based Reconstruction and Analysis (COBRA) is currently the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many...... models have 70,000 constraints and variables and will grow larger). We have developed a quadrupleprecision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging...

  18. Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model

    International Nuclear Information System (INIS)

    Fang, Yilin; Scheibe, Timothy D.; Mahadevan, Radhakrishnan; Garg, Srinath; Long, Philip E.; Lovley, Derek R.

    2011-01-01

    The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species, multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The

  19. Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model.

    Science.gov (United States)

    Fang, Yilin; Scheibe, Timothy D; Mahadevan, Radhakrishnan; Garg, Srinath; Long, Philip E; Lovley, Derek R

    2011-03-25

    The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint-based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species and multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The

  20. Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model

    Science.gov (United States)

    Fang, Yilin; Scheibe, Timothy D.; Mahadevan, Radhakrishnan; Garg, Srinath; Long, Philip E.; Lovley, Derek R.

    2011-03-01

    The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint-based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species and multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The

  1. Incorporating Protein Biosynthesis into the Saccharomyces cerevisiae Genome-scale Metabolic Model

    DEFF Research Database (Denmark)

    Olivares Hernandez, Roberto

    Based on stoichiometric biochemical equations that occur into the cell, the genome-scale metabolic models can quantify the metabolic fluxes, which are regarded as the final representation of the physiological state of the cell. For Saccharomyces Cerevisiae the genome scale model has been construc......Based on stoichiometric biochemical equations that occur into the cell, the genome-scale metabolic models can quantify the metabolic fluxes, which are regarded as the final representation of the physiological state of the cell. For Saccharomyces Cerevisiae the genome scale model has been...

  2. A constraint-based model of Scheffersomyces stipitis for improved ethanol production

    Directory of Open Access Journals (Sweden)

    Liu Ting

    2012-09-01

    Full Text Available Abstract Background As one of the best xylose utilization microorganisms, Scheffersomyces stipitis exhibits great potential for the efficient lignocellulosic biomass fermentation. Therefore, a comprehensive understanding of its unique physiological and metabolic characteristics is required to further improve its performance on cellulosic ethanol production. Results A constraint-based genome-scale metabolic model for S. stipitis CBS 6054 was developed on the basis of its genomic, transcriptomic and literature information. The model iTL885 consists of 885 genes, 870 metabolites, and 1240 reactions. During the reconstruction process, 36 putative sugar transporters were reannotated and the metabolisms of 7 sugars were illuminated. Essentiality study was conducted to predict essential genes on different growth media. Key factors affecting cell growth and ethanol formation were investigated by the use of constraint-based analysis. Furthermore, the uptake systems and metabolic routes of xylose were elucidated, and the optimization strategies for the overproduction of ethanol were proposed from both genetic and environmental perspectives. Conclusions Systems biology modelling has proven to be a powerful tool for targeting metabolic changes. Thus, this systematic investigation of the metabolism of S. stipitis could be used as a starting point for future experiment designs aimed at identifying the metabolic bottlenecks of this important yeast.

  3. Analysis of Piscirickettsia salmonis Metabolism Using Genome-Scale Reconstruction, Modeling, and Testing

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    María P. Cortés

    2017-12-01

    Full Text Available Piscirickettsia salmonis is an intracellular bacterial fish pathogen that causes piscirickettsiosis, a disease with highly adverse impact in the Chilean salmon farming industry. The development of effective treatment and control methods for piscireckttsiosis is still a challenge. To meet it the number of studies on P. salmonis has grown in the last couple of years but many aspects of the pathogen’s biology are still poorly understood. Studies on its metabolism are scarce and only recently a metabolic model for reference strain LF-89 was developed. We present a new genome-scale model for P. salmonis LF-89 with more than twice as many genes as in the previous model and incorporating specific elements of the fish pathogen metabolism. Comparative analysis with models of different bacterial pathogens revealed a lower flexibility in P. salmonis metabolic network. Through constraint-based analysis, we determined essential metabolites required for its growth and showed that it can benefit from different carbon sources tested experimentally in new defined media. We also built an additional model for strain A1-15972, and together with an analysis of P. salmonis pangenome, we identified metabolic features that differentiate two main species clades. Both models constitute a knowledge-base for P. salmonis metabolism and can be used to guide the efficient culture of the pathogen and the identification of specific drug targets.

  4. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

    DEFF Research Database (Denmark)

    King, Zachary A.; Lu, Justin; Dräger, Andreas

    2016-01-01

    Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repo...

  5. Use of an uncertainty analysis for genome-scale models as a prediction tool for microbial growth processes in subsurface environments.

    Science.gov (United States)

    Klier, Christine

    2012-03-06

    The integration of genome-scale, constraint-based models of microbial cell function into simulations of contaminant transport and fate in complex groundwater systems is a promising approach to help characterize the metabolic activities of microorganisms in natural environments. In constraint-based modeling, the specific uptake flux rates of external metabolites are usually determined by Michaelis-Menten kinetic theory. However, extensive data sets based on experimentally measured values are not always available. In this study, a genome-scale model of Pseudomonas putida was used to study the key issue of uncertainty arising from the parametrization of the influx of two growth-limiting substrates: oxygen and toluene. The results showed that simulated growth rates are highly sensitive to substrate affinity constants and that uncertainties in specific substrate uptake rates have a significant influence on the variability of simulated microbial growth. Michaelis-Menten kinetic theory does not, therefore, seem to be appropriate for descriptions of substrate uptake processes in the genome-scale model of P. putida. Microbial growth rates of P. putida in subsurface environments can only be accurately predicted if the processes of complex substrate transport and microbial uptake regulation are sufficiently understood in natural environments and if data-driven uptake flux constraints can be applied.

  6. Quantitative Assessment of Thermodynamic Constraints on the Solution Space of Genome-Scale Metabolic Models

    Science.gov (United States)

    Hamilton, Joshua J.; Dwivedi, Vivek; Reed, Jennifer L.

    2013-01-01

    Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations. PMID:23870272

  7. Quantitative assessment of thermodynamic constraints on the solution space of genome-scale metabolic models.

    Science.gov (United States)

    Hamilton, Joshua J; Dwivedi, Vivek; Reed, Jennifer L

    2013-07-16

    Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  8. Augment clinical measurement using a constraint-based esophageal model

    Science.gov (United States)

    Kou, Wenjun; Acharya, Shashank; Kahrilas, Peter; Patankar, Neelesh; Pandolfino, John

    2017-11-01

    Quantifying the mechanical properties of the esophageal wall is crucial to understanding impairments of trans-esophageal flow characteristic of several esophageal diseases. However, these data are unavailable owing to technological limitations of current clinical diagnostic instruments that instead display esophageal luminal cross sectional area based on intraluminal impedance change. In this work, we developed an esophageal model to predict bolus flow and the wall property based on clinical measurements. The model used the constraint-based immersed-boundary method developed previously by our group. Specifically, we first approximate the time-dependent wall geometry based on impedance planimetry data on luminal cross sectional area. We then fed these along with pressure data into the model and computed wall tension based on simulated pressure and flow fields, and the material property based on the strain-stress relationship. As examples, we applied this model to augment FLIP (Functional Luminal Imaging Probe) measurements in three clinical cases: a normal subject, achalasia, and eosinophilic esophagitis (EoE). Our findings suggest that the wall stiffness was greatest in the EoE case, followed by the achalasia case, and then the normal. This is supported by NIH Grant R01 DK56033 and R01 DK079902.

  9. 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.

  10. Genome-scale metabolic models as platforms for strain design and biological discovery.

    Science.gov (United States)

    Mienda, Bashir Sajo

    2017-07-01

    Genome-scale metabolic models (GEMs) have been developed and used in guiding systems' metabolic engineering strategies for strain design and development. This strategy has been used in fermentative production of bio-based industrial chemicals and fuels from alternative carbon sources. However, computer-aided hypotheses building using established algorithms and software platforms for biological discovery can be integrated into the pipeline for strain design strategy to create superior strains of microorganisms for targeted biosynthetic goals. Here, I described an integrated workflow strategy using GEMs for strain design and biological discovery. Specific case studies of strain design and biological discovery using Escherichia coli genome-scale model are presented and discussed. The integrated workflow presented herein, when applied carefully would help guide future design strategies for high-performance microbial strains that have existing and forthcoming genome-scale metabolic models.

  11. Genome Scale Modeling in Systems Biology: Algorithms and Resources

    Science.gov (United States)

    Najafi, Ali; Bidkhori, Gholamreza; Bozorgmehr, Joseph H.; Koch, Ina; Masoudi-Nejad, Ali

    2014-01-01

    In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics. PMID:24822031

  12. Genome-scale modeling of yeast: chronology, applications and critical perspectives.

    Science.gov (United States)

    Lopes, Helder; Rocha, Isabel

    2017-08-01

    Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed. © FEMS 2017.

  13. Metingear: a development environment for annotating genome-scale metabolic models.

    Science.gov (United States)

    May, John W; James, A Gordon; Steinbeck, Christoph

    2013-09-01

    Genome-scale metabolic models often lack annotations that would allow them to be used for further analysis. Previous efforts have focused on associating metabolites in the model with a cross reference, but this can be problematic if the reference is not freely available, multiple resources are used or the metabolite is added from a literature review. Associating each metabolite with chemical structure provides unambiguous identification of the components and a more detailed view of the metabolism. We have developed an open-source desktop application that simplifies the process of adding database cross references and chemical structures to genome-scale metabolic models. Annotated models can be exported to the Systems Biology Markup Language open interchange format. Source code, binaries, documentation and tutorials are freely available at http://johnmay.github.com/metingear. The application is implemented in Java with bundles available for MS Windows and Macintosh OS X.

  14. In silico analysis of human metabolism: Reconstruction, contextualization and application of genome-scale models

    DEFF Research Database (Denmark)

    Geng, Jun; Nielsen, Jens

    2017-01-01

    The arising prevalence of metabolic diseases calls for a holistic approach for analysis of the underlying nature of abnormalities in cellular functions. Through mathematic representation and topological analysis of cellular metabolism, GEnome scale metabolic Models (GEMs) provide a promising fram...... that correctly describe interactions between cells or tissues, and we therefore discuss how GEMs can be integrated with blood circulation models. Finally, we end the review with proposing some possible future research directions....

  15. Genome scale models of yeast: towards standardized evaluation and consistent omic integration

    DEFF Research Database (Denmark)

    Sanchez, Benjamin J.; Nielsen, Jens

    2015-01-01

    Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published and are curre......Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published...... in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted....

  16. Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism

    Science.gov (United States)

    2016-03-15

    RESEARCH ARTICLE Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism Francisco G...jaques.reifman.civ@mail.mil Abstract A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm -based infections that are difficult to...eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic

  17. Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052

    Directory of Open Access Journals (Sweden)

    Kim Pan-Jun

    2011-08-01

    Full Text Available Abstract Background Solventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis. Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications. Results We present the first genome-scale metabolic model (iCM925 for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test

  18. Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling

    DEFF Research Database (Denmark)

    Ghaffari, Pouyan; Mardinoglu, Adil; Asplund, Anna

    2015-01-01

    Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell lines...... based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted...... for inhibition of cell growth may provide leads for the development of efficient cancer treatment strategies....

  19. Genome-scale modeling of the protein secretory machinery in yeast

    DEFF Research Database (Denmark)

    Feizi, Amir; Österlund, Tobias; Petranovic, Dina

    2013-01-01

    The protein secretory machinery in Eukarya is involved in post-translational modification (PTMs) and sorting of the secretory and many transmembrane proteins. While the secretory machinery has been well-studied using classic reductionist approaches, a holistic view of its complex nature is lacking....... Here, we present the first genome-scale model for the yeast secretory machinery which captures the knowledge generated through more than 50 years of research. The model is based on the concept of a Protein Specific Information Matrix (PSIM: characterized by seven PTMs features). An algorithm...

  20. Predicting growth of the healthy infant using a genome scale metabolic model.

    Science.gov (United States)

    Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

    2017-01-01

    An estimated 165 million children globally have stunted growth, and extensive growth data are available. Genome scale metabolic models allow the simulation of molecular flux over each metabolic enzyme, and are well adapted to analyze biological systems. We used a human genome scale metabolic model to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body composition. The model corroborates the finding that essential amino and fatty acids do not limit growth, but that energy is the main growth limiting factor. Disruptions to the supply and demand of energy markedly affected the predicted growth, indicating that elevated energy expenditure may be detrimental. The model was used to simulate the metabolic effect of mineral deficiencies, and showed the greatest growth reduction for deficiencies in copper, iron, and magnesium ions which affect energy production through oxidative phosphorylation. The model and simulation method were integrated to a platform and shared with the research community. The growth model constitutes another step towards the complete representation of human metabolism, and may further help improve the understanding of the mechanisms underlying stunting.

  1. Genome-scale model guided design of Propionibacterium for enhanced propionic acid production

    Directory of Open Access Journals (Sweden)

    Laura Navone

    2018-06-01

    Full Text Available Production of propionic acid by fermentation of propionibacteria has gained increasing attention in the past few years. However, biomanufacturing of propionic acid cannot compete with the current oxo-petrochemical synthesis process due to its well-established infrastructure, low oil prices and the high downstream purification costs of microbial production. Strain improvement to increase propionic acid yield is the best alternative to reduce downstream purification costs. The recent generation of genome-scale models for a number of Propionibacterium species facilitates the rational design of metabolic engineering strategies and provides a new opportunity to explore the metabolic potential of the Wood-Werkman cycle. Previous strategies for strain improvement have individually targeted acid tolerance, rate of propionate production or minimisation of by-products. Here we used the P. freudenreichii subsp. shermanii and the pan-Propionibacterium genome-scale metabolic models (GEMs to simultaneously target these combined issues. This was achieved by focussing on strategies which yield higher energies and directly suppress acetate formation. Using P. freudenreichii subsp. shermanii, two strategies were assessed. The first tested the ability to manipulate the redox balance to favour propionate production by over-expressing the first two enzymes of the pentose-phosphate pathway (PPP, Zwf (glucose-6-phosphate 1-dehydrogenase and Pgl (6-phosphogluconolactonase. Results showed a 4-fold increase in propionate to acetate ratio during the exponential growth phase. Secondly, the ability to enhance the energy yield from propionate production by over-expressing an ATP-dependent phosphoenolpyruvate carboxykinase (PEPCK and sodium-pumping methylmalonyl-CoA decarboxylase (MMD was tested, which extended the exponential growth phase. Together, these strategies demonstrate that in silico design strategies are predictive and can be used to reduce by-product formation in

  2. Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins.

    Science.gov (United States)

    Irani, Zahra Azimzadeh; Kerkhoven, Eduard J; Shojaosadati, Seyed Abbas; Nielsen, Jens

    2016-05-01

    Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering, a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better prediction of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required for using these models to understand and optimize protein production processes. © 2015 Wiley Periodicals, Inc.

  3. Investigating host-pathogen behavior and their interaction using genome-scale metabolic network models.

    Science.gov (United States)

    Sadhukhan, Priyanka P; Raghunathan, Anu

    2014-01-01

    Genome Scale Metabolic Modeling methods represent one way to compute whole cell function starting from the genome sequence of an organism and contribute towards understanding and predicting the genotype-phenotype relationship. About 80 models spanning all the kingdoms of life from archaea to eukaryotes have been built till date and used to interrogate cell phenotype under varying conditions. These models have been used to not only understand the flux distribution in evolutionary conserved pathways like glycolysis and the Krebs cycle but also in applications ranging from value added product formation in Escherichia coli to predicting inborn errors of Homo sapiens metabolism. This chapter describes a protocol that delineates the process of genome scale metabolic modeling for analysing host-pathogen behavior and interaction using flux balance analysis (FBA). The steps discussed in the process include (1) reconstruction of a metabolic network from the genome sequence, (2) its representation in a precise mathematical framework, (3) its translation to a model, and (4) the analysis using linear algebra and optimization. The methods for biological interpretations of computed cell phenotypes in the context of individual host and pathogen models and their integration are also discussed.

  4. 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

  5. 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

  6. Genome-scale metabolic modeling of Mucor circinelloides and comparative analysis with other oleaginous species.

    Science.gov (United States)

    Vongsangnak, Wanwipa; Klanchui, Amornpan; Tawornsamretkit, Iyarest; Tatiyaborwornchai, Witthawin; Laoteng, Kobkul; Meechai, Asawin

    2016-06-01

    We present a novel genome-scale metabolic model iWV1213 of Mucor circinelloides, which is an oleaginous fungus for industrial applications. The model contains 1213 genes, 1413 metabolites and 1326 metabolic reactions across different compartments. We demonstrate that iWV1213 is able to accurately predict the growth rates of M. circinelloides on various nutrient sources and culture conditions using Flux Balance Analysis and Phenotypic Phase Plane analysis. Comparative analysis of three oleaginous genome-scale models, including M. circinelloides (iWV1213), Mortierella alpina (iCY1106) and Yarrowia lipolytica (iYL619_PCP) revealed that iWV1213 possesses a higher number of genes involved in carbohydrate, amino acid, and lipid metabolisms that might contribute to its versatility in nutrient utilization. Moreover, the identification of unique and common active reactions among the Zygomycetes oleaginous models using Flux Variability Analysis unveiled a set of gene/enzyme candidates as metabolic engineering targets for cellular improvement. Thus, iWV1213 offers a powerful metabolic engineering tool for multi-level omics analysis, enabling strain optimization as a cell factory platform of lipid-based production. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Reconstruction of genome-scale human metabolic models using omics data

    DEFF Research Database (Denmark)

    Ryu, Jae Yong; Kim, Hyun Uk; Lee, Sang Yup

    2015-01-01

    used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods......, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic...... refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model....

  8. Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis

    Directory of Open Access Journals (Sweden)

    Balagurunathan Balaji

    2012-02-01

    Full Text Available Abstract Background Fermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes. Results We present a genome-scale metabolic model for Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation. Conclusion The genome-scale

  9. Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

    DEFF Research Database (Denmark)

    Vongsangnak, Wanwipa; Olsen, Peter; Hansen, Kim

    2008-01-01

    Background: Since ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number...... to a genome scale metabolic model of A. oryzae. Results: Our assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted...... model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources. Conclusion: A much enhanced annotation of the A. oryzae genome was performed and a genomescale metabolic model of A. oryzae was reconstructed. The model accurately predicted...

  10. Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

    Energy Technology Data Exchange (ETDEWEB)

    Seaver, Samuel M. D.; Bradbury, Louis M. T.; Frelin, Océane; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

    2015-03-10

    There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

  11. A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering.

    Science.gov (United States)

    Klamt, Steffen; Müller, Stefan; Regensburger, Georg; Zanghellini, Jürgen

    2018-02-07

    The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing. We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our findings by a small

  12. Network thermodynamic curation of human and yeast genome-scale metabolic models.

    Science.gov (United States)

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

    2014-07-15

    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. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  13. Genome-scale model guided design of Propionibacterium for enhanced propionic acid production.

    Science.gov (United States)

    Navone, Laura; McCubbin, Tim; Gonzalez-Garcia, Ricardo A; Nielsen, Lars K; Marcellin, Esteban

    2018-06-01

    Production of propionic acid by fermentation of propionibacteria has gained increasing attention in the past few years. However, biomanufacturing of propionic acid cannot compete with the current oxo-petrochemical synthesis process due to its well-established infrastructure, low oil prices and the high downstream purification costs of microbial production. Strain improvement to increase propionic acid yield is the best alternative to reduce downstream purification costs. The recent generation of genome-scale models for a number of Propionibacterium species facilitates the rational design of metabolic engineering strategies and provides a new opportunity to explore the metabolic potential of the Wood-Werkman cycle. Previous strategies for strain improvement have individually targeted acid tolerance, rate of propionate production or minimisation of by-products. Here we used the P. freudenreichii subsp . shermanii and the pan- Propionibacterium genome-scale metabolic models (GEMs) to simultaneously target these combined issues. This was achieved by focussing on strategies which yield higher energies and directly suppress acetate formation. Using P. freudenreichii subsp . shermanii , two strategies were assessed. The first tested the ability to manipulate the redox balance to favour propionate production by over-expressing the first two enzymes of the pentose-phosphate pathway (PPP), Zwf (glucose-6-phosphate 1-dehydrogenase) and Pgl (6-phosphogluconolactonase). Results showed a 4-fold increase in propionate to acetate ratio during the exponential growth phase. Secondly, the ability to enhance the energy yield from propionate production by over-expressing an ATP-dependent phosphoenolpyruvate carboxykinase (PEPCK) and sodium-pumping methylmalonyl-CoA decarboxylase (MMD) was tested, which extended the exponential growth phase. Together, these strategies demonstrate that in silico design strategies are predictive and can be used to reduce by-product formation in

  14. Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

    Science.gov (United States)

    Fang, Yilin; Yabusaki, Steven B.; Lipton, Mary S.; Long, Philip E.

    2012-01-01

    Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment. PMID:23042184

  15. Constraint-based modeling and kinetic analysis of the Smad dependent TGF-beta signaling pathway.

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    Zhike Zi

    Full Text Available BACKGROUND: Investigation of dynamics and regulation of the TGF-beta signaling pathway is central to the understanding of complex cellular processes such as growth, apoptosis, and differentiation. In this study, we aim at using systems biology approach to provide dynamic analysis on this pathway. METHODOLOGY/PRINCIPAL FINDINGS: We proposed a constraint-based modeling method to build a comprehensive mathematical model for the Smad dependent TGF-beta signaling pathway by fitting the experimental data and incorporating the qualitative constraints from the experimental analysis. The performance of the model generated by constraint-based modeling method is significantly improved compared to the model obtained by only fitting the quantitative data. The model agrees well with the experimental analysis of TGF-beta pathway, such as the time course of nuclear phosphorylated Smad, the subcellular location of Smad and signal response of Smad phosphorylation to different doses of TGF-beta. CONCLUSIONS/SIGNIFICANCE: The simulation results indicate that the signal response to TGF-beta is regulated by the balance between clathrin dependent endocytosis and non-clathrin mediated endocytosis. This model is useful to be built upon as new precise experimental data are emerging. The constraint-based modeling method can also be applied to quantitative modeling of other signaling pathways.

  16. Analysis of growth of Lactobacillus plantarum WCFS1 on a complex medium using a genome-scale metabolic model

    NARCIS (Netherlands)

    Teusink, B.; Wiersma, A.; Molenaar, D.; Francke, C.; Vos, de W.M.; Siezen, R.J.; Smid, E.J.

    2006-01-01

    A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for

  17. Systems biology of bacterial nitrogen fixation: High-throughput technology and its integrative description with constraint-based modeling

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    Resendis-Antonio Osbaldo

    2011-07-01

    Full Text Available Abstract Background Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. This undertaking is not trivial, and the development of computational methods useful in accomplishing an integrative, descriptive and predictive framework is a crucial issue to decoding the principles that regulated the metabolic activity of this biological process. Results In this work we present a systems biology description of the metabolic activity in bacterial nitrogen fixation. This was accomplished by an integrative analysis involving high-throughput data and constraint-based modeling to characterize the metabolic activity in Rhizobium etli bacteroids located at the root nodules of Phaseolus vulgaris (bean plant. Proteome and transcriptome technologies led us to identify 415 proteins and 689 up-regulated genes that orchestrate this biological process. Taking into account these data, we: 1 extended the metabolic reconstruction reported for R. etli; 2 simulated the metabolic activity during symbiotic nitrogen fixation; and 3 evaluated the in silico results in terms of bacteria phenotype. Notably, constraint-based modeling simulated nitrogen fixation activity in such a way that 76.83% of the enzymes and 69.48% of the genes were experimentally justified. Finally, to further assess the predictive scope of the computational model, gene deletion analysis was carried out on nine metabolic enzymes. Our model concluded that an altered metabolic activity on these enzymes induced

  18. Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data.

    Science.gov (United States)

    Gu, Deqing; Jian, Xingxing; Zhang, Cheng; Hua, Qiang

    2017-01-01

    Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named logical transformation of model (LTM) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.

  19. Expanding a dynamic flux balance model of yeast fermentation to genome-scale

    Science.gov (United States)

    2011-01-01

    Background Yeast is considered to be a workhorse of the biotechnology industry for the production of many value-added chemicals, alcoholic beverages and biofuels. Optimization of the fermentation is a challenging task that greatly benefits from dynamic models able to accurately describe and predict the fermentation profile and resulting products under different genetic and environmental conditions. In this article, we developed and validated a genome-scale dynamic flux balance model, using experimentally determined kinetic constraints. Results Appropriate equations for maintenance, biomass composition, anaerobic metabolism and nutrient uptake are key to improve model performance, especially for predicting glycerol and ethanol synthesis. Prediction profiles of synthesis and consumption of the main metabolites involved in alcoholic fermentation closely agreed with experimental data obtained from numerous lab and industrial fermentations under different environmental conditions. Finally, fermentation simulations of genetically engineered yeasts closely reproduced previously reported experimental results regarding final concentrations of the main fermentation products such as ethanol and glycerol. Conclusion A useful tool to describe, understand and predict metabolite production in batch yeast cultures was developed. The resulting model, if used wisely, could help to search for new metabolic engineering strategies to manage ethanol content in batch fermentations. PMID:21595919

  20. Genome-scale modelling of microbial metabolism with temporal and spatial resolution.

    Science.gov (United States)

    Henson, Michael A

    2015-12-01

    Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated. © 2015 Authors; published by Portland Press Limited.

  1. Novel insights into obesity and diabetes through genome-scale metabolic modeling

    Directory of Open Access Journals (Sweden)

    Leif eVäremo

    2013-04-01

    Full Text Available The growing prevalence of metabolic diseases, such as obesity and diabetes, are putting a high strain on global healthcare systems as well as increasing the demand for efficient treatment strategies. More than 360 million people worldwide are suffering from type 2 diabetes and, with the current trends, the projection is that 10% of the global adult population will be affected by 2030. In light of the systemic properties of metabolic diseases as well as the interconnected nature of metabolism, it is necessary to begin taking a holistic approach to study these diseases. Human genome-scale metabolic models (GEMs are topological and mathematical representations of cell metabolism and have proven to be valuable tools in the area of systems biology. Successful applications of GEMs include the process of gaining further biological and mechanistic understanding of diseases, finding potential biomarkers and identifying new drug targets. This review will focus on the modeling of human metabolism in the field of obesity and diabetes, showing its vast range of applications of clinical importance as well as point out future challenges.

  2. Genome-scale modeling enables metabolic engineering of Saccharomyces cerevisiae for succinic acid production.

    Science.gov (United States)

    Agren, Rasmus; Otero, José Manuel; Nielsen, Jens

    2013-07-01

    In this work, we describe the application of a genome-scale metabolic model and flux balance analysis for the prediction of succinic acid overproduction strategies in Saccharomyces cerevisiae. The top three single gene deletion strategies, Δmdh1, Δoac1, and Δdic1, were tested using knock-out strains cultivated anaerobically on glucose, coupled with physiological and DNA microarray characterization. While Δmdh1 and Δoac1 strains failed to produce succinate, Δdic1 produced 0.02 C-mol/C-mol glucose, in close agreement with model predictions (0.03 C-mol/C-mol glucose). Transcriptional profiling suggests that succinate formation is coupled to mitochondrial redox balancing, and more specifically, reductive TCA cycle activity. While far from industrial titers, this proof-of-concept suggests that in silico predictions coupled with experimental validation can be used to identify novel and non-intuitive metabolic engineering strategies.

  3. Deriving metabolic engineering strategies from genome-scale modeling with flux ratio constraints.

    Science.gov (United States)

    Yen, Jiun Y; Nazem-Bokaee, Hadi; Freedman, Benjamin G; Athamneh, Ahmad I M; Senger, Ryan S

    2013-05-01

    Optimized production of bio-based fuels and chemicals from microbial cell factories is a central goal of systems metabolic engineering. To achieve this goal, a new computational method of using flux balance analysis with flux ratios (FBrAtio) was further developed in this research and applied to five case studies to evaluate and design metabolic engineering strategies. The approach was implemented using publicly available genome-scale metabolic flux models. Synthetic pathways were added to these models along with flux ratio constraints by FBrAtio to achieve increased (i) cellulose production from Arabidopsis thaliana; (ii) isobutanol production from Saccharomyces cerevisiae; (iii) acetone production from Synechocystis sp. PCC6803; (iv) H2 production from Escherichia coli MG1655; and (v) isopropanol, butanol, and ethanol (IBE) production from engineered Clostridium acetobutylicum. The FBrAtio approach was applied to each case to simulate a metabolic engineering strategy already implemented experimentally, and flux ratios were continually adjusted to find (i) the end-limit of increased production using the existing strategy, (ii) new potential strategies to increase production, and (iii) the impact of these metabolic engineering strategies on product yield and culture growth. The FBrAtio approach has the potential to design "fine-tuned" metabolic engineering strategies in silico that can be implemented directly with available genomic tools. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Genome-scale metabolic models applied to human health and disease.

    Science.gov (United States)

    Cook, Daniel J; Nielsen, Jens

    2017-11-01

    Advances in genome sequencing, high throughput measurement of gene and protein expression levels, data accessibility, and computational power have allowed genome-scale metabolic models (GEMs) to become a useful tool for understanding metabolic alterations associated with many different diseases. Despite the proven utility of GEMs, researchers confront multiple challenges in the use of GEMs, their application to human health and disease, and their construction and simulation in an organ-specific and disease-specific manner. Several approaches that researchers are taking to address these challenges include using proteomic and transcriptomic-informed methods to build GEMs for individual organs, diseases, and patients and using constraints on model behavior during simulation to match observed metabolic fluxes. We review the challenges facing researchers in the use of GEMs, review the approaches used to address these challenges, and describe advances that are on the horizon and could lead to a better understanding of human metabolism. WIREs Syst Biol Med 2017, 9:e1393. doi: 10.1002/wsbm.1393 For further resources related to this article, please visit the WIREs website. © 2017 Wiley Periodicals, Inc.

  5. Revisiting the chlorophyll biosynthesis pathway using genome scale metabolic model of Oryza sativa japonica

    Science.gov (United States)

    Chatterjee, Ankita; Kundu, Sudip

    2015-01-01

    Chlorophyll is one of the most important pigments present in green plants and rice is one of the major food crops consumed worldwide. We curated the existing genome scale metabolic model (GSM) of rice leaf by incorporating new compartment, reactions and transporters. We used this modified GSM to elucidate how the chlorophyll is synthesized in a leaf through a series of bio-chemical reactions spanned over different organelles using inorganic macronutrients and light energy. We predicted the essential reactions and the associated genes of chlorophyll synthesis and validated against the existing experimental evidences. Further, ammonia is known to be the preferred source of nitrogen in rice paddy fields. The ammonia entering into the plant is assimilated in the root and leaf. The focus of the present work is centered on rice leaf metabolism. We studied the relative importance of ammonia transporters through the chloroplast and the cytosol and their interlink with other intracellular transporters. Ammonia assimilation in the leaves takes place by the enzyme glutamine synthetase (GS) which is present in the cytosol (GS1) and chloroplast (GS2). Our results provided possible explanation why GS2 mutants show normal growth under minimum photorespiration and appear chlorotic when exposed to air. PMID:26443104

  6. Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica.

    Science.gov (United States)

    Mishra, Pranjul; Lee, Na-Rae; Lakshmanan, Meiyappan; Kim, Minsuk; Kim, Byung-Gee; Lee, Dong-Yup

    2018-03-19

    Recently, there have been several attempts to produce long-chain dicarboxylic acids (DCAs) in various microbial hosts. Of these, Yarrowia lipolytica has great potential due to its oleaginous characteristics and unique ability to utilize hydrophobic substrates. However, Y. lipolytica should be further engineered to make it more competitive: the current approaches are mostly intuitive and cumbersome, thus limiting its industrial application. In this study, we proposed model-guided metabolic engineering strategies for enhanced production of DCAs in Y. lipolytica. At the outset, we reconstructed genome-scale metabolic model (GSMM) of Y. lipolytica (iYLI647) by substantially expanding the previous models. Subsequently, the model was validated using three sets of published culture experiment data. It was finally exploited to identify genetic engineering targets for overexpression, knockout, and cofactor modification by applying several in silico strain design methods, which potentially give rise to high yield production of the industrially relevant long-chain DCAs, e.g., dodecanedioic acid (DDDA). The resultant targets include (1) malate dehydrogenase and malic enzyme genes and (2) glutamate dehydrogenase gene, in silico overexpression of which generated additional NADPH required for fatty acid synthesis, leading to the increased DDDA fluxes by 48% and 22% higher, respectively, compared to wild-type. We further investigated the effect of supplying branched-chain amino acids on the acetyl-CoA turn-over rate which is key metabolite for fatty acid synthesis, suggesting their significance for production of DDDA in Y. lipolytica. In silico model-based strain design strategies allowed us to identify several metabolic engineering targets for overproducing DCAs in lipid accumulating yeast, Y. lipolytica. Thus, the current study can provide a methodological framework that is applicable to other oleaginous yeasts for value-added biochemical production.

  7. Noise analysis of genome-scale protein synthesis using a discrete computational model of translation

    Energy Technology Data Exchange (ETDEWEB)

    Racle, Julien; Hatzimanikatis, Vassily, E-mail: vassily.hatzimanikatis@epfl.ch [Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne (Switzerland); Swiss Institute of Bioinformatics (SIB), CH-1015 Lausanne (Switzerland); Stefaniuk, Adam Jan [Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne (Switzerland)

    2015-07-28

    Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA polymerases along their corresponding templates (messenger RNA (mRNA) and DNA). The large size of these systems, which scales with the number of genes, mRNA copies, codons per mRNA, and ribosomes, is responsible for some of the challenges. Additionally, one should be able to describe the dynamics of ribosome exchange between the free ribosome pool and those bound to mRNAs, as well as how mRNA species compete for ribosomes. We developed an efficient algorithm for stochastic simulations that addresses these issues and used it to study the contribution and trade-offs of noise to translation properties (rates, time delays, and rate-limiting steps). The algorithm scales linearly with the number of mRNA copies, which allowed us to study the importance of genome-scale competition between mRNAs for the same ribosomes. We determined that noise is minimized under conditions maximizing the specific synthesis rate. Moreover, sensitivity analysis of the stochastic system revealed the importance of the elongation rate in the resultant noise, whereas the translation initiation rate constant was more closely related to the average protein synthesis rate. We observed significant differences between our results and the noise properties of the most commonly used translation models. Overall, our studies demonstrate that the use of full mechanistic models is essential for the study of noise in translation and transcription.

  8. Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico.

    Science.gov (United States)

    McAnulty, Michael J; Yen, Jiun Y; Freedman, Benjamin G; Senger, Ryan S

    2012-05-14

    Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico. A new method called "flux balance analysis with flux ratios (FBrAtio)" was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC490) that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i) acetate, (ii) lactate, (iii) butyrate, (iv) acetone, (v) butanol, (vi) ethanol, (vii) CO2 and (viii) H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production. FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.

  9. Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling

    Directory of Open Access Journals (Sweden)

    Mahadevan Radhakrishnan

    2010-05-01

    Full Text Available Abstract Background Geobacter sulfurreducens is a member of the Geobacter species, which are capable of oxidation of organic waste coupled to the reduction of heavy metals and electrode with applications in bioremediation and bioenergy generation. While the metabolism of this organism has been studied through the development of a stoichiometry based genome-scale metabolic model, the associated regulatory network has not yet been well studied. In this manuscript, we report on the implementation of a thermodynamics based metabolic flux model for Geobacter sulfurreducens. We use this updated model to identify reactions that are subject to regulatory control in the metabolic network of G. sulfurreducens using thermodynamic variability analysis. Findings As a first step, we have validated the regulatory sites and bottleneck reactions predicted by the thermodynamic flux analysis in E. coli by evaluating the expression ranges of the corresponding genes. We then identified ten reactions in the metabolic network of G. sulfurreducens that are predicted to be candidates for regulation. We then compared the free energy ranges for these reactions with the corresponding gene expression fold changes under conditions of different environmental and genetic perturbations and show that the model predictions of regulation are consistent with data. In addition, we also identify reactions that operate close to equilibrium and show that the experimentally determined exchange coefficient (a measure of reversibility is significant for these reactions. Conclusions Application of the thermodynamic constraints resulted in identification of potential bottleneck reactions not only from the central metabolism but also from the nucleotide and amino acid subsystems, thereby showing the highly coupled nature of the thermodynamic constraints. In addition, thermodynamic variability analysis serves as a valuable tool in estimating the ranges of ΔrG' of every reaction in the model

  10. Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows.

    Science.gov (United States)

    Bordel, Sergio

    2018-04-13

    In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have specific metabolic features for a long time and currently there is a growing interest in characterizing new cancer specific metabolic hallmarks. In this article it is presented a method to find personalized therapeutic windows using RNA-seq data and Genome Scale Metabolic Models. This method is implemented in the python library, pyTARG. Our predictions showed that the most anticancer selective (affecting 27 out of 34 considered cancer cell lines and only 1 out of 6 healthy mesenchymal stem cell lines) single metabolic reactions are those involved in cholesterol biosynthesis. Excluding cholesterol biosynthesis, all the considered cell lines can be selectively affected by targeting different combinations (from 1 to 5 reactions) of only 18 metabolic reactions, which suggests that a small subset of drugs or siRNAs combined in patient specific manners could be at the core of metabolism based personalized treatments.

  11. Capturing the response of Clostridium acetobutylicum to chemical stressors using a regulated genome-scale metabolic model

    International Nuclear Information System (INIS)

    Dash, Satyakam; Mueller, Thomas J.; Venkataramanan, Keerthi P.; Papoutsakis, Eleftherios T.; Maranas, Costas D.

    2014-01-01

    Clostridia are anaerobic Gram-positive Firmicutes containing broad and flexible systems for substrate utilization, which have been used successfully to produce a range of industrial compounds. Clostridium acetobutylicum has been used to produce butanol on an industrial scale through acetone-butanol-ethanol (ABE) fermentation. A genome-scale metabolic (GSM) model is a powerful tool for understanding the metabolic capacities of an organism and developing metabolic engineering strategies for strain development. The integration of stress related specific transcriptomics information with the GSM model provides opportunities for elucidating the focal points of regulation

  12. Respiration climacteric in tomato fruits elucidated by constraint-based modelling.

    Science.gov (United States)

    Colombié, Sophie; Beauvoit, Bertrand; Nazaret, Christine; Bénard, Camille; Vercambre, Gilles; Le Gall, Sophie; Biais, Benoit; Cabasson, Cécile; Maucourt, Mickaël; Bernillon, Stéphane; Moing, Annick; Dieuaide-Noubhani, Martine; Mazat, Jean-Pierre; Gibon, Yves

    2017-03-01

    Tomato is a model organism to study the development of fleshy fruit including ripening initiation. Unfortunately, few studies deal with the brief phase of accelerated ripening associated with the respiration climacteric because of practical problems involved in measuring fruit respiration. Because constraint-based modelling allows predicting accurate metabolic fluxes, we investigated the respiration and energy dissipation of fruit pericarp at the breaker stage using a detailed stoichiometric model of the respiratory pathway, including alternative oxidase and uncoupling proteins. Assuming steady-state, a metabolic dataset was transformed into constraints to solve the model on a daily basis throughout tomato fruit development. We detected a peak of CO 2 released and an excess of energy dissipated at 40 d post anthesis (DPA) just before the onset of ripening coinciding with the respiration climacteric. We demonstrated the unbalanced carbon allocation with the sharp slowdown of accumulation (for syntheses and storage) and the beginning of the degradation of starch and cell wall polysaccharides. Experiments with fruits harvested from plants cultivated under stress conditions confirmed the concept. We conclude that modelling with an accurate metabolic dataset is an efficient tool to bypass the difficulty of measuring fruit respiration and to elucidate the underlying mechanisms of ripening. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

  13. The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

    Science.gov (United States)

    Agren, Rasmus; Liu, Liming; Shoaie, Saeed; Vongsangnak, Wanwipa; Nookaew, Intawat; Nielsen, Jens

    2013-01-01

    We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production. PMID:23555215

  14. Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction.

    Directory of Open Access Journals (Sweden)

    Katsunori Yoshikawa

    Full Text Available Arthrospira (Spirulina platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(PH dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.

  15. Constraint-based Student Modelling in Probability Story Problems with Scaffolding Techniques

    Directory of Open Access Journals (Sweden)

    Nabila Khodeir

    2018-01-01

    Full Text Available Constraint-based student modelling (CBM is an important technique employed in intelligent tutoring systems to model student knowledge to provide relevant assistance. This paper introduces the Math Story Problem Tutor (MAST, a Web-based intelligent tutoring system for probability story problems, which is able to generate problems of different contexts, types and difficulty levels for self-paced learning. Constraints in MAST are specified at a low-level of granularity to allow fine-grained diagnosis of the student error. Furthermore, MAST extends CBM to address errors due to misunderstanding of the narrative story. It can locate and highlight keywords that may have been overlooked or misunderstood leading to an error. This is achieved by utilizing the role of sentences and keywords that are defined through the Natural Language Generation (NLG methods deployed in the story problem generation. MAST also integrates CBM with scaffolding questions and feedback to provide various forms of help and guidance to the student. This allows the student to discover and correct any errors in his/her solution. MAST has been preliminary evaluated empirically and the results show the potential effectiveness in tutoring students with a decrease in the percentage of violated constraints along the learning curve. Additionally, there is a significant improvement in the results of the post–test exam in comparison to the pre-test exam of the students using MAST in comparison to those relying on the textbook

  16. Comprehensive reconstruction and in silico analysis of Aspergillus niger genome-scale metabolic network model that accounts for 1210 ORFs.

    Science.gov (United States)

    Lu, Hongzhong; Cao, Weiqiang; Ouyang, Liming; Xia, Jianye; Huang, Mingzhi; Chu, Ju; Zhuang, Yingping; Zhang, Siliang; Noorman, Henk

    2017-03-01

    Aspergillus niger is one of the most important cell factories for industrial enzymes and organic acids production. A comprehensive genome-scale metabolic network model (GSMM) with high quality is crucial for efficient strain improvement and process optimization. The lack of accurate reaction equations and gene-protein-reaction associations (GPRs) in the current best model of A. niger named GSMM iMA871, however, limits its application scope. To overcome these limitations, we updated the A. niger GSMM by combining the latest genome annotation and literature mining technology. Compared with iMA871, the number of reactions in iHL1210 was increased from 1,380 to 1,764, and the number of unique ORFs from 871 to 1,210. With the aid of our transcriptomics analysis, the existence of 63% ORFs and 68% reactions in iHL1210 can be verified when glucose was used as the only carbon source. Physiological data from chemostat cultivations, 13 C-labeled and molecular experiments from the published literature were further used to check the performance of iHL1210. The average correlation coefficients between the predicted fluxes and estimated fluxes from 13 C-labeling data were sufficiently high (above 0.89) and the prediction of cell growth on most of the reported carbon and nitrogen sources was consistent. Using the updated genome-scale model, we evaluated gene essentiality on synthetic and yeast extract medium, as well as the effects of NADPH supply on glucoamylase production in A. niger. In summary, the new A. niger GSMM iHL1210 contains significant improvements with respect to the metabolic coverage and prediction performance, which paves the way for systematic metabolic engineering of A. niger. Biotechnol. Bioeng. 2017;114: 685-695. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  17. IMGMD: A platform for the integration and standardisation of In silico Microbial Genome-scale Metabolic Models.

    Science.gov (United States)

    Ye, Chao; Xu, Nan; Dong, Chuan; Ye, Yuannong; Zou, Xuan; Chen, Xiulai; Guo, Fengbiao; Liu, Liming

    2017-04-07

    Genome-scale metabolic models (GSMMs) constitute a platform that combines genome sequences and detailed biochemical information to quantify microbial physiology at the system level. To improve the unity, integrity, correctness, and format of data in published GSMMs, a consensus IMGMD database was built in the LAMP (Linux + Apache + MySQL + PHP) system by integrating and standardizing 328 GSMMs constructed for 139 microorganisms. The IMGMD database can help microbial researchers download manually curated GSMMs, rapidly reconstruct standard GSMMs, design pathways, and identify metabolic targets for strategies on strain improvement. Moreover, the IMGMD database facilitates the integration of wet-lab and in silico data to gain an additional insight into microbial physiology. The IMGMD database is freely available, without any registration requirements, at http://imgmd.jiangnan.edu.cn/database.

  18. Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

    Science.gov (United States)

    Broddrick, Jared T.; Rubin, Benjamin E.; Welkie, David G.; Du, Niu; Mih, Nathan; Diamond, Spencer; Lee, Jenny J.; Golden, Susan S.; Palsson, Bernhard O.

    2016-01-01

    The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology. PMID:27911809

  19. Systematic construction of kinetic models from genome-scale metabolic networks.

    Directory of Open Access Journals (Sweden)

    Natalie J Stanford

    Full Text Available The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

  20. Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

    Science.gov (United States)

    Smallbone, Kieran; Klipp, Edda; Mendes, Pedro; Liebermeister, Wolfram

    2013-01-01

    The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. PMID:24324546

  1. In silico method for modelling metabolism and gene product expression at genome scale

    Energy Technology Data Exchange (ETDEWEB)

    Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem; Portnoy, Vasiliy A.; Lewis, Nathan E.; Orth, Jeffrey D.; Rutledge, Alexandra C.; Smith, Richard D.; Adkins, Joshua N.; Zengler, Karsten; Palsson, Bernard O.

    2012-07-03

    Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.

  2. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

    DEFF Research Database (Denmark)

    Sanchez, Benjamin J.; Zhang, Xi-Cheng; Nilsson, Avlant

    2017-01-01

    , which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance...... and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping...... with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between...

  3. 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

  4. 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.

  5. The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network

    DEFF Research Database (Denmark)

    Liu, Guodong; Marras, Antonio; Nielsen, Jens

    2014-01-01

    regulatory information is necessary to improve the accuracy and predictive ability of metabolic models. Here we review the strategies for the reconstruction of a transcriptional regulatory network (TRN) for yeast and the integration of such a reconstruction into a flux balance analysis-based metabolic model......Metabolism is regulated at multiple levels in response to the changes of internal or external conditions. Transcriptional regulation plays an important role in regulating many metabolic reactions by altering the concentrations of metabolic enzymes. Thus, integration of the transcriptional....... While many large-scale TRN reconstructions have been reported for yeast, these reconstructions still need to be improved regarding the functionality and dynamic property of the regulatory interactions. In addition, mathematical modeling approaches need to be further developed to efficiently integrate...

  6. Genome-Scale Analysis of Translation Elongation with a Ribosome Flow Model

    Science.gov (United States)

    Meilijson, Isaac; Kupiec, Martin; Ruppin, Eytan

    2011-01-01

    We describe the first large scale analysis of gene translation that is based on a model that takes into account the physical and dynamical nature of this process. The Ribosomal Flow Model (RFM) predicts fundamental features of the translation process, including translation rates, protein abundance levels, ribosomal densities and the relation between all these variables, better than alternative (‘non-physical’) approaches. In addition, we show that the RFM can be used for accurate inference of various other quantities including genes' initiation rates and translation costs. These quantities could not be inferred by previous predictors. We find that increasing the number of available ribosomes (or equivalently the initiation rate) increases the genomic translation rate and the mean ribosome density only up to a certain point, beyond which both saturate. Strikingly, assuming that the translation system is tuned to work at the pre-saturation point maximizes the predictive power of the model with respect to experimental data. This result suggests that in all organisms that were analyzed (from bacteria to Human), the global initiation rate is optimized to attain the pre-saturation point. The fact that similar results were not observed for heterologous genes indicates that this feature is under selection. Remarkably, the gap between the performance of the RFM and alternative predictors is strikingly large in the case of heterologous genes, testifying to the model's promising biotechnological value in predicting the abundance of heterologous proteins before expressing them in the desired host. PMID:21909250

  7. Predicting growth of the healthy infant using a genome scale metabolic model

    DEFF Research Database (Denmark)

    Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

    2017-01-01

    to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body...

  8. Modeling Method for Increased Precision and Scope of Directly Measurable Fluxes at a Genome-Scale

    DEFF Research Database (Denmark)

    McCloskey, Douglas; Young, Jamey D.; Xu, Sibei

    2016-01-01

    pathways of traditional MFA models and also covers the additional pathways of purine, pyrimidine, isoprenoid, methionine, riboflavin, coenzyme A, and folate, as well as other biosynthetic pathways. When evaluating the iDM2014 using a set of measured intracellular intermediate and cofactor mass isotopomer...

  9. Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale

    DEFF Research Database (Denmark)

    Liu, Joanne K.; O’Brien, Edward J.; Lerman, Joshua A.

    2014-01-01

    Background: Membranes play a crucial role in cellular functions. Membranes provide a physical barrier, control the trafficking of substances entering and leaving the cell, and are a major determinant of cellular ultra-structure. In addition, components embedded within the membrane participate...... the computation of cellular phenotypes through an integrated computation of proteome composition, abundance, and activity in four cellular compartments (cytoplasm, periplasm, inner and outer membrane). Reconstruction and validation of the model has demonstrated that the iJL1678-ME is capable of capturing...

  10. Improved Discovery of Molecular Interactions in Genome-Scale Data with Adaptive Model-Based Normalization

    Science.gov (United States)

    Brown, Patrick O.

    2013-01-01

    Background High throughput molecular-interaction studies using immunoprecipitations (IP) or affinity purifications are powerful and widely used in biology research. One of many important applications of this method is to identify the set of RNAs that interact with a particular RNA-binding protein (RBP). Here, the unique statistical challenge presented is to delineate a specific set of RNAs that are enriched in one sample relative to another, typically a specific IP compared to a non-specific control to model background. The choice of normalization procedure critically impacts the number of RNAs that will be identified as interacting with an RBP at a given significance threshold – yet existing normalization methods make assumptions that are often fundamentally inaccurate when applied to IP enrichment data. Methods In this paper, we present a new normalization methodology that is specifically designed for identifying enriched RNA or DNA sequences in an IP. The normalization (called adaptive or AD normalization) uses a basic model of the IP experiment and is not a variant of mean, quantile, or other methodology previously proposed. The approach is evaluated statistically and tested with simulated and empirical data. Results and Conclusions The adaptive (AD) normalization method results in a greatly increased range in the number of enriched RNAs identified, fewer false positives, and overall better concordance with independent biological evidence, for the RBPs we analyzed, compared to median normalization. The approach is also applicable to the study of pairwise RNA, DNA and protein interactions such as the analysis of transcription factors via chromatin immunoprecipitation (ChIP) or any other experiments where samples from two conditions, one of which contains an enriched subset of the other, are studied. PMID:23349766

  11. Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

    Directory of Open Access Journals (Sweden)

    Tyler W. H. Backman

    2018-01-01

    Full Text Available Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13 C Metabolic Flux Analysis ( 13 C MFA and Two-Scale 13 C Metabolic Flux Analysis (2S- 13 C MFA are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1 systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2 automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13 C MFA or 2S- 13 C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.

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

    Directory of Open Access Journals (Sweden)

    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.

  13. Constraint-based reachability

    Directory of Open Access Journals (Sweden)

    Arnaud Gotlieb

    2013-02-01

    Full Text Available Iterative imperative programs can be considered as infinite-state systems computing over possibly unbounded domains. Studying reachability in these systems is challenging as it requires to deal with an infinite number of states with standard backward or forward exploration strategies. An approach that we call Constraint-based reachability, is proposed to address reachability problems by exploring program states using a constraint model of the whole program. The keypoint of the approach is to interpret imperative constructions such as conditionals, loops, array and memory manipulations with the fundamental notion of constraint over a computational domain. By combining constraint filtering and abstraction techniques, Constraint-based reachability is able to solve reachability problems which are usually outside the scope of backward or forward exploration strategies. This paper proposes an interpretation of classical filtering consistencies used in Constraint Programming as abstract domain computations, and shows how this approach can be used to produce a constraint solver that efficiently generates solutions for reachability problems that are unsolvable by other approaches.

  14. 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

  15. A Novel Methodology to Estimate Metabolic Flux Distributions in Constraint-Based Models

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    Francesco Alessandro Massucci

    2013-09-01

    Full Text Available Quite generally, constraint-based metabolic flux analysis describes the space of viable flux configurations for a metabolic network as a high-dimensional polytope defined by the linear constraints that enforce the balancing of production and consumption fluxes for each chemical species in the system. In some cases, the complexity of the solution space can be reduced by performing an additional optimization, while in other cases, knowing the range of variability of fluxes over the polytope provides a sufficient characterization of the allowed configurations. There are cases, however, in which the thorough information encoded in the individual distributions of viable fluxes over the polytope is required. Obtaining such distributions is known to be a highly challenging computational task when the dimensionality of the polytope is sufficiently large, and the problem of developing cost-effective ad hoc algorithms has recently seen a major surge of interest. Here, we propose a method that allows us to perform the required computation heuristically in a time scaling linearly with the number of reactions in the network, overcoming some limitations of similar techniques employed in recent years. As a case study, we apply it to the analysis of the human red blood cell metabolic network, whose solution space can be sampled by different exact techniques, like Hit-and-Run Monte Carlo (scaling roughly like the third power of the system size. Remarkably accurate estimates for the true distributions of viable reaction fluxes are obtained, suggesting that, although further improvements are desirable, our method enhances our ability to analyze the space of allowed configurations for large biochemical reaction networks.

  16. Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth

    Science.gov (United States)

    2012-01-01

    Background Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known in vivo phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the in silico capability in predicting in vivo phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes. Results Here we employed a systematic and iterative process, designated as Reconciling In silico/in vivo mutaNt Growth (RING), to settle discrepancies between in silico prediction and in vivo observations to a newly reconstructed genome-scale metabolic model of the fission yeast, Schizosaccharomyces pombe, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of S. pombe. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype). Conclusion This study presents validation and refinement of a newly reconstructed metabolic model of the yeast S. pombe, through improving the metabolic model’s predictive capabilities by reconciling the in silico predicted growth phenotypes of single-gene knockout mutants, with experimental in vivo growth data. PMID:22631437

  17. Understanding the Representative Gut Microbiota Dysbiosis in Metformin-Treated Type 2 Diabetes Patients Using Genome-Scale Metabolic Modeling

    Directory of Open Access Journals (Sweden)

    Dorines Rosario

    2018-06-01

    Full Text Available Dysbiosis in the gut microbiome composition may be promoted by therapeutic drugs such as metformin, the world’s most prescribed antidiabetic drug. Under metformin treatment, disturbances of the intestinal microbes lead to increased abundance of Escherichia spp., Akkermansia muciniphila, Subdoligranulum variabile and decreased abundance of Intestinibacter bartlettii. This alteration may potentially lead to adverse effects on the host metabolism, with the depletion of butyrate producer genus. However, an increased production of butyrate and propionate was verified in metformin-treated Type 2 diabetes (T2D patients. The mechanisms underlying these nutritional alterations and their relation with gut microbiota dysbiosis remain unclear. Here, we used Genome-scale Metabolic Models of the representative gut bacteria Escherichia spp., I. bartlettii, A. muciniphila, and S. variabile to elucidate their bacterial metabolism and its effect on intestinal nutrient pool, including macronutrients (e.g., amino acids and short chain fatty acids, minerals and chemical elements (e.g., iron and oxygen. We applied flux balance analysis (FBA coupled with synthetic lethality analysis interactions to identify combinations of reactions and extracellular nutrients whose absence prevents growth. Our analyses suggest that Escherichia sp. is the bacteria least vulnerable to nutrient availability. We have also examined bacterial contribution to extracellular nutrients including short chain fatty acids, amino acids, and gasses. For instance, Escherichia sp. and S. variabile may contribute to the production of important short chain fatty acids (e.g., acetate and butyrate, respectively involved in the host physiology under aerobic and anaerobic conditions. We have also identified pathway susceptibility to nutrient availability and reaction changes among the four bacteria using both FBA and flux variability analysis. For instance, lipopolysaccharide synthesis, nucleotide sugar

  18. Constraint-Based Abstraction of a Model Checker for Infinite State Systems

    DEFF Research Database (Denmark)

    Banda, Gourinath; Gallagher, John Patrick

    Abstract interpretation-based model checking provides an approach to verifying properties of infinite-state systems. In practice, most previous work on abstract model checking is either restricted to verifying universal properties, or develops special techniques for temporal logics such as modal t...... to implementation of abstract model checking algorithms for abstract domains based on constraints, making use of an SMT solver....

  19. Design Principles as a Guide for Constraint Based and Dynamic Modeling: Towards an Integrative Workflow.

    Science.gov (United States)

    Sehr, Christiana; Kremling, Andreas; Marin-Sanguino, Alberto

    2015-10-16

    During the last 10 years, systems biology has matured from a fuzzy concept combining omics, mathematical modeling and computers into a scientific field on its own right. In spite of its incredible potential, the multilevel complexity of its objects of study makes it very difficult to establish a reliable connection between data and models. The great number of degrees of freedom often results in situations, where many different models can explain/fit all available datasets. This has resulted in a shift of paradigm from the initially dominant, maybe naive, idea of inferring the system out of a number of datasets to the application of different techniques that reduce the degrees of freedom before any data set is analyzed. There is a wide variety of techniques available, each of them can contribute a piece of the puzzle and include different kinds of experimental information. But the challenge that remains is their meaningful integration. Here we show some theoretical results that enable some of the main modeling approaches to be applied sequentially in a complementary manner, and how this workflow can benefit from evolutionary reasoning to keep the complexity of the problem in check. As a proof of concept, we show how the synergies between these modeling techniques can provide insight into some well studied problems: Ammonia assimilation in bacteria and an unbranched linear pathway with end-product inhibition.

  20. Design Principles as a Guide for Constraint Based and Dynamic Modeling: Towards an Integrative Workflow

    Directory of Open Access Journals (Sweden)

    Christiana Sehr

    2015-10-01

    Full Text Available During the last 10 years, systems biology has matured from a fuzzy concept combining omics, mathematical modeling and computers into a scientific field on its own right. In spite of its incredible potential, the multilevel complexity of its objects of study makes it very difficult to establish a reliable connection between data and models. The great number of degrees of freedom often results in situations, where many different models can explain/fit all available datasets. This has resulted in a shift of paradigm from the initially dominant, maybe naive, idea of inferring the system out of a number of datasets to the application of different techniques that reduce the degrees of freedom before any data set is analyzed. There is a wide variety of techniques available, each of them can contribute a piece of the puzzle and include different kinds of experimental information. But the challenge that remains is their meaningful integration. Here we show some theoretical results that enable some of the main modeling approaches to be applied sequentially in a complementary manner, and how this workflow can benefit from evolutionary reasoning to keep the complexity of the problem in check. As a proof of concept, we show how the synergies between these modeling techniques can provide insight into some well studied problems: Ammonia assimilation in bacteria and an unbranched linear pathway with end-product inhibition.

  1. Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism

    DEFF Research Database (Denmark)

    Machado, Daniel; Herrgard, Markus

    2014-01-01

    of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods...... is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity...... of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth...

  2. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

    Science.gov (United States)

    Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler; Levering, Jennifer; Zielinski, Daniel C; McConnell, Brian O; Long, Christopher P; Knoshaug, Eric P; Guarnieri, Michael T; Antoniewicz, Maciek R; Betenbaugh, Michael J; Zengler, Karsten

    2016-09-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. © 2016 American Society of Plant Biologists. All rights reserved.

  3. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions1

    Science.gov (United States)

    Zuñiga, Cristal; Li, Chien-Ting; Zielinski, Daniel C.; Guarnieri, Michael T.; Antoniewicz, Maciek R.; Zengler, Karsten

    2016-01-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. PMID:27372244

  4. Constraint-based model of Shewanella oneidensis MR-1 metabolism: a tool for data analysis and hypothesis generation.

    Directory of Open Access Journals (Sweden)

    Grigoriy E Pinchuk

    2010-06-01

    Full Text Available Shewanellae are gram-negative facultatively anaerobic metal-reducing bacteria commonly found in chemically (i.e., redox stratified environments. Occupying such niches requires the ability to rapidly acclimate to changes in electron donor/acceptor type and availability; hence, the ability to compete and thrive in such environments must ultimately be reflected in the organization and utilization of electron transfer networks, as well as central and peripheral carbon metabolism. To understand how Shewanella oneidensis MR-1 utilizes its resources, the metabolic network was reconstructed. The resulting network consists of 774 reactions, 783 genes, and 634 unique metabolites and contains biosynthesis pathways for all cell constituents. Using constraint-based modeling, we investigated aerobic growth of S. oneidensis MR-1 on numerous carbon sources. To achieve this, we (i used experimental data to formulate a biomass equation and estimate cellular ATP requirements, (ii developed an approach to identify cycles (such as futile cycles and circulations, (iii classified how reaction usage affects cellular growth, (iv predicted cellular biomass yields on different carbon sources and compared model predictions to experimental measurements, and (v used experimental results to refine metabolic fluxes for growth on lactate. The results revealed that aerobic lactate-grown cells of S. oneidensis MR-1 used less efficient enzymes to couple electron transport to proton motive force generation, and possibly operated at least one futile cycle involving malic enzymes. Several examples are provided whereby model predictions were validated by experimental data, in particular the role of serine hydroxymethyltransferase and glycine cleavage system in the metabolism of one-carbon units, and growth on different sources of carbon and energy. This work illustrates how integration of computational and experimental efforts facilitates the understanding of microbial metabolism at a

  5. Enumeration of smallest intervention strategies in genome-scale metabolic networks.

    Directory of Open Access Journals (Sweden)

    Axel von Kamp

    2014-01-01

    Full Text Available One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions in genome-scale metabolic network models. For this we combine two approaches, namely (i the mapping of MCSs to EMs in a dual network, and (ii a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth than reported previously. The strength of the presented approach is that smallest intervention strategies can be

  6. 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...... was extensively validated against published flux data, and flux distribution values were found to correlate well between simulations and experiments. The split pathway of the lysine synthesis pathway of C. glutamicum was investigated, and it was found that the direct dehydrogenase variant gave a higher lysine...... yield than the alternative succinyl pathway at high lysine production rates. The NADPH demand of the network was not found to be critical for lysine production until lysine yields exceeded 55% (mmol lysine (mmol glucose)(-1)). The model was validated during growth on the organic acids acetate...

  7. Reconstruction and in silico analysis of an Actinoplanes sp. SE50/110 genome-scale metabolic model for acarbose production

    Directory of Open Access Journals (Sweden)

    Yali eWang

    2015-06-01

    Full Text Available Actinoplanes sp. SE50/110 produces the -glucosidase inhibitor acarbose, which is used to treat type 2 diabetes mellitus. To obtain a comprehensive understanding of its cellular metabolism, a genome-scale metabolic model of strain SE50/110, iYLW1028, was reconstructed on the bases of the genome annotation, biochemical databases, and extensive literature mining. Model iYLW1028 comprises 1028 genes, 1128 metabolites and 1219 reactions. 122 and 81 genes were essential for cell growth on acarbose synthesis and sucrose media, respectively, and the acarbose biosynthetic pathway in SE50/110 was expounded completely. Based on model predictions, the addition of arginine and histidine to the media increased acarbose production by 78% and 59%, respectively. Additionally, dissolved oxygen has a great effect on acarbose production based on model predictions. Furthermore, genes to be overexpressed for the overproduction of acarbose were identified, and the deletion of treY eliminated the formation of by-product component C. Model iYLW1028 is a useful platform for optimizing and systems metabolic engineering for acarbose production in Actinoplanes sp. SE50/110.

  8. Modeling and Simulation of Optimal Resource Management during the Diurnal Cycle in Emiliania huxleyi by Genome-Scale Reconstruction and an Extended Flux Balance Analysis Approach.

    Science.gov (United States)

    Knies, David; Wittmüß, Philipp; Appel, Sebastian; Sawodny, Oliver; Ederer, Michael; Feuer, Ronny

    2015-10-28

    The coccolithophorid unicellular alga Emiliania huxleyi is known to form large blooms, which have a strong effect on the marine carbon cycle. As a photosynthetic organism, it is subjected to a circadian rhythm due to the changing light conditions throughout the day. For a better understanding of the metabolic processes under these periodically-changing environmental conditions, a genome-scale model based on a genome reconstruction of the E. huxleyi strain CCMP 1516 was created. It comprises 410 reactions and 363 metabolites. Biomass composition is variable based on the differentiation into functional biomass components and storage metabolites. The model is analyzed with a flux balance analysis approach called diurnal flux balance analysis (diuFBA) that was designed for organisms with a circadian rhythm. It allows storage metabolites to accumulate or be consumed over the diurnal cycle, while keeping the structure of a classical FBA problem. A feature of this approach is that the production and consumption of storage metabolites is not defined externally via the biomass composition, but the result of optimal resource management adapted to the diurnally-changing environmental conditions. The model in combination with this approach is able to simulate the variable biomass composition during the diurnal cycle in proximity to literature data.

  9. Modeling and Simulation of Optimal Resource Management during the Diurnal Cycle in Emiliania huxleyi by Genome-Scale Reconstruction and an Extended Flux Balance Analysis Approach

    Directory of Open Access Journals (Sweden)

    David Knies

    2015-10-01

    Full Text Available The coccolithophorid unicellular alga Emiliania huxleyi is known to form large blooms, which have a strong effect on the marine carbon cycle. As a photosynthetic organism, it is subjected to a circadian rhythm due to the changing light conditions throughout the day. For a better understanding of the metabolic processes under these periodically-changing environmental conditions, a genome-scale model based on a genome reconstruction of the E. huxleyi strain CCMP 1516 was created. It comprises 410 reactions and 363 metabolites. Biomass composition is variable based on the differentiation into functional biomass components and storage metabolites. The model is analyzed with a flux balance analysis approach called diurnal flux balance analysis (diuFBA that was designed for organisms with a circadian rhythm. It allows storage metabolites to accumulate or be consumed over the diurnal cycle, while keeping the structure of a classical FBA problem. A feature of this approach is that the production and consumption of storage metabolites is not defined externally via the biomass composition, but the result of optimal resource management adapted to the diurnally-changing environmental conditions. The model in combination with this approach is able to simulate the variable biomass composition during the diurnal cycle in proximity to literature data.

  10. Constraint-based scheduling

    Science.gov (United States)

    Zweben, Monte

    1993-01-01

    The GERRY scheduling system developed by NASA Ames with assistance from the Lockheed Space Operations Company, and the Lockheed Artificial Intelligence Center, uses a method called constraint-based iterative repair. Using this technique, one encodes both hard rules and preference criteria into data structures called constraints. GERRY repeatedly attempts to improve schedules by seeking repairs for violated constraints. The system provides a general scheduling framework which is being tested on two NASA applications. The larger of the two is the Space Shuttle Ground Processing problem which entails the scheduling of all the inspection, repair, and maintenance tasks required to prepare the orbiter for flight. The other application involves power allocation for the NASA Ames wind tunnels. Here the system will be used to schedule wind tunnel tests with the goal of minimizing power costs. In this paper, we describe the GERRY system and its application to the Space Shuttle problem. We also speculate as to how the system would be used for manufacturing, transportation, and military problems.

  11. Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins

    DEFF Research Database (Denmark)

    Irani, Zahra Azimzadeh; Kerkhoven, Eduard J.; Shojaosadati, Seyed Abbas

    2016-01-01

    prediction of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required...... for using these models to understand and optimize protein production processes....

  12. Reconstruction of Oryza sativa indica Genome Scale Metabolic Model and Its Responses to Varying RuBisCO Activity, Light Intensity, and Enzymatic Cost Conditions

    Directory of Open Access Journals (Sweden)

    Ankita Chatterjee

    2017-11-01

    Full Text Available To combat decrease in rice productivity under different stresses, an understanding of rice metabolism is needed. Though there are different genome scale metabolic models (GSMs of Oryza sativa japonica, no GSM with gene-protein-reaction association exist for Oryza sativa indica. Here, we report a GSM, OSI1136 of O.s. indica, which includes 3602 genes and 1136 metabolic reactions and transporters distributed across the cytosol, mitochondrion, peroxisome, and chloroplast compartments. Flux balance analysis of the model showed that for varying RuBisCO activity (Vc/Vo (i the activity of the chloroplastic malate valve increases to transport reducing equivalents out of the chloroplast under increased photorespiratory conditions and (ii glyceraldehyde-3-phosphate dehydrogenase and phosphoglycerate kinase can act as source of cytosolic ATP under decreased photorespiration. Under increasing light conditions we observed metabolic flexibility, involving photorespiration, chloroplastic triose phosphate and the dicarboxylate transporters of the chloroplast and mitochondrion for redox and ATP exchanges across the intracellular compartments. Simulations under different enzymatic cost conditions revealed (i participation of peroxisomal glutathione-ascorbate cycle in photorespiratory H2O2 metabolism (ii different modes of the chloroplastic triose phosphate transporters and malate valve, and (iii two possible modes of chloroplastic Glu–Gln transporter which were related with the activity of chloroplastic and cytosolic isoforms of glutamine synthetase. Altogether, our results provide new insights into plant metabolism.

  13. Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation.

    Science.gov (United States)

    Cordes, Henrik; Thiel, Christoph; Baier, Vanessa; Blank, Lars M; Kuepfer, Lars

    2018-01-01

    Drug-induced perturbations of the endogenous metabolic network are a potential root cause of cellular toxicity. A mechanistic understanding of such unwanted side effects during drug therapy is therefore vital for patient safety. The comprehensive assessment of such drug-induced injuries requires the simultaneous consideration of both drug exposure at the whole-body and resulting biochemical responses at the cellular level. We here present a computational multi-scale workflow that combines whole-body physiologically based pharmacokinetic (PBPK) models and organ-specific genome-scale metabolic network (GSMN) models through shared reactions of the xenobiotic metabolism. The applicability of the proposed workflow is illustrated for isoniazid, a first-line antibacterial agent against Mycobacterium tuberculosis , which is known to cause idiosyncratic drug-induced liver injuries (DILI). We combined GSMN models of a human liver with N-acetyl transferase 2 (NAT2)-phenotype-specific PBPK models of isoniazid. The combined PBPK-GSMN models quantitatively describe isoniazid pharmacokinetics, as well as intracellular responses, and changes in the exometabolome in a human liver following isoniazid administration. Notably, intracellular and extracellular responses identified with the PBPK-GSMN models are in line with experimental and clinical findings. Moreover, the drug-induced metabolic perturbations are distributed and attenuated in the metabolic network in a phenotype-dependent manner. Our simulation results show that a simultaneous consideration of both drug pharmacokinetics at the whole-body and metabolism at the cellular level is mandatory to explain drug-induced injuries at the patient level. The proposed workflow extends our mechanistic understanding of the biochemistry underlying adverse events and may be used to prevent drug-induced injuries in the future.

  14. Analysis of metabolic networks of Streptomyces leeuwenhoekii C34 by means of a genome scale model: Prediction of modifications that enhance the production of specialized metabolites.

    Science.gov (United States)

    Razmilic, Valeria; Castro, Jean F; Andrews, Barbara; Asenjo, Juan A

    2018-07-01

    The first genome scale model (GSM) for Streptomyces leeuwenhoekii C34 was developed to study the biosynthesis pathways of specialized metabolites and to find metabolic engineering targets for enhancing their production. The model, iVR1007, consists of 1,722 reactions, 1,463 metabolites, and 1,007 genes, it includes the biosynthesis pathways of chaxamycins, chaxalactins, desferrioxamines, ectoine, and other specialized metabolites. iVR1007 was validated using experimental information of growth on 166 different sources of carbon, nitrogen and phosphorous, showing an 83.7% accuracy. The model was used to predict metabolic engineering targets for enhancing the biosynthesis of chaxamycins and chaxalactins. Gene knockouts, such as sle03600 (L-homoserine O-acetyltransferase), and sle39090 (trehalose-phosphate synthase), that enhance the production of the specialized metabolites by increasing the pool of precursors were identified. Using the algorithm of flux scanning based on enforced objective flux (FSEOF) implemented in python, 35 and 25 over-expression targets for increasing the production of chaxamycin A and chaxalactin A, respectively, that were not directly associated with their biosynthesis routes were identified. Nineteen over-expression targets that were common to the two specialized metabolites studied, like the over-expression of the acetyl carboxylase complex (sle47660 (accA) and any of the following genes: sle44630 (accA_1) or sle39830 (accA_2) or sle27560 (bccA) or sle59710) were identified. The predicted knockouts and over-expression targets will be used to perform metabolic engineering of S. leeuwenhoekii C34 and obtain overproducer strains. © 2018 Wiley Periodicals, Inc.

  15. 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.

  16. Using proteomic data to assess a genome-scale "in silico" model of metal reducing bacteria in the simulation of field-scale uranium bioremediation

    Science.gov (United States)

    Yabusaki, S.; Fang, Y.; Wilkins, M. J.; Long, P.; Rifle IFRC Science Team

    2011-12-01

    A series of field experiments in a shallow alluvial aquifer at a former uranium mill tailings site have demonstrated that indigenous bacteria can be stimulated with acetate to catalyze the conversion of hexavalent uranium in a groundwater plume to immobile solid-associated uranium in the +4 oxidation state. While this bioreduction of uranium has been shown to lower groundwater concentrations below actionable standards, a viable remediation methodology will need a mechanistic, predictive and quantitative understanding of the microbially-mediated reactions that catalyze the reduction of uranium in the context of site-specific processes, properties, and conditions. At the Rifle IFRC site, we are investigating the impacts on uranium behavior of pulsed acetate amendment, acetate-oxidizing iron and sulfate reducing bacteria, seasonal water table variation, spatially-variable physical (hydraulic conductivity, porosity) and geochemical (reactive surface area) material properties. The simulation of three-dimensional, variably saturated flow and biogeochemical reactive transport during a uranium bioremediation field experiment includes a genome-scale in silico model of Geobacter sp. to represent the Fe(III) terminal electron accepting process (TEAP). The Geobacter in silico model of cell-scale physiological metabolic pathways is comprised of hundreds of intra-cellular and environmental exchange reactions. One advantage of this approach is that the TEAP reaction stoichiometry and rate are now functions of the metabolic status of the microorganism. The linkage of in silico model reactions to specific Geobacter proteins has enabled the use of groundwater proteomic analyses to assess the accuracy of the model under evolving hydrologic and biogeochemical conditions. In this case, the largest predicted fluxes through in silico model reactions generally correspond to high abundances of proteins linked to those reactions (e.g. the condensation reaction catalyzed by the protein

  17. Genome-scale metabolic modeling to provide insight into the production of storage compounds during feast-famine cycles of activated sludge.

    Science.gov (United States)

    Tajparast, Mohammad; Frigon, Dominic

    2013-01-01

    Studying storage metabolism during feast-famine cycles of activated sludge treatment systems provides profound insight in terms of both operational issues (e.g., foaming and bulking) and process optimization for the production of value added by-products (e.g., bioplastics). We examined the storage metabolism (including poly-β-hydroxybutyrate [PHB], glycogen, and triacylglycerols [TAGs]) during feast-famine cycles using two genome-scale metabolic models: Rhodococcus jostii RHA1 (iMT1174) and Escherichia coli K-12 (iAF1260) for growth on glucose, acetate, and succinate. The goal was to develop the proper objective function (OF) for the prediction of the main storage compound produced in activated sludge for given feast-famine cycle conditions. For the flux balance analysis, combinations of three OFs were tested. For all of them, the main OF was to maximize growth rates. Two additional sub-OFs were used: (1) minimization of biochemical fluxes, and (2) minimization of metabolic adjustments (MoMA) between the feast and famine periods. All (sub-)OFs predicted identical substrate-storage associations for the feast-famine growth of the above-mentioned metabolic models on a given substrate when glucose and acetate were set as sole carbon sources (i.e., glucose-glycogen and acetate-PHB), in agreement with experimental observations. However, in the case of succinate as substrate, the predictions depended on the network structure of the metabolic models such that the E. coli model predicted glycogen accumulation and the R. jostii model predicted PHB accumulation. While the accumulation of both PHB and glycogen was observed experimentally, PHB showed higher dynamics during an activated sludge feast-famine growth cycle with succinate as substrate. These results suggest that new modeling insights between metabolic predictions and population ecology will be necessary to properly predict metabolisms likely to emerge within the niches of activated sludge communities. Nonetheless

  18. The OME Framework for genome-scale systems biology

    Energy Technology Data Exchange (ETDEWEB)

    Palsson, Bernhard O. [Univ. of California, San Diego, CA (United States); Ebrahim, Ali [Univ. of California, San Diego, CA (United States); Federowicz, Steve [Univ. of California, San Diego, CA (United States)

    2014-12-19

    The life sciences are undergoing continuous and accelerating integration with computational and engineering sciences. The biology that many in the field have been trained on may be hardly recognizable in ten to twenty years. One of the major drivers for this transformation is the blistering pace of advancements in DNA sequencing and synthesis. These advances have resulted in unprecedented amounts of new data, information, and knowledge. Many software tools have been developed to deal with aspects of this transformation and each is sorely needed [1-3]. However, few of these tools have been forced to deal with the full complexity of genome-scale models along with high throughput genome- scale data. This particular situation represents a unique challenge, as it is simultaneously necessary to deal with the vast breadth of genome-scale models and the dizzying depth of high-throughput datasets. It has been observed time and again that as the pace of data generation continues to accelerate, the pace of analysis significantly lags behind [4]. It is also evident that, given the plethora of databases and software efforts [5-12], it is still a significant challenge to work with genome-scale metabolic models, let alone next-generation whole cell models [13-15]. We work at the forefront of model creation and systems scale data generation [16-18]. The OME Framework was borne out of a practical need to enable genome-scale modeling and data analysis under a unified framework to drive the next generation of genome-scale biological models. Here we present the OME Framework. It exists as a set of Python classes. However, we want to emphasize the importance of the underlying design as an addition to the discussions on specifications of a digital cell. A great deal of work and valuable progress has been made by a number of communities [13, 19-24] towards interchange formats and implementations designed to achieve similar goals. While many software tools exist for handling genome-scale

  19. Modelling central metabolic fluxes by constraint-based optimization reveals metabolic reprogramming of developing Solanum lycopersicum (tomato) fruit.

    Science.gov (United States)

    Colombié, Sophie; Nazaret, Christine; Bénard, Camille; Biais, Benoît; Mengin, Virginie; Solé, Marion; Fouillen, Laëtitia; Dieuaide-Noubhani, Martine; Mazat, Jean-Pierre; Beauvoit, Bertrand; Gibon, Yves

    2015-01-01

    Modelling of metabolic networks is a powerful tool to analyse the behaviour of developing plant organs, including fruits. Guided by our current understanding of heterotrophic metabolism of plant cells, a medium-scale stoichiometric model, including the balance of co-factors and energy, was constructed in order to describe metabolic shifts that occur through the nine sequential stages of Solanum lycopersicum (tomato) fruit development. The measured concentrations of the main biomass components and the accumulated metabolites in the pericarp, determined at each stage, were fitted in order to calculate, by derivation, the corresponding external fluxes. They were used as constraints to solve the model by minimizing the internal fluxes. The distribution of the calculated fluxes of central metabolism were then analysed and compared with known metabolic behaviours. For instance, the partition of the main metabolic pathways (glycolysis, pentose phosphate pathway, etc.) was relevant throughout fruit development. We also predicted a valid import of carbon and nitrogen by the fruit, as well as a consistent CO2 release. Interestingly, the energetic balance indicates that excess ATP is dissipated just before the onset of ripening, supporting the concept of the climacteric crisis. Finally, the apparent contradiction between calculated fluxes with low values compared with measured enzyme capacities suggest a complex reprogramming of the metabolic machinery during fruit development. With a powerful set of experimental data and an accurate definition of the metabolic system, this work provides important insight into the metabolic and physiological requirements of the developing tomato fruits. © 2014 The Authors The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

  20. Constraint-Based Modeling Highlights Cell Energy, Redox Status and α-Ketoglutarate Availability as Metabolic Drivers for Anthocyanin Accumulation in Grape Cells Under Nitrogen Limitation

    Directory of Open Access Journals (Sweden)

    Eric Soubeyrand

    2018-05-01

    Full Text Available Anthocyanin biosynthesis is regulated by environmental factors (such as light, temperature, and water availability and nutrient status (such as carbon, nitrogen, and phosphate nutrition. Previous reports show that low nitrogen availability strongly enhances anthocyanin accumulation in non carbon-limited plant organs or cell suspensions. It has been hypothesized that high carbon-to-nitrogen ratio would lead to an energy excess in plant cells, and that an increase in flavonoid pathway metabolic fluxes would act as an “energy escape valve,” helping plant cells to cope with energy and carbon excess. However, this hypothesis has never been tested directly. To this end, we used the grapevine Vitis vinifera L. cultivar Gamay Teinturier (syn. Gamay Freaux or Freaux Tintorier, VIVC #4382 cell suspension line as a model system to study the regulation of anthocyanin accumulation in response to nitrogen supply. The cells were sub-cultured in the presence of either control (25 mM or low (5 mM nitrate concentration. Targeted metabolomics and enzyme activity determinations were used to parametrize a constraint-based model describing both the central carbon and nitrogen metabolisms and the flavonoid (phenylpropanoid pathway connected by the energy (ATP and reducing power equivalents (NADPH and NADH cofactors. The flux analysis (2 flux maps generated, for control and low nitrogen in culture medium clearly showed that in low nitrogen-fed cells all the metabolic fluxes of central metabolism were decreased, whereas fluxes that consume energy and reducing power, were either increased (upper part of glycolysis, shikimate, and flavonoid pathway or maintained (pentose phosphate pathway. Also, fluxes of flavanone 3β-hydroxylase, flavonol synthase, and anthocyanidin synthase were strongly increased, advocating for a regulation of the flavonoid pathway by alpha-ketoglutarate levels. These results strongly support the hypothesis of anthocyanin biosynthesis acting as

  1. Effect of amino acid supplementation on titer and glycosylation distribution in hybridoma cell cultures-Systems biology-based interpretation using genome-scale metabolic flux balance model and multivariate data analysis.

    Science.gov (United States)

    Reimonn, Thomas M; Park, Seo-Young; Agarabi, Cyrus D; Brorson, Kurt A; Yoon, Seongkyu

    2016-09-01

    Genome-scale flux balance analysis (FBA) is a powerful systems biology tool to characterize intracellular reaction fluxes during cell cultures. FBA estimates intracellular reaction rates by optimizing an objective function, subject to the constraints of a metabolic model and media uptake/excretion rates. A dynamic extension to FBA, dynamic flux balance analysis (DFBA), can calculate intracellular reaction fluxes as they change during cell cultures. In a previous study by Read et al. (2013), a series of informed amino acid supplementation experiments were performed on twelve parallel murine hybridoma cell cultures, and this data was leveraged for further analysis (Read et al., Biotechnol Prog. 2013;29:745-753). In order to understand the effects of media changes on the model murine hybridoma cell line, a systems biology approach is applied in the current study. Dynamic flux balance analysis was performed using a genome-scale mouse metabolic model, and multivariate data analysis was used for interpretation. The calculated reaction fluxes were examined using partial least squares and partial least squares discriminant analysis. The results indicate media supplementation increases product yield because it raises nutrient levels extending the growth phase, and the increased cell density allows for greater culture performance. At the same time, the directed supplementation does not change the overall metabolism of the cells. This supports the conclusion that product quality, as measured by glycoform assays, remains unchanged because the metabolism remains in a similar state. Additionally, the DFBA shows that metabolic state varies more at the beginning of the culture but less by the middle of the growth phase, possibly due to stress on the cells during inoculation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1163-1173, 2016. © 2016 American Institute of Chemical Engineers.

  2. 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...

  3. Genome-scale reconstruction and in silico analysis of the Ralstonia eutropha H16 for polyhydroxyalkanoate synthesis, lithoautotrophic growth, and 2-methyl citric acid production

    Directory of Open Access Journals (Sweden)

    Kim Tae

    2011-06-01

    Full Text Available Abstract Background Ralstonia eutropha H16, found in both soil and water, is a Gram-negative lithoautotrophic bacterium that can utillize CO2 and H2 as its sources of carbon and energy in the absence of organic substrates. R. eutropha H16 can reach high cell densities either under lithoautotrophic or heterotrophic conditions, which makes it suitable for a number of biotechnological applications. It is the best known and most promising producer of polyhydroxyalkanoates (PHAs from various carbon substrates and is an environmentally important bacterium that can degrade aromatic compounds. In order to make R. eutropha H16 a more efficient and robust biofactory, system-wide metabolic engineering to improve its metabolic performance is essential. Thus, it is necessary to analyze its metabolic characteristics systematically and optimize the entire metabolic network at systems level. Results We present the lithoautotrophic genome-scale metabolic model of R. eutropha H16 based on the annotated genome with biochemical and physiological information. The stoichiometic model, RehMBEL1391, is composed of 1391 reactions including 229 transport reactions and 1171 metabolites. Constraints-based flux analyses were performed to refine and validate the genome-scale metabolic model under environmental and genetic perturbations. First, the lithoautotrophic growth characteristics of R. eutropha H16 were investigated under varying feeding ratios of gas mixture. Second, the genome-scale metabolic model was used to design the strategies for the production of poly[R-(--3hydroxybutyrate] (PHB under different pH values and carbon/nitrogen source uptake ratios. It was also used to analyze the metabolic characteristics of R. eutropha when the phosphofructokinase gene was expressed. Finally, in silico gene knockout simulations were performed to identify targets for metabolic engineering essential for the production of 2-methylcitric acid in R. eutropha H16. Conclusion The

  4. Genome scale engineering techniques for metabolic engineering.

    Science.gov (United States)

    Liu, Rongming; Bassalo, Marcelo C; Zeitoun, Ramsey I; Gill, Ryan T

    2015-11-01

    Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  5. Genome-scale neurogenetics: methodology and meaning.

    Science.gov (United States)

    McCarroll, Steven A; Feng, Guoping; Hyman, Steven E

    2014-06-01

    Genetic analysis is currently offering glimpses into molecular mechanisms underlying such neuropsychiatric disorders as schizophrenia, bipolar disorder and autism. After years of frustration, success in identifying disease-associated DNA sequence variation has followed from new genomic technologies, new genome data resources, and global collaborations that could achieve the scale necessary to find the genes underlying highly polygenic disorders. Here we describe early results from genome-scale studies of large numbers of subjects and the emerging significance of these results for neurobiology.

  6. Fuzzy Constraint-Based Agent Negotiation

    Institute of Scientific and Technical Information of China (English)

    Menq-Wen Lin; K. Robert Lai; Ting-Jung Yu

    2005-01-01

    Conflicts between two or more parties arise for various reasons and perspectives. Thus, resolution of conflicts frequently relies on some form of negotiation. This paper presents a general problem-solving framework for modeling multi-issue multilateral negotiation using fuzzy constraints. Agent negotiation is formulated as a distributed fuzzy constraint satisfaction problem (DFCSP). Fuzzy constrains are thus used to naturally represent each agent's desires involving imprecision and human conceptualization, particularly when lexical imprecision and subjective matters are concerned. On the other hand, based on fuzzy constraint-based problem-solving, our approach enables an agent not only to systematically relax fuzzy constraints to generate a proposal, but also to employ fuzzy similarity to select the alternative that is subject to its acceptability by the opponents. This task of problem-solving is to reach an agreement that benefits all agents with a high satisfaction degree of fuzzy constraints, and move towards the deal more quickly since their search focuses only on the feasible solution space. An application to multilateral negotiation of a travel planning is provided to demonstrate the usefulness and effectiveness of our framework.

  7. Systems Biology Approach to Bioremediation of Nitroaromatics: Constraint-Based Analysis of 2,4,6-Trinitrotoluene Biotransformation by Escherichia coli

    Directory of Open Access Journals (Sweden)

    Maryam Iman

    2017-08-01

    Full Text Available Microbial remediation of nitroaromatic compounds (NACs is a promising environmentally friendly and cost-effective approach to the removal of these life-threating agents. Escherichia coli (E. coli has shown remarkable capability for the biotransformation of 2,4,6-trinitro-toluene (TNT. Efforts to develop E. coli as an efficient TNT degrading biocatalyst will benefit from holistic flux-level description of interactions between multiple TNT transforming pathways operating in the strain. To gain such an insight, we extended the genome-scale constraint-based model of E. coli to account for a curated version of major TNT transformation pathways known or evidently hypothesized to be active in E. coli in present of TNT. Using constraint-based analysis (CBA methods, we then performed several series of in silico experiments to elucidate the contribution of these pathways individually or in combination to the E. coli TNT transformation capacity. Results of our analyses were validated by replicating several experimentally observed TNT degradation phenotypes in E. coli cultures. We further used the extended model to explore the influence of process parameters, including aeration regime, TNT concentration, cell density, and carbon source on TNT degradation efficiency. We also conducted an in silico metabolic engineering study to design a series of E. coli mutants capable of degrading TNT at higher yield compared with the wild-type strain. Our study, therefore, extends the application of CBA to bioremediation of nitroaromatics and demonstrates the usefulness of this approach to inform bioremediation research.

  8. Development of the hard and soft constraints based optimisation model for unit sizing of the hybrid renewable energy system designed for microgrid applications

    Science.gov (United States)

    Sundaramoorthy, Kumaravel

    2017-02-01

    The hybrid energy systems (HESs) based electricity generation system has become a more attractive solution for rural electrification nowadays. Economically feasible and technically reliable HESs are solidly based on an optimisation stage. This article discusses about the optimal unit sizing model with the objective function to minimise the total cost of the HES. Three typical rural sites from southern part of India have been selected for the application of the developed optimisation methodology. Feasibility studies and sensitivity analysis on the optimal HES are discussed elaborately in this article. A comparison has been carried out with the Hybrid Optimization Model for Electric Renewable optimisation model for three sites. The optimal HES is found with less total net present rate and rate of energy compared with the existing method

  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. 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

  11. Analysis of regional total factor energy efficiency in China under environmental constraints: based on undesirable-minds and DEA window model

    Science.gov (United States)

    Zhang, Shuying; Li, Deshan; Li, Shuangqiang; Jiang, Hanyu; Shen, Yuqing

    2017-06-01

    With China’s entrance into the new economy, the improvement of energy efficiency has become an important indicator to measure the quality of ecological civilization construction and economic development. According to the panel data of Chinese regions in 1996-2014, the nearest distance to the efficient frontier of Undesirable-MinDS Xeon model and DEA window model have been used to calculate the total factor energy efficiency of China’s regions. Study found that: Under environmental constraints, China’s total factor energy efficiency has increased after the first drop in the overall 1996-2014, and then increases again. And the difference between the regions is very large, showing a characteristic of “the east is the highest, the west is lower, and lowest is in the central” finally, this paper puts forward relevant policy suggestions.

  12. Constraint-based scheduling applying constraint programming to scheduling problems

    CERN Document Server

    Baptiste, Philippe; Nuijten, Wim

    2001-01-01

    Constraint Programming is a problem-solving paradigm that establishes a clear distinction between two pivotal aspects of a problem: (1) a precise definition of the constraints that define the problem to be solved and (2) the algorithms and heuristics enabling the selection of decisions to solve the problem. It is because of these capabilities that Constraint Programming is increasingly being employed as a problem-solving tool to solve scheduling problems. Hence the development of Constraint-Based Scheduling as a field of study. The aim of this book is to provide an overview of the most widely used Constraint-Based Scheduling techniques. Following the principles of Constraint Programming, the book consists of three distinct parts: The first chapter introduces the basic principles of Constraint Programming and provides a model of the constraints that are the most often encountered in scheduling problems. Chapters 2, 3, 4, and 5 are focused on the propagation of resource constraints, which usually are responsibl...

  13. An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice

    Directory of Open Access Journals (Sweden)

    Liu Lili

    2013-06-01

    Full Text Available Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs, protein-protein interactions (PPIs and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

  14. solveME: fast and reliable solution of nonlinear ME models

    DEFF Research Database (Denmark)

    Yang, Laurence; Ma, Ding; Ebrahim, Ali

    2016-01-01

    Background: Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstr......Background: Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic...... reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints. Results: Here, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models...

  15. Constraint-based Attribute and Interval Planning

    Science.gov (United States)

    Jonsson, Ari; Frank, Jeremy

    2013-01-01

    In this paper we describe Constraint-based Attribute and Interval Planning (CAIP), a paradigm for representing and reasoning about plans. The paradigm enables the description of planning domains with time, resources, concurrent activities, mutual exclusions among sets of activities, disjunctive preconditions and conditional effects. We provide a theoretical foundation for the paradigm, based on temporal intervals and attributes. We then show how the plans are naturally expressed by networks of constraints, and show that the process of planning maps directly to dynamic constraint reasoning. In addition, we de ne compatibilities, a compact mechanism for describing planning domains. We describe how this framework can incorporate the use of constraint reasoning technology to improve planning. Finally, we describe EUROPA, an implementation of the CAIP framework.

  16. Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine.

    Science.gov (United States)

    Aurich, Maike K; Thiele, Ines

    2016-01-01

    Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model's topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine.

  17. The Genome-Scale Integrated Networks in Microorganisms

    Directory of Open Access Journals (Sweden)

    Tong Hao

    2018-02-01

    Full Text Available The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types of the molecular networks, for example, genome-scale metabolic network (GMN, transcriptional regulatory network (TRN, and signal transduction network (STN. It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.

  18. Constraint-Based Local Search for Constrained Optimum Paths Problems

    Science.gov (United States)

    Pham, Quang Dung; Deville, Yves; van Hentenryck, Pascal

    Constrained Optimum Path (COP) problems arise in many real-life applications and are ubiquitous in communication networks. They have been traditionally approached by dedicated algorithms, which are often hard to extend with side constraints and to apply widely. This paper proposes a constraint-based local search (CBLS) framework for COP applications, bringing the compositionality, reuse, and extensibility at the core of CBLS and CP systems. The modeling contribution is the ability to express compositional models for various COP applications at a high level of abstraction, while cleanly separating the model and the search procedure. The main technical contribution is a connected neighborhood based on rooted spanning trees to find high-quality solutions to COP problems. The framework, implemented in COMET, is applied to Resource Constrained Shortest Path (RCSP) problems (with and without side constraints) and to the edge-disjoint paths problem (EDP). Computational results show the potential significance of the approach.

  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. Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

    Science.gov (United States)

    Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen

    2017-02-01

    Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

  1. Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

    Science.gov (United States)

    Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

    2016-09-19

    Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

  2. 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; Kierzek, Andrzej M

    2017-11-01

    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. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

  3. Evaluating Direct Manipulation Operations for Constraint-Based Layout

    OpenAIRE

    Zeidler , Clemens; Lutteroth , Christof; Stuerzlinger , Wolfgang; Weber , Gerald

    2013-01-01

    Part 11: Interface Layout and Data Entry; International audience; Layout managers are used to control the placement of widgets in graphical user interfaces (GUIs). Constraint-based layout managers are more powerful than other ones. However, they are also more complex and their layouts are prone to problems that usually require direct editing of constraints. Today, designers commonly use GUI builders to specify GUIs. The complexities of traditional approaches to constraint-based layouts pose c...

  4. Genome-scale reconstruction of metabolic networks of Lactobacillus casei ATCC 334 and 12A.

    Directory of Open Access Journals (Sweden)

    Elena Vinay-Lara

    Full Text Available Lactobacillus casei strains are widely used in industry and the utility of this organism in these industrial applications is strain dependent. Hence, tools capable of predicting strain specific phenotypes would have utility in the selection of strains for specific industrial processes. Genome-scale metabolic models can be utilized to better understand genotype-phenotype relationships and to compare different organisms. To assist in the selection and development of strains with enhanced industrial utility, genome-scale models for L. casei ATCC 334, a well characterized strain, and strain 12A, a corn silage isolate, were constructed. Draft models were generated from RAST genome annotations using the Model SEED database and refined by evaluating ATP generating cycles, mass-and-charge-balances of reactions, and growth phenotypes. After the validation process was finished, we compared the metabolic networks of these two strains to identify metabolic, genetic and ortholog differences that may lead to different phenotypic behaviors. We conclude that the metabolic capabilities of the two networks are highly similar. The L. casei ATCC 334 model accounts for 1,040 reactions, 959 metabolites and 548 genes, while the L. casei 12A model accounts for 1,076 reactions, 979 metabolites and 640 genes. The developed L. casei ATCC 334 and 12A metabolic models will enable better understanding of the physiology of these organisms and be valuable tools in the development and selection of strains with enhanced utility in a variety of industrial applications.

  5. Constraint-based solver for the Military unit path finding problem

    CSIR Research Space (South Africa)

    Leenen, L

    2010-04-01

    Full Text Available -based approach because it requires flexibility in modelling. The authors formulate the MUPFP as a constraint satisfaction problem and a constraint-based extension of the search algorithm. The concept demonstrator uses a provided map, for example taken from Google...

  6. Designing a Constraint Based Parser for Sanskrit

    Science.gov (United States)

    Kulkarni, Amba; Pokar, Sheetal; Shukl, Devanand

    Verbal understanding (śā bdabodha) of any utterance requires the knowledge of how words in that utterance are related to each other. Such knowledge is usually available in the form of cognition of grammatical relations. Generative grammars describe how a language codes these relations. Thus the knowledge of what information various grammatical relations convey is available from the generation point of view and not the analysis point of view. In order to develop a parser based on any grammar one should then know precisely the semantic content of the grammatical relations expressed in a language string, the clues for extracting these relations and finally whether these relations are expressed explicitly or implicitly. Based on the design principles that emerge from this knowledge, we model the parser as finding a directed Tree, given a graph with nodes representing the words and edges representing the possible relations between them. Further, we also use the Mīmā ṃsā constraint of ākā ṅkṣā (expectancy) to rule out non-solutions and sannidhi (proximity) to prioritize the solutions. We have implemented a parser based on these principles and its performance was found to be satisfactory giving us a confidence to extend its functionality to handle the complex sentences.

  7. 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.

  8. Constraint-based job shop scheduling with ILOG SCHEDULER

    NARCIS (Netherlands)

    Nuijten, W.P.M.; Le Pape, C.

    1998-01-01

    We introduce constraint-based scheduling and discuss its main principles. An approximation algorithm based on tree search is developed for the job shop scheduling problem using ILOG SCHEDULER. A new way of calculating lower bounds on the makespan of the job shop scheduling problem is presented and

  9. Teaching Database Design with Constraint-Based Tutors

    Science.gov (United States)

    Mitrovic, Antonija; Suraweera, Pramuditha

    2016-01-01

    Design tasks are difficult to teach, due to large, unstructured solution spaces, underspecified problems, non-existent problem solving algorithms and stopping criteria. In this paper, we comment on our approach to develop KERMIT, a constraint-based tutor that taught database design. In later work, we re-implemented KERMIT as EER-Tutor, and…

  10. Large-Scale Constraint-Based Pattern Mining

    Science.gov (United States)

    Zhu, Feida

    2009-01-01

    We studied the problem of constraint-based pattern mining for three different data formats, item-set, sequence and graph, and focused on mining patterns of large sizes. Colossal patterns in each data formats are studied to discover pruning properties that are useful for direct mining of these patterns. For item-set data, we observed robustness of…

  11. Probing the genome-scale metabolic landscape of Bordetella pertussis, the causative agent of whooping cough.

    Science.gov (United States)

    Branco Dos Santos, Filipe; Olivier, Brett G; Boele, Joost; Smessaert, Vincent; De Rop, Philippe; Krumpochova, Petra; Klau, Gunnar W; Giera, Martin; Dehottay, Philippe; Teusink, Bas; Goffin, Philippe

    2017-08-25

    Whooping cough is a highly-contagious respiratory disease caused by Bordetella pertussi s. Despite vaccination, its incidence has been rising alarmingly, and yet, the physiology of B. pertussis remains poorly understood. We combined genome-scale metabolic reconstruction, a novel optimization algorithm and experimental data to probe the full metabolic potential of this pathogen, using strain Tohama I as a reference. Experimental validation showed that B. pertussis secretes a significant proportion of nitrogen as arginine and purine nucleosides, which may contribute to modulation of the host response. We also found that B. pertussis can be unexpectedly versatile, being able to metabolize many compounds while displaying minimal nutrient requirements. It can grow without cysteine - using inorganic sulfur sources such as thiosulfate - and it can grow on organic acids such as citrate or lactate as sole carbon sources, providing in vivo demonstration that its TCA cycle is functional. Although the metabolic reconstruction of eight additional strains indicates that the structural genes underlying this metabolic flexibility are widespread, experimental validation suggests a role of strain-specific regulatory mechanisms in shaping metabolic capabilities. Among five alternative strains tested, three were shown to grow on substrate combinations requiring a functional TCA cycle, but only one could use thiosulfate. Finally, the metabolic model was used to rationally design growth media with over two-fold improvements in pertussis toxin production. This study thus provides novel insights into B. pertussis physiology, and highlights the potential, but also limitations of models solely based on metabolic gene content. IMPORTANCE The metabolic capabilities of Bordetella pertussis - the causative agent of whooping cough - were investigated from a systems-level perspective. We constructed a comprehensive genome-scale metabolic model for B. pertussis , and challenged its predictions

  12. Zea mays iRS1563: A Comprehensive Genome-Scale Metabolic Reconstruction of Maize Metabolism

    Science.gov (United States)

    Saha, Rajib; Suthers, Patrick F.; Maranas, Costas D.

    2011-01-01

    The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species. PMID:21755001

  13. Toward the automated generation of genome-scale metabolic networks in the SEED.

    Science.gov (United States)

    DeJongh, Matthew; Formsma, Kevin; Boillot, Paul; Gould, John; Rycenga, Matthew; Best, Aaron

    2007-04-26

    Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis. Our method sets the

  14. Toward the automated generation of genome-scale metabolic networks in the SEED

    Directory of Open Access Journals (Sweden)

    Gould John

    2007-04-01

    Full Text Available Abstract Background Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. Results We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis. We have implemented our tools and database within the SEED, an open-source software environment for comparative

  15. Genome-scale consequences of cofactor balancing in engineered pentose utilization pathways in Saccharomyces cerevisiae.

    Directory of Open Access Journals (Sweden)

    Amit Ghosh

    Full Text Available Biofuels derived from lignocellulosic biomass offer promising alternative renewable energy sources for transportation fuels. Significant effort has been made to engineer Saccharomyces cerevisiae to efficiently ferment pentose sugars such as D-xylose and L-arabinose into biofuels such as ethanol through heterologous expression of the fungal D-xylose and L-arabinose pathways. However, one of the major bottlenecks in these fungal pathways is that the cofactors are not balanced, which contributes to inefficient utilization of pentose sugars. We utilized a genome-scale model of S. cerevisiae to predict the maximal achievable growth rate for cofactor balanced and imbalanced D-xylose and L-arabinose utilization pathways. Dynamic flux balance analysis (DFBA was used to simulate batch fermentation of glucose, D-xylose, and L-arabinose. The dynamic models and experimental results are in good agreement for the wild type and for the engineered D-xylose utilization pathway. Cofactor balancing the engineered D-xylose and L-arabinose utilization pathways simulated an increase in ethanol batch production of 24.7% while simultaneously reducing the predicted substrate utilization time by 70%. Furthermore, the effects of cofactor balancing the engineered pentose utilization pathways were evaluated throughout the genome-scale metabolic network. This work not only provides new insights to the global network effects of cofactor balancing but also provides useful guidelines for engineering a recombinant yeast strain with cofactor balanced engineered pathways that efficiently co-utilizes pentose and hexose sugars for biofuels production. Experimental switching of cofactor usage in enzymes has been demonstrated, but is a time-consuming effort. Therefore, systems biology models that can predict the likely outcome of such strain engineering efforts are highly useful for motivating which efforts are likely to be worth the significant time investment.

  16. iCN718, an Updated and Improved Genome-Scale Metabolic Network Reconstruction of Acinetobacter baumannii AYE.

    Science.gov (United States)

    Norsigian, Charles J; Kavvas, Erol; Seif, Yara; Palsson, Bernhard O; Monk, Jonathan M

    2018-01-01

    Acinetobacter baumannii has become an urgent clinical threat due to the recent emergence of multi-drug resistant strains. There is thus a significant need to discover new therapeutic targets in this organism. One means for doing so is through the use of high-quality genome-scale reconstructions. Well-curated and accurate genome-scale models (GEMs) of A. baumannii would be useful for improving treatment options. We present an updated and improved genome-scale reconstruction of A. baumannii AYE, named iCN718, that improves and standardizes previous A. baumannii AYE reconstructions. iCN718 has 80% accuracy for predicting gene essentiality data and additionally can predict large-scale phenotypic data with as much as 89% accuracy, a new capability for an A. baumannii reconstruction. We further demonstrate that iCN718 can be used to analyze conserved metabolic functions in the A. baumannii core genome and to build strain-specific GEMs of 74 other A. baumannii strains from genome sequence alone. iCN718 will serve as a resource to integrate and synthesize new experimental data being generated for this urgent threat pathogen.

  17. Bio-succinic acid production: Escherichia coli strains design from genome-scale perspectives

    Directory of Open Access Journals (Sweden)

    Bashir Sajo Mienda

    2017-10-01

    Full Text Available Escherichia coli (E. coli has been established to be a native producer of succinic acid (a platform chemical with different applications via mixed acid fermentation reactions. Genome-scale metabolic models (GEMs of E. coli have been published with capabilities of predicting strain design strategies for the production of bio-based succinic acid. Proof-of-principle strains are fundamentally constructed as a starting point for systems strategies for industrial strains development. Here, we review for the first time, the use of E. coli GEMs for construction of proof-of-principles strains for increasing succinic acid production. Specific case studies, where E. coli proof-of-principle strains were constructed for increasing bio-based succinic acid production from glucose and glycerol carbon sources have been highlighted. In addition, a propose systems strategies for industrial strain development that could be applicable for future microbial succinic acid production guided by GEMs have been presented.

  18. A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks.

    Directory of Open Access Journals (Sweden)

    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.

  19. Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions

    DEFF Research Database (Denmark)

    Kavvas, Erol S.; Seif, Yara; Yurkovich, James T.

    2018-01-01

    previous M. tuberculosis H37Rv genome-scale reconstructions. We functionally assess iEK1011 against previous models and show that the model increases correct gene essentiality predictions on two different experimental datasets by 6% (53% to 60%) and 18% (60% to 71%), respectively. We compared simulations...

  20. Environmental versatility promotes modularity in genome-scale metabolic networks.

    Science.gov (United States)

    Samal, Areejit; Wagner, Andreas; Martin, Olivier C

    2011-08-24

    The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple

  1. Environmental versatility promotes modularity in genome-scale metabolic networks

    Directory of Open Access Journals (Sweden)

    Wagner Andreas

    2011-08-01

    Full Text Available Abstract Background The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Results Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Conclusions Our work shows that modularity in metabolic networks can be a by-product of functional

  2. Metabolite coupling in genome-scale metabolic networks

    Directory of Open Access Journals (Sweden)

    Palsson Bernhard Ø

    2006-03-01

    metabolites. Conclusion The coupling of metabolites is an important topological property of metabolic networks. By computing coupling quantitatively for the first time in genome-scale metabolic networks, we provide insight into the basic structure of these networks.

  3. Exploring the Spatial-Temporal Disparities of Urban Land Use Economic Efficiency in China and Its Influencing Factors under Environmental Constraints Based on a Sequential Slacks-Based Model

    Directory of Open Access Journals (Sweden)

    Hualin Xie

    2015-07-01

    Full Text Available Using a sequential slack-based measure (SSBM model, this paper analyzes the spatiotemporal disparities of urban land use economic efficiency (ULUEE under environmental constraints, and its influencing factors in 270 cities across China from 2003–2012. The main results are as follows: (1 The average ULUEE for Chinese cities is only 0.411, and out of the 270 cities, only six cities are always efficient in urban land use in the study period. Most cities have a lot of room to improve the economic output of secondary and tertiary industries, as well as environmental protection work; (2 The eastern region of China enjoys the highest ULUEE, followed by the western and central regions. Super-scale cities show the best performance of all four city scales, followed by large-scale, small-scale and medium-scale cities. Cities with relatively developed economies and less pollutant discharge always have better ULUEE; (3 The results of slack variables analysis show that most cities have problems such as the labor surplus, over-development, excessive pollutant discharge, economic output shortage, and unreasonable use of funds is the most serious one; (4 The regression results of the influencing factors show that improvements of the per capita GDP and land use intensity are helpful to raise ULUEE. The urbanization rate and the proportion of foreign enterprises’ output account for the total output in the secondary and tertiary industries only have the same effect in some regions and city scales. The land management policy and land leasing policy have negative impact on the ULUEE in all the three regions and four city scales; (5 Some targeted policy goals are proposed, including the reduction of surplus labor, and pay more attention to environmental protection. Most importantly, effective implementation of land management policies from the central government, and stopping blind leasing of land to make up the local government’s financial deficit would be very

  4. A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism

    KAUST Repository

    Hefzi, Hooman

    2016-11-23

    Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

  5. Simulation of deep penetration welding of stainless steel using geometric constraints based on experimental information

    International Nuclear Information System (INIS)

    Milewski, J.O.; Lambrakos, S.G.

    1995-01-01

    This report presents a general overview of a method of numerically modelling deep penetration welding processes using geometric constraints based on boundary information obtained from experiment. General issues are considered concerning accurate numerical calculation of temperature and velocity fields in regions of the meltpool where the flow of fluid is characterized by quasi-stationary Stokes flow. It is this region of the meltpool which is closest to the heat-affected-zone (HAZ) and which represents a significant fraction of the fusion zone (FZ)

  6. 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.

  7. Ensembl Genomes: an integrative resource for genome-scale data from non-vertebrate species.

    Science.gov (United States)

    Kersey, Paul J; Staines, Daniel M; Lawson, Daniel; Kulesha, Eugene; Derwent, Paul; Humphrey, Jay C; Hughes, Daniel S T; Keenan, Stephan; Kerhornou, Arnaud; Koscielny, Gautier; Langridge, Nicholas; McDowall, Mark D; Megy, Karine; Maheswari, Uma; Nuhn, Michael; Paulini, Michael; Pedro, Helder; Toneva, Iliana; Wilson, Derek; Yates, Andrew; Birney, Ewan

    2012-01-01

    Ensembl Genomes (http://www.ensemblgenomes.org) is an integrative resource for genome-scale data from non-vertebrate species. The project exploits and extends technology (for genome annotation, analysis and dissemination) developed in the context of the (vertebrate-focused) Ensembl project and provides a complementary set of resources for non-vertebrate species through a consistent set of programmatic and interactive interfaces. These provide access to data including reference sequence, gene models, transcriptional data, polymorphisms and comparative analysis. Since its launch in 2009, Ensembl Genomes has undergone rapid expansion, with the goal of providing coverage of all major experimental organisms, and additionally including taxonomic reference points to provide the evolutionary context in which genes can be understood. Against the backdrop of a continuing increase in genome sequencing activities in all parts of the tree of life, we seek to work, wherever possible, with the communities actively generating and using data, and are participants in a growing range of collaborations involved in the annotation and analysis of genomes.

  8. 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.

  9. Genome-scale metabolic network validation of Shewanella oneidensis using transposon insertion frequency analysis.

    Directory of Open Access Journals (Sweden)

    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.

  10. Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production

    Directory of Open Access Journals (Sweden)

    Brooks J Paul

    2010-03-01

    Full Text Available Abstract Background Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405 is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous. Results Here we present a genome-scale model of C. thermocellum metabolism, iSR432, for the purpose of establishing a computational tool to study the metabolic network of C. thermocellum and facilitate efforts to engineer C. thermocellum for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the iSR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production. Conclusions By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of C. thermocellum and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of C. thermocellum

  11. Clinical Processes - The Killer Application for Constraint-Based Process Interactions

    DEFF Research Database (Denmark)

    Jiménez-Ramírez, Andrés; Barba, Irene; Reichert, Manfred

    2018-01-01

    . The scenario is subject to complex temporal constraints and entails the need for coordinating the constraint-based interactions among the processes related to a patient treatment process. As demonstrated in this work, the selected real process scenario can be suitably modeled through a declarative approach....... examples. However, to the best of our knowledge, they have not been used to model complex, real-world scenarios that comprise constraints going beyond control-flow. In this paper, we propose the use of a declarative language for modeling a sophisticated healthcare process scenario from the real world......For more than a decade, the interest in aligning information systems in a process-oriented way has been increasing. To enable operational support for business processes, the latter are usually specified in an imperative way. The resulting process models, however, tend to be too rigid to meet...

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

    Directory of Open Access Journals (Sweden)

    Jorge Fernandez-de-Cossio-Diaz

    2017-11-01

    Full Text Available 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.

  13. Determining the control circuitry of redox metabolism at the genome-scale.

    Directory of Open Access Journals (Sweden)

    Stephen Federowicz

    2014-04-01

    Full Text Available Determining how facultative anaerobic organisms sense and direct cellular responses to electron acceptor availability has been a subject of intense study. However, even in the model organism Escherichia coli, established mechanisms only explain a small fraction of the hundreds of genes that are regulated during electron acceptor shifts. Here we propose a qualitative model that accounts for the full breadth of regulated genes by detailing how two global transcription factors (TFs, ArcA and Fnr of E. coli, sense key metabolic redox ratios and act on a genome-wide basis to regulate anabolic, catabolic, and energy generation pathways. We first fill gaps in our knowledge of this transcriptional regulatory network by carrying out ChIP-chip and gene expression experiments to identify 463 regulatory events. We then interfaced this reconstructed regulatory network with a highly curated genome-scale metabolic model to show that ArcA and Fnr regulate >80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions. Based on the data, we propose a feedforward with feedback trim regulatory scheme, given the extensive repression of catabolic genes by ArcA and extensive activation of chemiosmotic genes by Fnr. We further corroborated this regulatory scheme by showing a 0.71 r(2 (p<1e-6 correlation between changes in metabolic flux and changes in regulatory activity across fermentative and nitrate respiratory conditions. Finally, we are able to relate the proposed model to a wealth of previously generated data by contextualizing the existing transcriptional regulatory network.

  14. 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.

  15. Analysis of Aspergillus nidulans metabolism at the genome-scale

    DEFF Research Database (Denmark)

    David, Helga; Ozcelik, İlknur Ş; Hofmann, Gerald

    2008-01-01

    of relevant secondary metabolites, was reconstructed based on detailed metabolic reconstructions available for A. niger and Saccharomyces cerevisiae, and information on the genetics, biochemistry and physiology of A. nidulans. Thereby, it was possible to identify metabolic functions without a gene associated...... a function. Results: In this work, we have manually assigned functions to 472 orphan genes in the metabolism of A. nidulans, by using a pathway-driven approach and by employing comparative genomics tools based on sequence similarity. The central metabolism of A. nidulans, as well as biosynthetic pathways......, in an objective and systematic manner. The functional assignments served as a basis to develop a mathematical model, linking 666 genes (both previously and newly annotated) to metabolic roles. The model was used to simulate metabolic behavior and additionally to integrate, analyze and interpret large-scale gene...

  16. Genome-scale metabolic representation of Amycolatopsis balhimycina

    DEFF Research Database (Denmark)

    Vongsangnak, Wanwipa; Figueiredo, L. F.; Förster, Jochen

    2012-01-01

    Infection caused by methicillin‐resistant Staphylococcus aureus (MRSA) is an increasing societal problem. Typically, glycopeptide antibiotics are used in the treatment of these infections. The most comprehensively studied glycopeptide antibiotic biosynthetic pathway is that of balhimycin...... to reconstruct a genome‐scale metabolic model for the organism. Here we generated an almost complete A. balhimycina genome sequence comprising 10,562,587 base pairs assembled into 2,153 contigs. The high GC‐genome (∼69%) includes 8,585 open reading frames (ORFs). We used our integrative toolbox called SEQTOR...

  17. Genome-scale analysis of aberrant DNA methylation in colorectal cancer

    Science.gov (United States)

    Hinoue, Toshinori; Weisenberger, Daniel J.; Lange, Christopher P.E.; Shen, Hui; Byun, Hyang-Min; Van Den Berg, David; Malik, Simeen; Pan, Fei; Noushmehr, Houtan; van Dijk, Cornelis M.; Tollenaar, Rob A.E.M.; Laird, Peter W.

    2012-01-01

    Colorectal cancer (CRC) is a heterogeneous disease in which unique subtypes are characterized by distinct genetic and epigenetic alterations. Here we performed comprehensive genome-scale DNA methylation profiling of 125 colorectal tumors and 29 adjacent normal tissues. We identified four DNA methylation–based subgroups of CRC using model-based cluster analyses. Each subtype shows characteristic genetic and clinical features, indicating that they represent biologically distinct subgroups. A CIMP-high (CIMP-H) subgroup, which exhibits an exceptionally high frequency of cancer-specific DNA hypermethylation, is strongly associated with MLH1 DNA hypermethylation and the BRAFV600E mutation. A CIMP-low (CIMP-L) subgroup is enriched for KRAS mutations and characterized by DNA hypermethylation of a subset of CIMP-H-associated markers rather than a unique group of CpG islands. Non-CIMP tumors are separated into two distinct clusters. One non-CIMP subgroup is distinguished by a significantly higher frequency of TP53 mutations and frequent occurrence in the distal colon, while the tumors that belong to the fourth group exhibit a low frequency of both cancer-specific DNA hypermethylation and gene mutations and are significantly enriched for rectal tumors. Furthermore, we identified 112 genes that were down-regulated more than twofold in CIMP-H tumors together with promoter DNA hypermethylation. These represent ∼7% of genes that acquired promoter DNA methylation in CIMP-H tumors. Intriguingly, 48/112 genes were also transcriptionally down-regulated in non-CIMP subgroups, but this was not attributable to promoter DNA hypermethylation. Together, we identified four distinct DNA methylation subgroups of CRC and provided novel insight regarding the role of CIMP-specific DNA hypermethylation in gene silencing. PMID:21659424

  18. Estimating phylogenetic trees from genome-scale data.

    Science.gov (United States)

    Liu, Liang; Xi, Zhenxiang; Wu, Shaoyuan; Davis, Charles C; Edwards, Scott V

    2015-12-01

    The heterogeneity of signals in the genomes of diverse organisms poses challenges for traditional phylogenetic analysis. Phylogenetic methods known as "species tree" methods have been proposed to directly address one important source of gene tree heterogeneity, namely the incomplete lineage sorting that occurs when evolving lineages radiate rapidly, resulting in a diversity of gene trees from a single underlying species tree. Here we review theory and empirical examples that help clarify conflicts between species tree and concatenation methods, and misconceptions in the literature about the performance of species tree methods. Considering concatenation as a special case of the multispecies coalescent model helps explain differences in the behavior of the two methods on phylogenomic data sets. Recent work suggests that species tree methods are more robust than concatenation approaches to some of the classic challenges of phylogenetic analysis, including rapidly evolving sites in DNA sequences and long-branch attraction. We show that approaches, such as binning, designed to augment the signal in species tree analyses can distort the distribution of gene trees and are inconsistent. Computationally efficient species tree methods incorporating biological realism are a key to phylogenetic analysis of whole-genome data. © 2015 New York Academy of Sciences.

  19. Exploring massive, genome scale datasets with the genometricorr package

    KAUST Repository

    Favorov, Alexander; Mularoni, Loris; Cope, Leslie M.; Medvedeva, Yulia; Mironov, Andrey A.; Makeev, Vsevolod J.; Wheelan, Sarah J.

    2012-01-01

    We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. Availability and implementation: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor. © 2012 Favorov et al.

  20. Exploring massive, genome scale datasets with the genometricorr package

    KAUST Repository

    Favorov, Alexander

    2012-05-31

    We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. Availability and implementation: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor. © 2012 Favorov et al.

  1. Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

    DEFF Research Database (Denmark)

    Barah, Pankaj; Jayavelu, Naresh Doni; Rasmussen, Simon

    2013-01-01

    available from Arabidopsis thaliana 1001 genome project, we further investigated sequence polymorphisms in the core cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF regulon genes. Considering the limited knowledge about......BACKGROUND: Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking....... RESULTS: In this study, we report genome-scale transcript response diversity of 10 A. thaliana ecotypes originating from different geographical locations to non-freezing cold stress (10°C). To analyze the transcriptional response diversity, we initially compared transcriptome changes in all 10 ecotypes...

  2. A protocol for generating a high-quality genome-scale metabolic reconstruction.

    Science.gov (United States)

    Thiele, Ines; Palsson, Bernhard Ø

    2010-01-01

    Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

  3. A constraints-based approach to the acquisition of expertise in outdoor adventure sports

    NARCIS (Netherlands)

    Davids, Keith; Brymer, Eric; Seifert, Ludovic; Orth, Dominic

    2013-01-01

    A constraints-based framework enables a new understanding of expertise in outdoor adventure sports by considering performer-environment couplings through emergent and self-organizing behaviours in relation to interacting constraints. Expert adventure athletes, conceptualized as complex, dynamical

  4. Constraint-based workflow management systems : shifting control to users

    NARCIS (Netherlands)

    Pesic, M.

    2008-01-01

    Many organizations use information technology to support various aspects of their business processes: the operational aspect, collaboration between employees, etc. Workow management systems aim at supporting the operational aspect of complex business processes by using process models to automate the

  5. Constraint-based deadlock checking of high-level specifications

    DEFF Research Database (Denmark)

    Hallerstede, Stefan; Leuschel, Michael

    2011-01-01

    Establishing the absence of deadlocks is important in many applications of formal methods. The use of model checking for finding deadlocks in formal models is limited because in many industrial applications the state space is either infinite or much too large to be explored exhaustively. In this ......Establishing the absence of deadlocks is important in many applications of formal methods. The use of model checking for finding deadlocks in formal models is limited because in many industrial applications the state space is either infinite or much too large to be explored exhaustively......B's Prolog kernel, such as reification of membership and arithmetic constraints. ProB typically finds counter examples to deadlock-freedom constraints, a formula of about 900 partly nested conjunctions and disjunction among them 80 arithmetic and 150 set-theoretic predicates (in total a formula of 30 pages...

  6. Constraint-Based Abstract Semantics for Temporal Logic

    DEFF Research Database (Denmark)

    Banda, Gourinath; Gallagher, John Patrick

    2010-01-01

    Abstract interpretation provides a practical approach to verifying properties of infinite-state systems. We apply the framework of abstract interpretation to derive an abstract semantic function for the modal mu-calculus, which is the basis for abstract model checking. The abstract semantic funct...

  7. Rapid prototyping of microbial cell factories via genome-scale engineering.

    Science.gov (United States)

    Si, Tong; Xiao, Han; Zhao, Huimin

    2015-11-15

    Advances in reading, writing and editing genetic materials have greatly expanded our ability to reprogram biological systems at the resolution of a single nucleotide and on the scale of a whole genome. Such capacity has greatly accelerated the cycles of design, build and test to engineer microbes for efficient synthesis of fuels, chemicals and drugs. In this review, we summarize the emerging technologies that have been applied, or are potentially useful for genome-scale engineering in microbial systems. We will focus on the development of high-throughput methodologies, which may accelerate the prototyping of microbial cell factories. Copyright © 2014 Elsevier Inc. All rights reserved.

  8. Rapid Prototyping of Microbial Cell Factories via Genome-scale Engineering

    Science.gov (United States)

    Si, Tong; Xiao, Han; Zhao, Huimin

    2014-01-01

    Advances in reading, writing and editing genetic materials have greatly expanded our ability to reprogram biological systems at the resolution of a single nucleotide and on the scale of a whole genome. Such capacity has greatly accelerated the cycles of design, build and test to engineer microbes for efficient synthesis of fuels, chemicals and drugs. In this review, we summarize the emerging technologies that have been applied, or are potentially useful for genome-scale engineering in microbial systems. We will focus on the development of high-throughput methodologies, which may accelerate the prototyping of microbial cell factories. PMID:25450192

  9. Machine Translation Using Constraint-Based Synchronous Grammar

    Institute of Scientific and Technical Information of China (English)

    WONG Fai; DONG Mingchui; HU Dongcheng

    2006-01-01

    A synchronous grammar based on the formalism of context-free grammar was developed by generalizing the first component of production that models the source text. Unlike other synchronous grammars,the grammar allows multiple target productions to be associated to a single production rule which can be used to guide a parser to infer different possible translational equivalences for a recognized input string according to the feature constraints of symbols in the pattern. An extended generalized LR algorithm was adapted to the parsing of the proposed formalism to analyze the syntactic structure of a language. The grammar was used as the basis for building a machine translation system for Portuguese to Chinese translation. The empirical results show that the grammar is more expressive when modeling the translational equivalences of parallel texts for machine translation and grammar rewriting applications.

  10. A genome-scale RNA-interference screen identifies RRAS signaling as a pathologic feature of Huntington's disease.

    Directory of Open Access Journals (Sweden)

    John P Miller

    Full Text Available A genome-scale RNAi screen was performed in a mammalian cell-based assay to identify modifiers of mutant huntingtin toxicity. Ontology analysis of suppressor data identified processes previously implicated in Huntington's disease, including proteolysis, glutamate excitotoxicity, and mitochondrial dysfunction. In addition to established mechanisms, the screen identified multiple components of the RRAS signaling pathway as loss-of-function suppressors of mutant huntingtin toxicity in human and mouse cell models. Loss-of-function in orthologous RRAS pathway members also suppressed motor dysfunction in a Drosophila model of Huntington's disease. Abnormal activation of RRAS and a down-stream effector, RAF1, was observed in cellular models and a mouse model of Huntington's disease. We also observe co-localization of RRAS and mutant huntingtin in cells and in mouse striatum, suggesting that activation of R-Ras may occur through protein interaction. These data indicate that mutant huntingtin exerts a pathogenic effect on this pathway that can be corrected at multiple intervention points including RRAS, FNTA/B, PIN1, and PLK1. Consistent with these results, chemical inhibition of farnesyltransferase can also suppress mutant huntingtin toxicity. These data suggest that pharmacological inhibition of RRAS signaling may confer therapeutic benefit in Huntington's disease.

  11. CBDS: Constraint-based diagnostic system for malfunction identification in the nuclear power plant

    International Nuclear Information System (INIS)

    Ha, J.

    1992-01-01

    Traditional rule-based diagnostic expert systems use the experience of experts in the form of rules that associate symptoms with underlying faults. A commonly recognized failing of such systems is their narrow range of expertise and their inability to recognize problems outside this range of expertise. A model base diagnostic system isolating malfunctioning components-CBDS, the Constraint based Diagnostic System-has been developed. Since the intended behavior of a device is more predictable than unintended behaviors (faults), a model based system using the intended behavior has a potential to diagnose unexpected malfunctions by considering faults as open-quotes anything other than the intended behavior.close quotes As a knowledge base, the CBDS generates and decomposes a constraint network based on the structure and behavior model, which are represented symbolically in algebraic equations. Behaviors of generic components are organized in a component model library. Once the library is available, actual domain knowledge can be represented by declaring component types and their connections. To capture various plant knowledge, the mixed model was developed which allow the use of different parameter types in one equation by defining various operators. The CBDS uses the general idea of model based diagnosis. It detects a discrepancy between observation and prediction using constraint propagation, which carriers and accumulates the assumptions when parameter values are deduced. When measured plant parameters are asserted into a constraint network and are propagated through the network, a discrepancy will be detected if there exists any malfunctioning component. The CBDS was tested in the Recirculation Flow Control System of a BWR, and has been shown to be able to diagnose unexpected events

  12. 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.

  13. An Improved Constraint-Based System for the Verification of Security Protocols

    NARCIS (Netherlands)

    Corin, R.J.; Etalle, Sandro

    We propose a constraint-based system for the verification of security protocols that improves upon the one developed by Millen and Shmatikov [30]. Our system features (1) a significantly more efficient implementation, (2) a monotonic behavior, which also allows to detect flaws associated to partial

  14. ASPIRE: An Authoring System and Deployment Environment for Constraint-Based Tutors

    Science.gov (United States)

    Mitrovic, Antonija; Martin, Brent; Suraweera, Pramuditha; Zakharov, Konstantin; Milik, Nancy; Holland, Jay; McGuigan, Nicholas

    2009-01-01

    Over the last decade, the Intelligent Computer Tutoring Group (ICTG) has implemented many successful constraint-based Intelligent Tutoring Systems (ITSs) in a variety of instructional domains. Our tutors have proven their effectiveness not only in controlled lab studies but also in real classrooms, and some of them have been commercialized.…

  15. An Improved Constraint-based system for the verification of security protocols

    NARCIS (Netherlands)

    Corin, R.J.; Etalle, Sandro; Hermenegildo, Manuel V.; Puebla, German

    We propose a constraint-based system for the verification of security protocols that improves upon the one developed by Millen and Shmatikov. Our system features (1) a significantly more efficient implementation, (2) a monotonic behavior, which also allows to detect aws associated to partial runs

  16. Moving image analysis to the cloud: A case study with a genome-scale tomographic study

    Energy Technology Data Exchange (ETDEWEB)

    Mader, Kevin [4Quant Ltd., Switzerland & Institute for Biomedical Engineering at University and ETH Zurich (Switzerland); Stampanoni, Marco [Institute for Biomedical Engineering at University and ETH Zurich, Switzerland & Swiss Light Source at Paul Scherrer Institut, Villigen (Switzerland)

    2016-01-28

    Over the last decade, the time required to measure a terabyte of microscopic imaging data has gone from years to minutes. This shift has moved many of the challenges away from experimental design and measurement to scalable storage, organization, and analysis. As many scientists and scientific institutions lack training and competencies in these areas, major bottlenecks have arisen and led to substantial delays and gaps between measurement, understanding, and dissemination. We present in this paper a framework for analyzing large 3D datasets using cloud-based computational and storage resources. We demonstrate its applicability by showing the setup and costs associated with the analysis of a genome-scale study of bone microstructure. We then evaluate the relative advantages and disadvantages associated with local versus cloud infrastructures.

  17. Moving image analysis to the cloud: A case study with a genome-scale tomographic study

    International Nuclear Information System (INIS)

    Mader, Kevin; Stampanoni, Marco

    2016-01-01

    Over the last decade, the time required to measure a terabyte of microscopic imaging data has gone from years to minutes. This shift has moved many of the challenges away from experimental design and measurement to scalable storage, organization, and analysis. As many scientists and scientific institutions lack training and competencies in these areas, major bottlenecks have arisen and led to substantial delays and gaps between measurement, understanding, and dissemination. We present in this paper a framework for analyzing large 3D datasets using cloud-based computational and storage resources. We demonstrate its applicability by showing the setup and costs associated with the analysis of a genome-scale study of bone microstructure. We then evaluate the relative advantages and disadvantages associated with local versus cloud infrastructures

  18. Genome-scale analysis of positional clustering of mouse testis-specific genes

    Directory of Open Access Journals (Sweden)

    Lee Bernett TK

    2005-01-01

    Full Text Available Abstract Background Genes are not randomly distributed on a chromosome as they were thought even after removal of tandem repeats. The positional clustering of co-expressed genes is known in prokaryotes and recently reported in several eukaryotic organisms such as Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens. In order to further investigate the mode of tissue-specific gene clustering in higher eukaryotes, we have performed a genome-scale analysis of positional clustering of the mouse testis-specific genes. Results Our computational analysis shows that a large proportion of testis-specific genes are clustered in groups of 2 to 5 genes in the mouse genome. The number of clusters is much higher than expected by chance even after removal of tandem repeats. Conclusion Our result suggests that testis-specific genes tend to cluster on the mouse chromosomes. This provides another piece of evidence for the hypothesis that clusters of tissue-specific genes do exist.

  19. Targeted and genome-scale methylomics reveals gene body signatures in human cell lines

    Science.gov (United States)

    Ball, Madeleine Price; Li, Jin Billy; Gao, Yuan; Lee, Je-Hyuk; LeProust, Emily; Park, In-Hyun; Xie, Bin; Daley, George Q.; Church, George M.

    2012-01-01

    Cytosine methylation, an epigenetic modification of DNA, is a target of growing interest for developing high throughput profiling technologies. Here we introduce two new, complementary techniques for cytosine methylation profiling utilizing next generation sequencing technology: bisulfite padlock probes (BSPPs) and methyl sensitive cut counting (MSCC). In the first method, we designed a set of ~10,000 BSPPs distributed over the ENCODE pilot project regions to take advantage of existing expression and chromatin immunoprecipitation data. We observed a pattern of low promoter methylation coupled with high gene body methylation in highly expressed genes. Using the second method, MSCC, we gathered genome-scale data for 1.4 million HpaII sites and confirmed that gene body methylation in highly expressed genes is a consistent phenomenon over the entire genome. Our observations highlight the usefulness of techniques which are not inherently or intentionally biased in favor of only profiling particular subsets like CpG islands or promoter regions. PMID:19329998

  20. The architecture of ArgR-DNA complexes at the genome-scale in Escherichia coli

    DEFF Research Database (Denmark)

    Cho, Suhyung; Cho, Yoo-Bok; Kang, Taek Jin

    2015-01-01

    DNA-binding motifs that are recognized by transcription factors (TFs) have been well studied; however, challenges remain in determining the in vivo architecture of TF-DNA complexes on a genome-scale. Here, we determined the in vivo architecture of Escherichia coli arginine repressor (ArgR)-DNA co...

  1. Dynamic Demand-Capacity Balancing for Air Traffic Management Using Constraint-Based Local Search: First Results

    Directory of Open Access Journals (Sweden)

    Farshid Hassani Bijarbooneh

    2009-10-01

    Full Text Available Using constraint-based local search, we effectively model and efficiently solve the problem of balancing the traffic demands on portions of the European airspace while ensuring that their capacity constraints are satisfied. The traffic demand of a portion of airspace is the hourly number of flights planned to enter it, and its capacity is the upper bound on this number under which air-traffic controllers can work. Currently, the only form of demand-capacity balancing we allow is ground holding, that is the changing of the take-off times of not yet airborne flights. Experiments with projected European flight plans of the year 2030 show that already this first form of demand-capacity balancing is feasible without incurring too much total delay and that it can lead to a significantly better demand-capacity balance.

  2. Research in constraint-based layout, visualization, CAD, and related topics : a bibliographical survey

    OpenAIRE

    Hower, Walter; Graf, Winfried H.

    1995-01-01

    The present work compiles numerous papers in the area of computer-aided design, graphics, layout configuration, and user interfaces in general. There is nearly no conference on graphics, multimedia, and user interfaces that does not include a section on constraint-based graphics; on the other hand most conferences on constraint processing favour applications in graphics. This work of bibliographical pointers may serve as a basis for a detailed and comprehensive survey of this important and ch...

  3. Identifying all moiety conservation laws in genome-scale metabolic networks.

    Science.gov (United States)

    De Martino, Andrea; De Martino, Daniele; Mulet, Roberto; Pagnani, Andrea

    2014-01-01

    The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

  4. Identifying all moiety conservation laws in genome-scale metabolic networks.

    Directory of Open Access Journals (Sweden)

    Andrea De Martino

    Full Text Available The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

  5. 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.

  6. Data partitioning enables the use of standard SOAP Web Services in genome-scale workflows.

    Science.gov (United States)

    Sztromwasser, Pawel; Puntervoll, Pål; Petersen, Kjell

    2011-07-26

    Biological databases and computational biology tools are provided by research groups around the world, and made accessible on the Web. Combining these resources is a common practice in bioinformatics, but integration of heterogeneous and often distributed tools and datasets can be challenging. To date, this challenge has been commonly addressed in a pragmatic way, by tedious and error-prone scripting. Recently however a more reliable technique has been identified and proposed as the platform that would tie together bioinformatics resources, namely Web Services. In the last decade the Web Services have spread wide in bioinformatics, and earned the title of recommended technology. However, in the era of high-throughput experimentation, a major concern regarding Web Services is their ability to handle large-scale data traffic. We propose a stream-like communication pattern for standard SOAP Web Services, that enables efficient flow of large data traffic between a workflow orchestrator and Web Services. We evaluated the data-partitioning strategy by comparing it with typical communication patterns on an example pipeline for genomic sequence annotation. The results show that data-partitioning lowers resource demands of services and increases their throughput, which in consequence allows to execute in-silico experiments on genome-scale, using standard SOAP Web Services and workflows. As a proof-of-principle we annotated an RNA-seq dataset using a plain BPEL workflow engine.

  7. Data partitioning enables the use of standard SOAP Web Services in genome-scale workflows

    Directory of Open Access Journals (Sweden)

    Sztromwasser Paweł

    2011-06-01

    Full Text Available Biological databases and computational biology tools are provided by research groups around the world, and made accessible on the Web. Combining these resources is a common practice in bioinformatics, but integration of heterogeneous and often distributed tools and datasets can be challenging. To date, this challenge has been commonly addressed in a pragmatic way, by tedious and error-prone scripting. Recently however a more reliable technique has been identified and proposed as the platform that would tie together bioinformatics resources, namely Web Services. In the last decade the Web Services have spread wide in bioinformatics, and earned the title of recommended technology. However, in the era of high-throughput experimentation, a major concern regarding Web Services is their ability to handle large-scale data traffic. We propose a stream-like communication pattern for standard SOAP Web Services, that enables efficient flow of large data traffic between a workflow orchestrator and Web Services. We evaluated the data-partitioning strategy by comparing it with typical communication patterns on an example pipeline for genomic sequence annotation. The results show that data-partitioning lowers resource demands of services and increases their throughput, which in consequence allows to execute in-silico experiments on genome-scale, using standard SOAP Web Services and workflows. As a proof-of-principle we annotated an RNA-seq dataset using a plain BPEL workflow engine.

  8. 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.

  9. Survey of protein–DNA interactions in Aspergillus oryzae on a genomic scale

    Science.gov (United States)

    Wang, Chao; Lv, Yangyong; Wang, Bin; Yin, Chao; Lin, Ying; Pan, Li

    2015-01-01

    The genome-scale delineation of in vivo protein–DNA interactions is key to understanding genome function. Only ∼5% of transcription factors (TFs) in the Aspergillus genus have been identified using traditional methods. Although the Aspergillus oryzae genome contains >600 TFs, knowledge of the in vivo genome-wide TF-binding sites (TFBSs) in aspergilli remains limited because of the lack of high-quality antibodies. We investigated the landscape of in vivo protein–DNA interactions across the A. oryzae genome through coupling the DNase I digestion of intact nuclei with massively parallel sequencing and the analysis of cleavage patterns in protein–DNA interactions at single-nucleotide resolution. The resulting map identified overrepresented de novo TF-binding motifs from genomic footprints, and provided the detailed chromatin remodeling patterns and the distribution of digital footprints near transcription start sites. The TFBSs of 19 known Aspergillus TFs were also identified based on DNase I digestion data surrounding potential binding sites in conjunction with TF binding specificity information. We observed that the cleavage patterns of TFBSs were dependent on the orientation of TF motifs and independent of strand orientation, consistent with the DNA shape features of binding motifs with flanking sequences. PMID:25883143

  10. GMATA: An Integrated Software Package for Genome-Scale SSR Mining, Marker Development and Viewing.

    Science.gov (United States)

    Wang, Xuewen; Wang, Le

    2016-01-01

    Simple sequence repeats (SSRs), also referred to as microsatellites, are highly variable tandem DNAs that are widely used as genetic markers. The increasing availability of whole-genome and transcript sequences provides information resources for SSR marker development. However, efficient software is required to efficiently identify and display SSR information along with other gene features at a genome scale. We developed novel software package Genome-wide Microsatellite Analyzing Tool Package (GMATA) integrating SSR mining, statistical analysis and plotting, marker design, polymorphism screening and marker transferability, and enabled simultaneously display SSR markers with other genome features. GMATA applies novel strategies for SSR analysis and primer design in large genomes, which allows GMATA to perform faster calculation and provides more accurate results than existing tools. Our package is also capable of processing DNA sequences of any size on a standard computer. GMATA is user friendly, only requires mouse clicks or types inputs on the command line, and is executable in multiple computing platforms. We demonstrated the application of GMATA in plants genomes and reveal a novel distribution pattern of SSRs in 15 grass genomes. The most abundant motifs are dimer GA/TC, the A/T monomer and the GCG/CGC trimer, rather than the rich G/C content in DNA sequence. We also revealed that SSR count is a linear to the chromosome length in fully assembled grass genomes. GMATA represents a powerful application tool that facilitates genomic sequence analyses. GAMTA is freely available at http://sourceforge.net/projects/gmata/?source=navbar.

  11. Construction and analysis of the model of energy metabolism in E. coli.

    Directory of Open Access Journals (Sweden)

    Zixiang Xu

    Full Text Available Genome-scale models of metabolism have only been analyzed with the constraint-based modelling philosophy and there have been several genome-scale gene-protein-reaction models. But research on the modelling for energy metabolism of organisms just began in recent years and research on metabolic weighted complex network are rare in literature. We have made three research based on the complete model of E. coli's energy metabolism. We first constructed a metabolic weighted network using the rates of free energy consumption within metabolic reactions as the weights. We then analyzed some structural characters of the metabolic weighted network that we constructed. We found that the distribution of the weight values was uneven, that most of the weight values were zero while reactions with abstract large weight values were rare and that the relationship between w (weight values and v (flux values was not of linear correlation. At last, we have done some research on the equilibrium of free energy for the energy metabolism system of E. coli. We found that E(out (free energy rate input from the environment can meet the demand of E(ch(in (free energy rate dissipated by chemical process and that chemical process plays a great role in the dissipation of free energy in cells. By these research and to a certain extend, we can understand more about the energy metabolism of E. coli.

  12. The Systems Biology Markup Language (SBML) Level 3 Package: Flux Balance Constraints.

    NARCIS (Netherlands)

    Olivier, B.G.; Bergmann, F.T.

    2015-01-01

    Constraint-based modeling is a well established modelling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size, genome scale models are typically analysed using constraint-based optimization techniques. One widely used method is Flux

  13. Presentation : Development of an age-specific genome-scale model of skeletal muscle metabolism

    NARCIS (Netherlands)

    Cabbia, A.; van Riel, N.A.W.

    2017-01-01

    Skeletal myocytes are among the most metabolically active cell types, implicated in nutrient balance, contributing to the insulin-stimulated clearance of glucose from the blood, and secreting myokines that contribute in regulating inflammation and the ageing process. The loss of muscle mass and

  14. Toward genome-scale models of the Chinese hamster ovary cells: incentives, status and perspectives

    DEFF Research Database (Denmark)

    Kaas, Christian Schrøder; Fan, Yuzhou; Weilguny, Dietmar

    2014-01-01

    Bioprocessing of the important Chinese hamster ovary (CHO) cell lines used for the production of biopharmaceuticals stands at the brink of several redefining events. In 2011, the field entered the genomics era, which has accelerated omics-based phenotyping of the cell lines. In this review we...

  15. Flux balance analysis of genome-scale metabolic model of rice ...

    Indian Academy of Sciences (India)

    2015-09-28

    Sep 28, 2015 ... biologists are also trying to understand the plant's systems level biochemistry ... metabolism to observe the effect of intracellular transporters' transport ..... [The information about this pathway and associated genes in .... 2013 A method for accounting for mainte- ... Biological control of rice diseases pp 1–11.

  16. Analysis of genetic variation and potential applications in genome-scale metabolic modeling

    DEFF Research Database (Denmark)

    Cardoso, Joao; Andersen, Mikael Rørdam; Herrgard, Markus

    2015-01-01

    scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function......Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology......, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic...

  17. Genome-scale model of Streptococcus thermophilus LMG18311 for metabolic comparison.

    NARCIS (Netherlands)

    Pastink, M.I.; Teusink, B.; Hols, P.; Visser, S.; Vos, W.M.; Hugenholtz, J.

    2009-01-01

    In this report, we describe the amino acid metabolism and amino acid dependency of the dairy bacterium Streptococcus thermophilus LMG18311 and compare them with those of two other characterized lactic acid bacteria, Lactococcus lactis and Lactobacillus plantarum. Through the construction of a

  18. HOROPLAN: computer-assisted nurse scheduling using constraint-based programming.

    Science.gov (United States)

    Darmoni, S J; Fajner, A; Mahé, N; Leforestier, A; Vondracek, M; Stelian, O; Baldenweck, M

    1995-01-01

    Nurse scheduling is a difficult and time consuming task. The schedule has to determine the day to day shift assignments of each nurse for a specified period of time in a way that satisfies the given requirements as much as possible, taking into account the wishes of nurses as closely as possible. This paper presents a constraint-based, artificial intelligence approach by describing a prototype implementation developed with the Charme language and the first results of its use in the Rouen University Hospital. Horoplan implements a non-cyclical constraint-based scheduling, using some heuristics. Four levels of constraints were defined to give a maximum of flexibility: French level (e.g. number of worked hours in a year), hospital level (e.g. specific day-off), department level (e.g. specific shift) and care unit level (e.g. specific pattern for week-ends). Some constraints must always be verified and can not be overruled and some constraints can be overruled at a certain cost. Rescheduling is possible at any time specially in case of an unscheduled absence.

  19. LocateP: Genome-scale subcellular-location predictor for bacterial proteins

    Directory of Open Access Journals (Sweden)

    Zhou Miaomiao

    2008-03-01

    Full Text Available Abstract Background In the past decades, various protein subcellular-location (SCL predictors have been developed. Most of these predictors, like TMHMM 2.0, SignalP 3.0, PrediSi and Phobius, aim at the identification of one or a few SCLs, whereas others such as CELLO and Psortb.v.2.0 aim at a broader classification. Although these tools and pipelines can achieve a high precision in the accurate prediction of signal peptides and transmembrane helices, they have a much lower accuracy when other sequence characteristics are concerned. For instance, it proved notoriously difficult to identify the fate of proteins carrying a putative type I signal peptidase (SPIase cleavage site, as many of those proteins are retained in the cell membrane as N-terminally anchored membrane proteins. Moreover, most of the SCL classifiers are based on the classification of the Swiss-Prot database and consequently inherited the inconsistency of that SCL classification. As accurate and detailed SCL prediction on a genome scale is highly desired by experimental researchers, we decided to construct a new SCL prediction pipeline: LocateP. Results LocateP combines many of the existing high-precision SCL identifiers with our own newly developed identifiers for specific SCLs. The LocateP pipeline was designed such that it mimics protein targeting and secretion processes. It distinguishes 7 different SCLs within Gram-positive bacteria: intracellular, multi-transmembrane, N-terminally membrane anchored, C-terminally membrane anchored, lipid-anchored, LPxTG-type cell-wall anchored, and secreted/released proteins. Moreover, it distinguishes pathways for Sec- or Tat-dependent secretion and alternative secretion of bacteriocin-like proteins. The pipeline was tested on data sets extracted from literature, including experimental proteomics studies. The tests showed that LocateP performs as well as, or even slightly better than other SCL predictors for some locations and outperforms

  20. Expression induction of P450 genes by imidacloprid in Nilaparvata lugens: A genome-scale analysis.

    Science.gov (United States)

    Zhang, Jianhua; Zhang, Yixi; Wang, Yunchao; Yang, Yuanxue; Cang, Xinzhu; Liu, Zewen

    2016-09-01

    The overexpression of P450 monooxygenase genes is a main mechanism for the resistance to imidacloprid, a representative neonicotinoid insecticide, in Nilaparvata lugens (brown planthopper, BPH). However, only two P450 genes (CYP6AY1 and CYP6ER1), among fifty-four P450 genes identified from BPH genome database, have been reported to play important roles in imidacloprid resistance until now. In this study, after the confirmation of important roles of P450s in imidacloprid resistance by the synergism analysis, the expression induction by imidacloprid was determined for all P450 genes. In the susceptible (Sus) strain, eight P450 genes in Clade4, eight in Clade3 and two in Clade2 were up-regulated by imidacloprid, among which three genes (CYP6CS1, CYP6CW1 and CYP6ER1, all in Clade3) were increased to above 4.0-fold and eight genes to above 2.0-fold. In contrast, no P450 genes were induced in Mito clade. Eight genes induced to above 2.0-fold were selected to determine their expression and induced levels in Huzhou population, in which piperonyl butoxide showed the biggest effects on imidacloprid toxicity among eight field populations. The expression levels of seven P450 genes were higher in Huzhou population than that in Sus strain, with the biggest differences for CYP6CS1 (9.8-fold), CYP6ER1 (7.7-fold) and CYP6AY1 (5.1-fold). The induction levels for all tested genes were bigger in Sus strain than that in Huzhou population except CYP425B1. Screening the induction of P450 genes by imidacloprid in the genome-scale will provide an overall view on the possible metabolic factors in the resistance to neonicotinoid insecticides. The further work, such as the functional study of recombinant proteins, will be performed to validate the roles of these P450s in imidacloprid resistance. Copyright © 2015 Elsevier B.V. All rights reserved.

  1. RegPrecise 3.0--a resource for genome-scale exploration of transcriptional regulation in bacteria.

    Science.gov (United States)

    Novichkov, Pavel S; Kazakov, Alexey E; Ravcheev, Dmitry A; Leyn, Semen A; Kovaleva, Galina Y; Sutormin, Roman A; Kazanov, Marat D; Riehl, William; Arkin, Adam P; Dubchak, Inna; Rodionov, Dmitry A

    2013-11-01

    Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in prokaryotes is one of the critical tasks of modern genomics. Bacteria from different taxonomic groups, whose lifestyles and natural environments are substantially different, possess highly diverged transcriptional regulatory networks. The comparative genomics approaches are useful for in silico reconstruction of bacterial regulons and networks operated by both transcription factors (TFs) and RNA regulatory elements (riboswitches). RegPrecise (http://regprecise.lbl.gov) is a web resource for collection, visualization and analysis of transcriptional regulons reconstructed by comparative genomics. We significantly expanded a reference collection of manually curated regulons we introduced earlier. RegPrecise 3.0 provides access to inferred regulatory interactions organized by phylogenetic, structural and functional properties. Taxonomy-specific collections include 781 TF regulogs inferred in more than 160 genomes representing 14 taxonomic groups of Bacteria. TF-specific collections include regulogs for a selected subset of 40 TFs reconstructed across more than 30 taxonomic lineages. Novel collections of regulons operated by RNA regulatory elements (riboswitches) include near 400 regulogs inferred in 24 bacterial lineages. RegPrecise 3.0 provides four classifications of the reference regulons implemented as controlled vocabularies: 55 TF protein families; 43 RNA motif families; ~150 biological processes or metabolic pathways; and ~200 effectors or environmental signals. Genome-wide visualization of regulatory networks and metabolic pathways covered by the reference regulons are available for all studied genomes. A separate section of RegPrecise 3.0 contains draft regulatory networks in 640 genomes obtained by an conservative propagation of the reference regulons to closely related genomes. RegPrecise 3.0 gives access to the transcriptional regulons reconstructed in

  2. Microarray analysis of serum mRNA in patients with head and neck squamous cell carcinoma at whole-genome scale

    Czech Academy of Sciences Publication Activity Database

    Čapková, M.; Šáchová, Jana; Strnad, Hynek; Kolář, Michal; Hroudová, Miluše; Chovanec, M.; Čada, Z.; Štefl, M.; Valach, J.; Kastner, J.; Smetana, K. Jr.; Plzák, J.

    -, April 23 (2014) ISSN 2314-6141 R&D Projects: GA MZd(CZ) NT13488 Institutional support: RVO:68378050 Keywords : Microarray Analysis * Head and Neck Squamous Cell Carcinoma * whole-genome scale Subject RIV: EB - Genetics ; Molecular Biology

  3. Stoichiometric Representation of Gene–Protein–Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction

    DEFF Research Database (Denmark)

    Machado, Daniel; Herrgard, Markus; Rocha, Isabel

    2016-01-01

    only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene...... design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions...

  4. A constraint-based approach to intelligent support of nuclear reactor design

    International Nuclear Information System (INIS)

    Furuta, Kazuo

    1993-01-01

    Constraint is a powerful representation to formulate and solve problems in design; a constraint-based approach to intelligent support of nuclear reactor design is proposed. We first discuss the features of the approach, and then present the architecture of a nuclear reactor design support system under development. In this design support system, the knowledge base contains constraints useful to structure the design space as object class definitions, and several types of constraint resolvers are provided as design support subsystems. The adopted method of constraint resolution are explained in detail. The usefulness of the approach is demonstrated using two design problems: Design window search and multiobjective optimization in nuclear reactor design. (orig./HP)

  5. The roles of constraint-based and dedication-based influences on user's continued online shopping behavior.

    Science.gov (United States)

    Chang, Su-Chao; Chou, Chi-Min

    2012-11-01

    The objective of this study was to determine empirically the role of constraint-based and dedication-based influences as drivers of the intention to continue using online shopping websites. Constraint-based influences consist of two variables: trust and perceived switching costs. Dedication-based influences consist of three variables: satisfaction, perceived usefulness, and trust. The current results indicate that both constraint-based and dedication-based influences are important drivers of the intention to continue using online shopping websites. The data also shows that trust has the strongest total effect on online shoppers' intention to continue using online shopping websites. In addition, the results indicate that the antecedents of constraint-based influences, technical bonds (e.g., perceived operational competence and perceived website interactivity) and social bonds (e.g., perceived relationship investment, community building, and intimacy) have indirect positive effects on the intention to continue using online shopping websites. Based on these findings, this research suggests that online shopping websites should build constraint-based and dedication-based influences to enhance user's continued online shopping behaviors simultaneously.

  6. Predicting the accumulation of storage compounds by Rhodococcus jostii RHA1 in the feast-famine growth cycles using genome-scale flux balance analysis.

    Science.gov (United States)

    Tajparast, Mohammad; Frigon, Dominic

    2018-01-01

    Feast-famine cycles in biological wastewater resource recovery systems select for bacterial species that accumulate intracellular storage compounds such as poly-β-hydroxybutyrate (PHB), glycogen, and triacylglycerols (TAG). These species survive better the famine phase and resume rapid substrate uptake at the beginning of the feast phase faster than microorganisms unable to accumulate storage. However, ecophysiological conditions favouring the accumulation of either storage compounds remain to be clarified, and predictive capabilities need to be developed to eventually rationally design reactors producing these compounds. Using a genome-scale metabolic modelling approach, the storage metabolism of Rhodococcus jostii RHA1 was investigated for steady-state feast-famine cycles on glucose and acetate as the sole carbon sources. R. jostii RHA1 is capable of accumulating the three storage compounds (PHB, TAG, and glycogen) simultaneously. According to the experimental observations, when glucose was the substrate, feast phase chemical oxygen demand (COD) accumulation was similar for the three storage compounds; when acetate was the substrate, however, PHB accumulation was 3 times higher than TAG accumulation and essentially no glycogen was accumulated. These results were simulated using the genome-scale metabolic model of R. jostii RHA1 (iMT1174) by means of flux balance analysis (FBA) to determine the objective functions capable of predicting these behaviours. Maximization of the growth rate was set as the main objective function, while minimization of total reaction fluxes and minimization of metabolic adjustment (environmental MOMA) were considered as the sub-objective functions. The environmental MOMA sub-objective performed better than the minimization of total reaction fluxes sub-objective function at predicting the mixture of storage compounds accumulated. Additional experiments with 13C-labelled bicarbonate (HCO3-) found that the fluxes through the central

  7. Genome-scale portrait and evolutionary significance of human-specific core promoter tri- and tetranucleotide short tandem repeats.

    Science.gov (United States)

    Nazaripanah, N; Adelirad, F; Delbari, A; Sahaf, R; Abbasi-Asl, T; Ohadi, M

    2018-04-05

    While there is an ongoing trend to identify single nucleotide substitutions (SNSs) that are linked to inter/intra-species differences and disease phenotypes, short tandem repeats (STRs)/microsatellites may be of equal (if not more) importance in the above processes. Genes that contain STRs in their promoters have higher expression divergence compared to genes with fixed or no STRs in the gene promoters. In line with the above, recent reports indicate a role of repetitive sequences in the rise of young transcription start sites (TSSs) in human evolution. Following a comparative genomics study of all human protein-coding genes annotated in the GeneCards database, here we provide a genome-scale portrait of human-specific short- and medium-size (≥ 3-repeats) tri- and tetranucleotide STRs and STR motifs in the critical core promoter region between - 120 and + 1 to the TSS and evidence of skewing of this compartment in reference to the STRs that are not human-specific (Levene's test p human-specific transcripts was detected in the tri and tetra human-specific compartments (mid-p genome-scale skewing of STRs at a specific region of the human genome and a link between a number of these STRs and TSS selection/transcript specificity. The STRs and genes listed here may have a role in the evolution and development of characteristics and phenotypes that are unique to the human species.

  8. 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.

  9. Constraint-based component-modeling for knowledge-based design

    Science.gov (United States)

    Kolb, Mark A.

    1992-01-01

    The paper describes the application of various advanced programming techniques derived from artificial intelligence research to the development of flexible design tools for conceptual design. Special attention is given to two techniques which appear to be readily applicable to such design tools: the constraint propagation technique and the object-oriented programming. The implementation of these techniques in a prototype computer tool, Rubber Airplane, is described.

  10. A Constraint-Based Model for Fast Post-Disaster Emergency Vehicle Routing

    Directory of Open Access Journals (Sweden)

    Roberto Amadini

    2013-12-01

    Full Text Available Disasters like terrorist attacks, earthquakes, hurricanes, and volcano eruptions are usually unpredictable events that affect a high number of people. We propose an approach that could be used as a decision support tool for a post-disaster response that allows the assignment of victims to hospitals and organizes their transportation via emergency vehicles. By exploiting the synergy between Mixed Integer Programming and Constraint Programming techniques, we are able to compute the routing of the vehicles so as to rescue much more victims than both heuristic based and complete approaches in a very reasonable time.

  11. Experience Report: Constraint-Based Modelling and Simulation of Railway Emergency Response Plans

    DEFF Research Database (Denmark)

    Debois, Søren; Hildebrandt, Thomas; Sandberg, Lene

    2016-01-01

    ways to proceed, including ways not necessarily anticipated in the paper-based emergency response plans. The case study was undertaken as part of a short research, ProSec, project funded by the Danish Defence Agency, with the aim of applying and developing methods for collaborative mapping of emergency...

  12. Vocalist - an international programme for the validation of constraint based methodology in structural integrity

    International Nuclear Information System (INIS)

    Lidbury, D.; Bass, R.; Gilles, Ph.; Connors, D.; Eisele, U.; Keim, E.; Keinanen, H.; Marie, St.; Nagel, G.; Taylor, N.; Wadier, Y.

    2001-01-01

    The pattern of crack-tip stresses and strains causing plastic flow and fracture in components is different to that in test specimens. This gives rise to the so-called constraint effect. Crack-tip constraint in components is generally lower than in test specimens. Effective toughness is correspondingly higher. The fracture toughness measured on test specimens is thus likely to underestimate that exhibited by cracks in components. A 36-month programme was initiated in October 2000 as part of the Fifth Framework of the European Atomic Energy Community (EURATOM), with the objective of achieving (i) an improved defect assessment methodology for predicting safety margins; (ii) improved lifetime management arguments. The programme VOCALIST (Validation of Constraint Based Methodology in Structural Integrity) is one of a 'cluster' of Fifth Framework projects in the area of Plant Life Management (Nuclear Fission). VOCALIST is also an associated project of NESC (Network for Evaluating Steel Components). The present paper describes the aims and objectives of VOCALIST, its interactions with NESC, and gives details of its various Work Packages. (authors)

  13. Vocalist - an international programme for the validation of constraint based methodology in structural integrity

    Energy Technology Data Exchange (ETDEWEB)

    Lidbury, D. [AEA Technology, Consulting (United Kingdom); Bass, R. [Oak Ridge National Lab., TN (United States); Gilles, Ph. [FRAMATOME, 92 - Paris-La-Defence (France); Connors, D. [BNFL Magnox Generation (United Kingdom); Eisele, U. [Multiphoton Absorption, MPA, Stuttgart (Germany); Keim, E. [Framatome ANP GmbH (Germany); Keinanen, H. [VTT Energy, Espoo (Finland); Marie, St. [CEA Saclay, Dept. de Mecanique et de Technologie, 91 - Gif sur Yvette (France); Nagel, G. [E.ON Kernraft (Germany); Taylor, N. [Joint Research Center, JRC-IAM (Netherlands); Wadier, Y. [Electricite de France (EDF), 93 - Saint-Denis (France). Dept. de Radioprotection

    2001-07-01

    The pattern of crack-tip stresses and strains causing plastic flow and fracture in components is different to that in test specimens. This gives rise to the so-called constraint effect. Crack-tip constraint in components is generally lower than in test specimens. Effective toughness is correspondingly higher. The fracture toughness measured on test specimens is thus likely to underestimate that exhibited by cracks in components. A 36-month programme was initiated in October 2000 as part of the Fifth Framework of the European Atomic Energy Community (EURATOM), with the objective of achieving (i) an improved defect assessment methodology for predicting safety margins; (ii) improved lifetime management arguments. The programme VOCALIST (Validation of Constraint Based Methodology in Structural Integrity) is one of a 'cluster' of Fifth Framework projects in the area of Plant Life Management (Nuclear Fission). VOCALIST is also an associated project of NESC (Network for Evaluating Steel Components). The present paper describes the aims and objectives of VOCALIST, its interactions with NESC, and gives details of its various Work Packages. (authors)

  14. Constraint based scheduling for the Goddard Space Flight Center distributed Active Archive Center's data archive and distribution system

    Science.gov (United States)

    Short, Nick, Jr.; Bedet, Jean-Jacques; Bodden, Lee; Boddy, Mark; White, Jim; Beane, John

    1994-01-01

    The Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) has been operational since October 1, 1993. Its mission is to support the Earth Observing System (EOS) by providing rapid access to EOS data and analysis products, and to test Earth Observing System Data and Information System (EOSDIS) design concepts. One of the challenges is to ensure quick and easy retrieval of any data archived within the DAAC's Data Archive and Distributed System (DADS). Over the 15-year life of EOS project, an estimated several Petabytes (10(exp 15)) of data will be permanently stored. Accessing that amount of information is a formidable task that will require innovative approaches. As a precursor of the full EOS system, the GSFC DAAC with a few Terabits of storage, has implemented a prototype of a constraint-based task and resource scheduler to improve the performance of the DADS. This Honeywell Task and Resource Scheduler (HTRS), developed by Honeywell Technology Center in cooperation the Information Science and Technology Branch/935, the Code X Operations Technology Program, and the GSFC DAAC, makes better use of limited resources, prevents backlog of data, provides information about resources bottlenecks and performance characteristics. The prototype which is developed concurrently with the GSFC Version 0 (V0) DADS, models DADS activities such as ingestion and distribution with priority, precedence, resource requirements (disk and network bandwidth) and temporal constraints. HTRS supports schedule updates, insertions, and retrieval of task information via an Application Program Interface (API). The prototype has demonstrated with a few examples, the substantial advantages of using HTRS over scheduling algorithms such as a First In First Out (FIFO) queue. The kernel scheduling engine for HTRS, called Kronos, has been successfully applied to several other domains such as space shuttle mission scheduling, demand flow manufacturing, and avionics communications

  15. 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.

  16. Determining the Control Circuitry of Redox Metabolism at the Genome-Scale

    DEFF Research Database (Denmark)

    Federowicz, Stephen; Kim, Donghyuk; Ebrahim, Ali

    2014-01-01

    -scale metabolic model to show that ArcA and Fnr regulate >80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions. Based on the data, we propose a feedforward with feedback trim regulatory scheme, given the extensive repression of catabolic genes...

  17. HyDe: a Python Package for Genome-Scale Hybridization Detection.

    Science.gov (United States)

    Blischak, Paul D; Chifman, Julia; Wolfe, Andrea D; Kubatko, Laura S

    2018-03-19

    The analysis of hybridization and gene flow among closely related taxa is a common goal for researchers studying speciation and phylogeography. Many methods for hybridization detection use simple site pattern frequencies from observed genomic data and compare them to null models that predict an absence of gene flow. The theory underlying the detection of hybridization using these site pattern probabilities exploits the relationship between the coalescent process for gene trees within population trees and the process of mutation along the branches of the gene trees. For certain models, site patterns are predicted to occur in equal frequency (i.e., their difference is 0), producing a set of functions called phylogenetic invariants. In this paper we introduce HyDe, a software package for detecting hybridization using phylogenetic invariants arising under the coalescent model with hybridization. HyDe is written in Python, and can be used interactively or through the command line using pre-packaged scripts. We demonstrate the use of HyDe on simulated data, as well as on two empirical data sets from the literature. We focus in particular on identifying individual hybrids within population samples and on distinguishing between hybrid speciation and gene flow. HyDe is freely available as an open source Python package under the GNU GPL v3 on both GitHub (https://github.com/pblischak/HyDe) and the Python Package Index (PyPI: https://pypi.python.org/pypi/phyde).

  18. Genome-scale reconstruction of the sigma factor network in Escherichia coli: topology and functional states

    DEFF Research Database (Denmark)

    Cho, Byung-Kwan; Kim, Donghyuk; Knight, Eric M.

    2014-01-01

    Background: At the beginning of the transcription process, the RNA polymerase (RNAP) core enzyme requires a sigma-factor to recognize the genomic location at which the process initiates. Although the crucial role of sigma-factors has long been appreciated and characterized for many individual...... to transcription units (TUs), representing an increase of more than 300% over what has been previously reported. The reconstructed network was used to investigate competition between alternative sigma-factors (the sigma(70) and sigma(38) regulons), confirming the competition model of sigma substitution...

  19. Multiple Constraints Based Robust Matching of Poor-Texture Close-Range Images for Monitoring a Simulated Landslide

    Directory of Open Access Journals (Sweden)

    Gang Qiao

    2016-05-01

    Full Text Available Landslides are one of the most destructive geo-hazards that can bring about great threats to both human lives and infrastructures. Landslide monitoring has been always a research hotspot. In particular, landslide simulation experimentation is an effective tool in landslide research to obtain critical parameters that help understand the mechanism and evaluate the triggering and controlling factors of slope failure. Compared with other traditional geotechnical monitoring approaches, the close-range photogrammetry technique shows potential in tracking and recording the 3D surface deformation and failure processes. In such cases, image matching usually plays a critical role in stereo image processing for the 3D geometric reconstruction. However, the complex imaging conditions such as rainfall, mass movement, illumination, and ponding will reduce the texture quality of the stereo images, bringing about difficulties in the image matching process and resulting in very sparse matches. To address this problem, this paper presents a multiple-constraints based robust image matching approach for poor-texture close-range images particularly useful in monitoring a simulated landslide. The Scale Invariant Feature Transform (SIFT algorithm was first applied to the stereo images for generation of scale-invariate feature points, followed by a two-step matching process: feature-based image matching and area-based image matching. In the first feature-based matching step, the triangulation process was performed based on the SIFT matches filtered by the Fundamental Matrix (FM and a robust checking procedure, to serve as the basic constraints for feature-based iterated matching of all the non-matched SIFT-derived feature points inside each triangle. In the following area-based image-matching step, the corresponding points of the non-matched features in each triangle of the master image were predicted in the homologous triangle of the searching image by using geometric

  20. Genome-scale analysis of the high-efficient protein secretion system of Aspergillus oryzae

    DEFF Research Database (Denmark)

    Liu, Lifang; Feizi, Amir; Osterlund, Tobias

    2014-01-01

    related fungal species such as Aspergillus nidulans and Aspergillus niger. To evaluate the defined component list, we performed transcriptome analysis on three a-amylase over-producing strains with varying levels of secretion capacities. Specifically, secretory components involved in the ER......Background: The koji mold, Aspergillus oryzae is widely used for the production of industrial enzymes due to its particularly high protein secretion capacity and ability to perform post-translational modifications. However, systemic analysis of its secretion system is lacking, generally due...... to the poorly annotated proteome. Results: Here we defined a functional protein secretory component list of A. oryzae using a previously reported secretory model of S. cerevisiae as scaffold. Additional secretory components were obtained by blast search with the functional components reported in other closely...

  1. Linear time algorithms to construct populations fitting multiple constraint distributions at genomic scales.

    Science.gov (United States)

    Siragusa, Enrico; Haiminen, Niina; Utro, Filippo; Parida, Laxmi

    2017-10-09

    Computer simulations can be used to study population genetic methods, models and parameters, as well as to predict potential outcomes. For example, in plant populations, predicting the outcome of breeding operations can be studied using simulations. In-silico construction of populations with pre-specified characteristics is an important task in breeding optimization and other population genetic studies. We present two linear time Simulation using Best-fit Algorithms (SimBA) for two classes of problems where each co-fits two distributions: SimBA-LD fits linkage disequilibrium and minimum allele frequency distributions, while SimBA-hap fits founder-haplotype and polyploid allele dosage distributions. An incremental gap-filling version of previously introduced SimBA-LD is here demonstrated to accurately fit the target distributions, allowing efficient large scale simulations. SimBA-hap accuracy and efficiency is demonstrated by simulating tetraploid populations with varying numbers of founder haplotypes, we evaluate both a linear time greedy algoritm and an optimal solution based on mixed-integer programming. SimBA is available on http://researcher.watson.ibm.com/project/5669.

  2. Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks.

    Science.gov (United States)

    Balaur, Irina; Mazein, Alexander; Saqi, Mansoor; Lysenko, Artem; Rawlings, Christopher J; Auffray, Charles

    2017-04-01

    The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/ . The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework . ibalaur@eisbm.org. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  3. Construction and Analysis of Two Genome-Scale Deletion Libraries for Bacillus subtilis.

    Science.gov (United States)

    Koo, Byoung-Mo; Kritikos, George; Farelli, Jeremiah D; Todor, Horia; Tong, Kenneth; Kimsey, Harvey; Wapinski, Ilan; Galardini, Marco; Cabal, Angelo; Peters, Jason M; Hachmann, Anna-Barbara; Rudner, David Z; Allen, Karen N; Typas, Athanasios; Gross, Carol A

    2017-03-22

    A systems-level understanding of Gram-positive bacteria is important from both an environmental and health perspective and is most easily obtained when high-quality, validated genomic resources are available. To this end, we constructed two ordered, barcoded, erythromycin-resistance- and kanamycin-resistance-marked single-gene deletion libraries of the Gram-positive model organism, Bacillus subtilis. The libraries comprise 3,968 and 3,970 genes, respectively, and overlap in all but four genes. Using these libraries, we update the set of essential genes known for this organism, provide a comprehensive compendium of B. subtilis auxotrophic genes, and identify genes required for utilizing specific carbon and nitrogen sources, as well as those required for growth at low temperature. We report the identification of enzymes catalyzing several missing steps in amino acid biosynthesis. Finally, we describe a suite of high-throughput phenotyping methodologies and apply them to provide a genome-wide analysis of competence and sporulation. Altogether, we provide versatile resources for studying gene function and pathway and network architecture in Gram-positive bacteria. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  4. Exploring massive, genome scale datasets with the GenometriCorr package.

    Directory of Open Access Journals (Sweden)

    Alexander Favorov

    2012-05-01

    Full Text Available We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features, for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets.The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor.

  5. Genome-scale mutational signatures of aflatoxin in cells, mice, and human tumors

    Science.gov (United States)

    Huang, Mi Ni; Yu, Willie; Teoh, Wei Wei; Ardin, Maude; Jusakul, Apinya; Ng, Alvin Wei Tian; Boot, Arnoud; Abedi-Ardekani, Behnoush; Villar, Stephanie; Myint, Swe Swe; Othman, Rashidah; Poon, Song Ling; Heguy, Adriana; Olivier, Magali; Hollstein, Monica; Tan, Patrick; Teh, Bin Tean; Sabapathy, Kanaga; Zavadil, Jiri; Rozen, Steven G.

    2017-01-01

    Aflatoxin B1 (AFB1) is a mutagen and IARC (International Agency for Research on Cancer) Group 1 carcinogen that causes hepatocellular carcinoma (HCC). Here, we present the first whole-genome data on the mutational signatures of AFB1 exposure from a total of >40,000 mutations in four experimental systems: two different human cell lines, in liver tumors in wild-type mice, and in mice that carried a hepatitis B surface antigen transgene—this to model the multiplicative effects of aflatoxin exposure and hepatitis B in causing HCC. AFB1 mutational signatures from all four experimental systems were remarkably similar. We integrated the experimental mutational signatures with data from newly sequenced HCCs from Qidong County, China, a region of well-studied aflatoxin exposure. This indicated that COSMIC mutational signature 24, previously hypothesized to stem from aflatoxin exposure, indeed likely represents AFB1 exposure, possibly combined with other exposures. Among published somatic mutation data, we found evidence of AFB1 exposure in 0.7% of HCCs treated in North America, 1% of HCCs from Japan, but 16% of HCCs from Hong Kong. Thus, aflatoxin exposure apparently remains a substantial public health issue in some areas. This aspect of our study exemplifies the promise of future widespread resequencing of tumor genomes in providing new insights into the contribution of mutagenic exposures to cancer incidence. PMID:28739859

  6. Genome-scale analysis of the high-efficient protein secretion system of Aspergillus oryzae.

    Science.gov (United States)

    Liu, Lifang; Feizi, Amir; Österlund, Tobias; Hjort, Carsten; Nielsen, Jens

    2014-06-24

    The koji mold, Aspergillus oryzae is widely used for the production of industrial enzymes due to its particularly high protein secretion capacity and ability to perform post-translational modifications. However, systemic analysis of its secretion system is lacking, generally due to the poorly annotated proteome. Here we defined a functional protein secretory component list of A. oryzae using a previously reported secretory model of S. cerevisiae as scaffold. Additional secretory components were obtained by blast search with the functional components reported in other closely related fungal species such as Aspergillus nidulans and Aspergillus niger. To evaluate the defined component list, we performed transcriptome analysis on three α-amylase over-producing strains with varying levels of secretion capacities. Specifically, secretory components involved in the ER-associated processes (including components involved in the regulation of transport between ER and Golgi) were significantly up-regulated, with many of them never been identified for A. oryzae before. Furthermore, we defined a complete list of the putative A. oryzae secretome and monitored how it was affected by overproducing amylase. In combination with the transcriptome data, the most complete secretory component list and the putative secretome, we improved the systemic understanding of the secretory machinery of A. oryzae in response to high levels of protein secretion. The roles of many newly predicted secretory components were experimentally validated and the enriched component list provides a better platform for driving more mechanistic studies of the protein secretory pathway in this industrially important fungus.

  7. A genome-scale integration and analysis of Lactococcus lactis translation data.

    Directory of Open Access Journals (Sweden)

    Julien Racle

    Full Text Available Protein synthesis is a template polymerization process composed by three main steps: initiation, elongation, and termination. During translation, ribosomes are engaged into polysomes whose size is used for the quantitative characterization of translatome. However, simultaneous transcription and translation in the bacterial cytosol complicates the analysis of translatome data. We established a procedure for robust estimation of the ribosomal density in hundreds of genes from Lactococcus lactis polysome size measurements. We used a mechanistic model of translation to integrate the information about the ribosomal density and for the first time we estimated the protein synthesis rate for each gene and identified the rate limiting steps. Contrary to conventional considerations, we find significant number of genes to be elongation limited. This number increases during stress conditions compared to optimal growth and proteins synthesized at maximum rate are predominantly elongation limited. Consistent with bacterial physiology, we found proteins with similar rate and control characteristics belonging to the same functional categories. Under stress conditions, we found that synthesis rate of regulatory proteins is becoming comparable to proteins favored under optimal growth. These findings suggest that the coupling of metabolic states and protein synthesis is more important than previously thought.

  8. Computing the functional proteome

    DEFF Research Database (Denmark)

    O'Brien, Edward J.; Palsson, Bernhard

    2015-01-01

    Constraint-based models enable the computation of feasible, optimal, and realized biological phenotypes from reaction network reconstructions and constraints on their operation. To date, stoichiometric reconstructions have largely focused on metabolism, resulting in genome-scale metabolic models (M...

  9. Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem.

    Science.gov (United States)

    Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

    2016-12-13

    Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

  10. 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

  11. Genome-scale Evaluation of the Biotechnological Potential of Red Sea Bacilli Strains

    KAUST Repository

    Othoum, Ghofran K.

    2018-02-01

    s metabolism at the system level. Accordingly, a draft metabolic model for B. paralicheniformis Bac48 (iPARA1056) was reconstructed, refined, and validated using growth rate and growth phenotypes under different substrates, generated using high-throughput Phenotype Microarray technology. The presented studies indicate that several of the isolated strains represent promising chassis for the development of cell factories for enzyme production and also point to the richness of their genomes with specific modules of secondary metabolism that have likely evolved in Red Sea Bacilli due to environmental adaptation.

  12. Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction.

    Science.gov (United States)

    Yang, Yuedong; Li, Xiaomei; Zhao, Huiying; Zhan, Jian; Wang, Jihua; Zhou, Yaoqi

    2017-01-01

    As most RNA structures are elusive to structure determination, obtaining solvent accessible surface areas (ASAs) of nucleotides in an RNA structure is an important first step to characterize potential functional sites and core structural regions. Here, we developed RNAsnap, the first machine-learning method trained on protein-bound RNA structures for solvent accessibility prediction. Built on sequence profiles from multiple sequence alignment (RNAsnap-prof), the method provided robust prediction in fivefold cross-validation and an independent test (Pearson correlation coefficients, r, between predicted and actual ASA values are 0.66 and 0.63, respectively). Application of the method to 6178 mRNAs revealed its positive correlation to mRNA accessibility by dimethyl sulphate (DMS) experimentally measured in vivo (r = 0.37) but not in vitro (r = 0.07), despite the lack of training on mRNAs and the fact that DMS accessibility is only an approximation to solvent accessibility. We further found strong association across coding and noncoding regions between predicted solvent accessibility of the mutation site of a single nucleotide variant (SNV) and the frequency of that variant in the population for 2.2 million SNVs obtained in the 1000 Genomes Project. Moreover, mapping solvent accessibility of RNAs to the human genome indicated that introns, 5' cap of 5' and 3' cap of 3' untranslated regions, are more solvent accessible, consistent with their respective functional roles. These results support conformational selections as the mechanism for the formation of RNA-protein complexes and highlight the utility of genome-scale characterization of RNA tertiary structures by RNAsnap. The server and its stand-alone downloadable version are available at http://sparks-lab.org. © 2016 Yang et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

  13. Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimized Implementations in the bnlearn R Package

    Directory of Open Access Journals (Sweden)

    Marco Scutari

    2017-03-01

    Full Text Available It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: Its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world scenarios. Efficient implementations of score-based structure learning benefit from past and current research in optimization theory, which can be adapted to the task by using the network score as the objective function to maximize. This is not true for approaches based on conditional independence tests, called constraint-based learning algorithms. The only optimization in widespread use, backtracking, leverages the symmetries implied by the definitions of neighborhood and Markov blanket. In this paper we illustrate how backtracking is implemented in recent versions of the bnlearn R package, and how it degrades the stability of Bayesian network structure learning for little gain in terms of speed. As an alternative, we describe a software architecture and framework that can be used to parallelize constraint-based structure learning algorithms (also implemented in bnlearn and we demonstrate its performance using four reference networks and two real-world data sets from genetics and systems biology. We show that on modern multi-core or multiprocessor hardware parallel implementations are preferable over backtracking, which was developed when single-processor machines were the norm.

  14. Mechanistic modeling of aberrant energy metabolism in human disease

    Directory of Open Access Journals (Sweden)

    Vineet eSangar

    2012-10-01

    Full Text Available Dysfunction in energy metabolism—including in pathways localized to the mitochondria—has been implicated in the pathogenesis of a wide array of disorders, ranging from cancer to neurodegenerative diseases to type II diabetes. The inherent complexities of energy and mitochondrial metabolism present a significant obstacle in the effort to understand the role that these molecular processes play in the development of disease. To help unravel these complexities, systems biology methods have been applied to develop an array of computational metabolic models, ranging from mitochondria-specific processes to genome-scale cellular networks. These constraint-based models can efficiently simulate aspects of normal and aberrant metabolism in various genetic and environmental conditions. Development of these models leverages—and also provides a powerful means to integrate and interpret—information from a wide range of sources including genomics, proteomics, metabolomics, and enzyme kinetics. Here, we review a variety of mechanistic modeling studies that explore metabolic functions, deficiency disorders, and aberrant biochemical pathways in mitochondria and related regions in the cell.

  15. Transforming Existing Procedural Business Processes into a Constraint-Based Formalism

    NARCIS (Netherlands)

    dr. Martijn Zoet; Eline de Haan; Floor Vermeer; Jeroen van Grondelle; Slinger Jansen

    2013-01-01

    Many organizations use business process management to manage and model their processes. Currently, flow-based process formalisms, such as BPMN, are considered the standard for modeling processes. However, recent literature describes several limitations of this type of formalism that can be solved by

  16. Reconstruction and Quality Control of a Genome-Scale Metabolic Model for Methylococcus capsulatus

    DEFF Research Database (Denmark)

    Lieven, Christian

    Climate-change induced food scarcity is a grim prospect for the future of humanity. Alas, increasingly detrimental effects of global warming and overpopulation are predicted to beget this outlook by the year 2050. Fortunately, the production of single-cell protein (SCP) from microbial fermentations...

  17. Using a Genome-Scale Metabolic Network Model to Elucidate the Mechanism of Chloroquine Action in Plasmodium falciparum

    Science.gov (United States)

    2017-03-22

    The transcriptome data of P. falciparum obtained under different stress conditions (e.g., drug exposure, genetic mutation , etc.) contain information...Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p. Proc. Natl. Acad

  18. Constraining genome-scale models to represent the bow tie structure of metabolism for 13C metabolic flux analysis

    DEFF Research Database (Denmark)

    Backman, Tyler W.H.; Ando, David; Singh, Jahnavi

    2018-01-01

    for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13C MFA or 2S- 13C MFA, as well as provide for a substantially lower set of flux bounds......Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis (13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S-13C MFA) are two techniques used...

  19. FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

    OpenAIRE

    Hoffman, Judy; Wang, Dequan; Yu, Fisher; Darrell, Trevor

    2016-01-01

    Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be surprisingly poor under domain shifts that appear mild to a human observer. For example, training on one city and testing on another in a different geographic region and/or weather condition may result in significantly degraded performance due to pixel-level distribution shift. In this paper, we introduce the first do...

  20. Metabolic modeling of energy balances in Mycoplasma hyopneumoniae shows that pyruvate addition increases growth rate.

    Science.gov (United States)

    Kamminga, Tjerko; Slagman, Simen-Jan; Bijlsma, Jetta J E; Martins Dos Santos, Vitor A P; Suarez-Diez, Maria; Schaap, Peter J

    2017-10-01

    Mycoplasma hyopneumoniae is cultured on large-scale to produce antigen for inactivated whole-cell vaccines against respiratory disease in pigs. However, the fastidious nutrient requirements of this minimal bacterium and the low growth rate make it challenging to reach sufficient biomass yield for antigen production. In this study, we sequenced the genome of M. hyopneumoniae strain 11 and constructed a high quality constraint-based genome-scale metabolic model of 284 chemical reactions and 298 metabolites. We validated the model with time-series data of duplicate fermentation cultures to aim for an integrated model describing the dynamic profiles measured in fermentations. The model predicted that 84% of cellular energy in a standard M. hyopneumoniae cultivation was used for non-growth associated maintenance and only 16% of cellular energy was used for growth and growth associated maintenance. Following a cycle of model-driven experimentation in dedicated fermentation experiments, we were able to increase the fraction of cellular energy used for growth through pyruvate addition to the medium. This increase in turn led to an increase in growth rate and a 2.3 times increase in the total biomass concentration reached after 3-4 days of fermentation, enhancing the productivity of the overall process. The model presented provides a solid basis to understand and further improve M. hyopneumoniae fermentation processes. Biotechnol. Bioeng. 2017;114: 2339-2347. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  1. Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism.

    Science.gov (United States)

    Fleming, R M T; Thiele, I; Provan, G; Nasheuer, H P

    2010-06-07

    The quantitative analysis of biochemical reactions and metabolites is at frontier of biological sciences. The recent availability of high-throughput technology data sets in biology has paved the way for new modelling approaches at various levels of complexity including the metabolome of a cell or an organism. Understanding the metabolism of a single cell and multi-cell organism will provide the knowledge for the rational design of growth conditions to produce commercially valuable reagents in biotechnology. Here, we demonstrate how equations representing steady state mass conservation, energy conservation, the second law of thermodynamics, and reversible enzyme kinetics can be formulated as a single system of linear equalities and inequalities, in addition to linear equalities on exponential variables. Even though the feasible set is non-convex, the reformulation is exact and amenable to large-scale numerical analysis, a prerequisite for computationally feasible genome scale modelling. Integrating flux, concentration and kinetic variables in a unified constraint-based formulation is aimed at increasing the quantitative predictive capacity of flux balance analysis. Incorporation of experimental and theoretical bounds on thermodynamic and kinetic variables ensures that the predicted steady state fluxes are both thermodynamically and biochemically feasible. The resulting in silico predictions are tested against fluxomic data for central metabolism in Escherichia coli and compare favourably with in silico prediction by flux balance analysis. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

  2. Understanding and treating lateral ankle sprains and their consequences: a constraints-based approach.

    Science.gov (United States)

    Wikstrom, Erik A; Hubbard-Turner, Tricia; McKeon, Patrick O

    2013-06-01

    Lateral ankle sprains are a common consequence of physical activity. If not managed appropriately, a cascade of negative alterations to both the joint structure and a person's movement patterns continue to stress the injured ligaments. These alterations result in an individual entering a continuum of disability as evidenced by the ~30 % of ankle sprains that develop into chronic ankle instability (CAI) and up to 78 % of CAI cases that develop into post-traumatic ankle osteoarthritis (OA). Despite this knowledge, no significant improvements in treatment efficacy have been made using traditional treatment paradigms. Therefore, the purpose of this review is to (1) provide an overview of the consequences associated with acute lateral ankle sprains, CAI and post-traumatic ankle OA; (2) introduce the patient-, clinician-, laboratory (PCL)-oriented) model that addresses the lateral ankle sprains and their consequences from a constraints perspective; and (3) introduce the dynamic systems theory as the framework to illustrate how multiple post-injury adaptations create a singular pathology that predisposes individuals with lateral ankle sprains to fall into a continuum of disability. The consequences associated with lateral ankle sprains, CAI and ankle OA are similar and encompass alterations to the structure of the ankle joint (e.g. ligament laxity, positional faults, etc.) and the sensorimotor function responsible for proper ankle joint function (e.g. postural control, gait, etc.). Further, the impairments have been quantified across a range of patient-oriented (e.g. self-report questionnaires), clinician-oriented (e.g. bedside measures of range of motion and postural control), and laboratory-oriented (e.g. arthrometry, gait analysis) outcome measures. The interaction of PCL-oriented outcomes is critically important for understanding the phenomenon of CAI across the continuum of disability. Through the integration of all three sources of evidence, we can clearly see that

  3. Omnidirectional angle constraint based dynamic six-degree-of-freedom measurement for spacecraft rendezvous and docking simulation

    Science.gov (United States)

    Shi, Shendong; Yang, Linghui; Lin, Jiarui; Ren, Yongjie; Guo, Siyang; Zhu, Jigui

    2018-04-01

    In this paper we present a novel omnidirectional angle constraint based method for dynamic 6-DOF (six-degree-of-freedom) measurement. A photoelectric scanning measurement network is employed whose photoelectric receivers are fixed on the measured target. They are in a loop distribution and receive signals from rotating transmitters. Each receiver indicates an angle constraint direction. Therefore, omnidirectional angle constraints can be constructed in each rotation cycle. By solving the constrained optimization problem, 6-DOF information can be obtained, which is independent of traditional rigid coordinate system transformation. For the dynamic error caused by the measurement principle, we present an interpolation method for error reduction. Accuracy testing is performed in an 8  ×  8 m measurement area with four transmitters. The experimental results show that the dynamic orientation RMSEs (root-mean-square errors) are reduced from 0.077° to 0.044°, 0.040° to 0.030° and 0.032° to 0.015° in the X, Y, and Z axes, respectively. The dynamic position RMSE is reduced from 0.65 mm to 0.24 mm. This method is applied during the final approach phase in the rendezvous and docking simulation. Experiments under different conditions are performed in a 40  ×  30 m area, and the method is verified to be effective.

  4. A Thermodynamically-consistent FBA-based Approach to Biogeochemical Reaction Modeling

    Science.gov (United States)

    Shapiro, B.; Jin, Q.

    2015-12-01

    Microbial rates are critical to understanding biogeochemical processes in natural environments. Recently, flux balance analysis (FBA) has been applied to predict microbial rates in aquifers and other settings. FBA is a genome-scale constraint-based modeling approach that computes metabolic rates and other phenotypes of microorganisms. This approach requires a prior knowledge of substrate uptake rates, which is not available for most natural microbes. Here we propose to constrain substrate uptake rates on the basis of microbial kinetics. Specifically, we calculate rates of respiration (and fermentation) using a revised Monod equation; this equation accounts for both the kinetics and thermodynamics of microbial catabolism. Substrate uptake rates are then computed from the rates of respiration, and applied to FBA to predict rates of microbial growth. We implemented this method by linking two software tools, PHREEQC and COBRA Toolbox. We applied this method to acetotrophic methanogenesis by Methanosarcina barkeri, and compared the simulation results to previous laboratory observations. The new method constrains acetate uptake by accounting for the kinetics and thermodynamics of methanogenesis, and predicted well the observations of previous experiments. In comparison, traditional methods of dynamic-FBA constrain acetate uptake on the basis of enzyme kinetics, and failed to reproduce the experimental results. These results show that microbial rate laws may provide a better constraint than enzyme kinetics for applying FBA to biogeochemical reaction modeling.

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

    NARCIS (Netherlands)

    Yuan, H.; Cheung, C.Y. Maurice; Poolman, M.G.; Hilbers, P.A.J.; van Riel, N.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

  6. GENOME-BASED MODELING AND DESIGN OF METABOLIC INTERACTIONS IN MICROBIAL COMMUNITIES

    Directory of Open Access Journals (Sweden)

    Radhakrishnan Mahadevan

    2012-10-01

    With the advent of genome sequencing, omics technologies, bioinformatics and genome-scale modeling, researchers now have unprecedented capabilities to analyze and engineer the metabolism of microbial communities. The goal of this review is to summarize recent applications of genome-scale metabolic modeling to microbial communities. A brief introduction to lumped community models is used to motivate the need for genome-level descriptions of individual species and their metabolic interactions. The review of genome-scale models begins with static modeling approaches, which are appropriate for communities where the extracellular environment can be assumed to be time invariant or slowly varying. Dynamic extensions of the static modeling approach are described, and then applications of genome-scale models for design of synthetic microbial communities are reviewed. The review concludes with a summary of metagenomic tools for analyzing community metabolism and an outlook for future research.

  7. Genome-scale metabolic reconstructions and theoretical investigation of methane conversion in Methylomicrobium buryatense strain 5G(B1).

    Science.gov (United States)

    de la Torre, Andrea; Metivier, Aisha; Chu, Frances; Laurens, Lieve M L; Beck, David A C; Pienkos, Philip T; Lidstrom, Mary E; Kalyuzhnaya, Marina G

    2015-11-25

    Methane-utilizing bacteria (methanotrophs) are capable of growth on methane and are attractive systems for bio-catalysis. However, the application of natural methanotrophic strains to large-scale production of value-added chemicals/biofuels requires a number of physiological and genetic alterations. An accurate metabolic model coupled with flux balance analysis can provide a solid interpretative framework for experimental data analyses and integration. A stoichiometric flux balance model of Methylomicrobium buryatense strain 5G(B1) was constructed and used for evaluating metabolic engineering strategies for biofuels and chemical production with a methanotrophic bacterium as the catalytic platform. The initial metabolic reconstruction was based on whole-genome predictions. Each metabolic step was manually verified, gapfilled, and modified in accordance with genome-wide expression data. The final model incorporates a total of 841 reactions (in 167 metabolic pathways). Of these, up to 400 reactions were recruited to produce 118 intracellular metabolites. The flux balance simulations suggest that only the transfer of electrons from methanol oxidation to methane oxidation steps can support measured growth and methane/oxygen consumption parameters, while the scenario employing NADH as a possible source of electrons for particulate methane monooxygenase cannot. Direct coupling between methane oxidation and methanol oxidation accounts for most of the membrane-associated methane monooxygenase activity. However the best fit to experimental results is achieved only after assuming that the efficiency of direct coupling depends on growth conditions and additional NADH input (about 0.1-0.2 mol of incremental NADH per one mol of methane oxidized). The additional input is proposed to cover loss of electrons through inefficiency and to sustain methane oxidation at perturbations or support uphill electron transfer. Finally, the model was used for testing the carbon conversion

  8. ANISEED 2017: extending the integrated ascidian database to the exploration and evolutionary comparison of genome-scale datasets.

    Science.gov (United States)

    Brozovic, Matija; Dantec, Christelle; Dardaillon, Justine; Dauga, Delphine; Faure, Emmanuel; Gineste, Mathieu; Louis, Alexandra; Naville, Magali; Nitta, Kazuhiro R; Piette, Jacques; Reeves, Wendy; Scornavacca, Céline; Simion, Paul; Vincentelli, Renaud; Bellec, Maelle; Aicha, Sameh Ben; Fagotto, Marie; Guéroult-Bellone, Marion; Haeussler, Maximilian; Jacox, Edwin; Lowe, Elijah K; Mendez, Mickael; Roberge, Alexis; Stolfi, Alberto; Yokomori, Rui; Brown, C Titus; Cambillau, Christian; Christiaen, Lionel; Delsuc, Frédéric; Douzery, Emmanuel; Dumollard, Rémi; Kusakabe, Takehiro; Nakai, Kenta; Nishida, Hiroki; Satou, Yutaka; Swalla, Billie; Veeman, Michael; Volff, Jean-Nicolas; Lemaire, Patrick

    2018-01-04

    ANISEED (www.aniseed.cnrs.fr) is the main model organism database for tunicates, the sister-group of vertebrates. This release gives access to annotated genomes, gene expression patterns, and anatomical descriptions for nine ascidian species. It provides increased integration with external molecular and taxonomy databases, better support for epigenomics datasets, in particular RNA-seq, ChIP-seq and SELEX-seq, and features novel interactive interfaces for existing and novel datatypes. In particular, the cross-species navigation and comparison is enhanced through a novel taxonomy section describing each represented species and through the implementation of interactive phylogenetic gene trees for 60% of tunicate genes. The gene expression section displays the results of RNA-seq experiments for the three major model species of solitary ascidians. Gene expression is controlled by the binding of transcription factors to cis-regulatory sequences. A high-resolution description of the DNA-binding specificity for 131 Ciona robusta (formerly C. intestinalis type A) transcription factors by SELEX-seq is provided and used to map candidate binding sites across the Ciona robusta and Phallusia mammillata genomes. Finally, use of a WashU Epigenome browser enhances genome navigation, while a Genomicus server was set up to explore microsynteny relationships within tunicates and with vertebrates, Amphioxus, echinoderms and hemichordates. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  9. Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment

    KAUST Repository

    Rapakoulia, Trisevgeni

    2017-08-09

    Motivation: Drug combination therapy for treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect, while reducing the likelihood of drug resistance. In order to reduce time and cost spent in comprehensive screens, methods are needed which can model additive effects of possible drug combinations. Results: We here show that the transcriptional response to combinatorial drug treatment at promoters, as measured by single molecule CAGE technology, is accurately described by a linear combination of the responses of the individual drugs at a genome wide scale. We also find that the same linear relationship holds for transcription at enhancer elements. We conclude that the described approach is promising for eliciting the transcriptional response to multidrug treatment at promoters and enhancers in an unbiased genome wide way, which may minimize the need for exhaustive combinatorial screens.

  10. Genome-scale data suggest reclassifications in the Leisingera-Phaeobacter cluster including proposals for Sedimentitalea gen. nov. and Pseudophaeobacter gen. nov.

    Directory of Open Access Journals (Sweden)

    Sven eBreider

    2014-08-01

    Full Text Available Earlier phylogenetic analyses of the marine Rhodobacteraceae (class Alphaproteobacteria genera Leisingera and Phaeobacter indicated that neither genus might be monophyletic. We here used phylogenetic reconstruction from genome-scale data, MALDI-TOF mass-spectrometry analysis and a re-assessment of the phenotypic data from the literature to settle this matter, aiming at a reclassification of the two genera. Neither Phaeobacter nor Leisingera formed a clade in any of the phylogenetic analyses conducted. Rather, smaller monophyletic assemblages emerged, which were phenotypically more homogeneous, too. We thus propose the reclassification of Leisingera nanhaiensis as the type species of a new genus as Sedimentitalea nanhaiensis gen. nov., comb. nov., the reclassification of Phaeobacter arcticus and Phaeobacter leonis as Pseudophaeobacter arcticus gen. nov., comb. nov. and Pseudophaeobacter leonis comb. nov., and the reclassification of Phaeobacter aquaemixtae, Phaeobacter caeruleus and Phaeobacter daeponensis as Leisingera aquaemixtae comb. nov., Leisingera caerulea comb. nov. and Leisingera daeponensis comb. nov. The genera Phaeobacter and Leisingera are accordingly emended.

  11. A Genome-Scale Investigation of How Sequence, Function, and Tree-Based Gene Properties Influence Phylogenetic Inference.

    Science.gov (United States)

    Shen, Xing-Xing; Salichos, Leonidas; Rokas, Antonis

    2016-09-02

    Molecular phylogenetic inference is inherently dependent on choices in both methodology and data. Many insightful studies have shown how choices in methodology, such as the model of sequence evolution or optimality criterion used, can strongly influence inference. In contrast, much less is known about the impact of choices in the properties of the data, typically genes, on phylogenetic inference. We investigated the relationships between 52 gene properties (24 sequence-based, 19 function-based, and 9 tree-based) with each other and with three measures of phylogenetic signal in two assembled data sets of 2,832 yeast and 2,002 mammalian genes. We found that most gene properties, such as evolutionary rate (measured through the percent average of pairwise identity across taxa) and total tree length, were highly correlated with each other. Similarly, several gene properties, such as gene alignment length, Guanine-Cytosine content, and the proportion of tree distance on internal branches divided by relative composition variability (treeness/RCV), were strongly correlated with phylogenetic signal. Analysis of partial correlations between gene properties and phylogenetic signal in which gene evolutionary rate and alignment length were simultaneously controlled, showed similar patterns of correlations, albeit weaker in strength. Examination of the relative importance of each gene property on phylogenetic signal identified gene alignment length, alongside with number of parsimony-informative sites and variable sites, as the most important predictors. Interestingly, the subsets of gene properties that optimally predicted phylogenetic signal differed considerably across our three phylogenetic measures and two data sets; however, gene alignment length and RCV were consistently included as predictors of all three phylogenetic measures in both yeasts and mammals. These results suggest that a handful of sequence-based gene properties are reliable predictors of phylogenetic signal

  12. Flux balance modeling to predict bacterial survival during pulsed-activity events

    Science.gov (United States)

    Jose, Nicholas A.; Lau, Rebecca; Swenson, Tami L.; Klitgord, Niels; Garcia-Pichel, Ferran; Bowen, Benjamin P.; Baran, Richard; Northen, Trent R.

    2018-04-01

    Desert biological soil crusts (BSCs) are cyanobacteria-dominated surface soil microbial communities common to plant interspaces in arid environments. The capability to significantly dampen their metabolism allows them to exist for extended periods in a desiccated dormant state that is highly robust to environmental stresses. However, within minutes of wetting, metabolic functions reboot, maximizing activity during infrequent permissive periods. Microcoleus vaginatus, a primary producer within the crust ecosystem and an early colonizer, initiates crust formation by binding particles in the upper layer of soil via exopolysaccharides, making microbial dominated biological soil crusts highly dependent on the viability of this organism. Previous studies have suggested that biopolymers play a central role in the survival of this organism by powering resuscitation, rapidly forming compatible solutes, and fueling metabolic activity in dark, hydrated conditions. To elucidate the mechanism of this phenomenon and provide a basis for future modeling of BSCs, we developed a manually curated, genome-scale metabolic model of Microcoleus vaginatus (iNJ1153). To validate this model, gas chromatography-mass spectroscopy (GC-MS) and liquid chromatography-mass spectroscopy (LC-MS) were used to characterize the rate of biopolymer accumulation and depletion in in hydrated Microcoleus vaginatus under light and dark conditions. Constraint-based flux balance analysis showed agreement between model predictions and experimental reaction fluxes. A significant amount of consumed carbon and light energy is invested into storage molecules glycogen and polyphosphate, while β-polyhydroxybutyrate may function as a secondary resource. Pseudo-steady-state modeling suggests that glycogen, the primary carbon source with the fastest depletion rate, will be exhausted if M. vaginatus experiences dark wetting events 4 times longer than light wetting events.

  13. Input and output constraints-based stabilisation of switched nonlinear systems with unstable subsystems and its application

    Science.gov (United States)

    Chen, Chao; Liu, Qian; Zhao, Jun

    2018-01-01

    This paper studies the problem of stabilisation of switched nonlinear systems with output and input constraints. We propose a recursive approach to solve this issue. None of the subsystems are assumed to be stablisable while the switched system is stabilised by dual design of controllers for subsystems and a switching law. When only dealing with bounded input, we provide nested switching controllers using an extended backstepping procedure. If both input and output constraints are taken into consideration, a Barrier Lyapunov Function is employed during operation to construct multiple Lyapunov functions for switched nonlinear system in the backstepping procedure. As a practical example, the control design of an equilibrium manifold expansion model of aero-engine is given to demonstrate the effectiveness of the proposed design method.

  14. Critical chain construction with multi-resource constraints based on portfolio technology in South-to-North Water Diversion Project

    Directory of Open Access Journals (Sweden)

    Jing-chun Feng

    2011-06-01

    Full Text Available Recently, the critical chain study has become a hot issue in the project management research field. The construction of the critical chain with multi-resource constraints is a new research subject. According to the system analysis theory and project portfolio theory, this paper discusses the creation of project portfolios based on the similarity principle and gives the definition of priority in multi-resource allocation based on quantitative analysis. A model with multi-resource constraints, which can be applied to the critical chain construction of the A-bid section in the South-to-North Water Diversion Project, was proposed. Contrast analysis with the comprehensive treatment construction method and aggressive treatment construction method was carried out. This paper also makes suggestions for further research directions and subjects, which will be useful in improving the theories in relevant research fields.

  15. Efficient constraint-based Sequential Pattern Mining (SPM algorithm to understand customers’ buying behaviour from time stamp-based sequence dataset

    Directory of Open Access Journals (Sweden)

    Niti Ashish Kumar Desai

    2015-12-01

    Full Text Available Business Strategies are formulated based on an understanding of customer needs. This requires development of a strategy to understand customer behaviour and buying patterns, both current and future. This involves understanding, first how an organization currently understands customer needs and second predicting future trends to drive growth. This article focuses on purchase trend of customer, where timing of purchase is more important than association of item to be purchased, and which can be found out with Sequential Pattern Mining (SPM methods. Conventional SPM algorithms worked purely on frequency identifying patterns that were more frequent but suffering from challenges like generation of huge number of uninteresting patterns, lack of user’s interested patterns, rare item problem, etc. Article attempts a solution through development of a SPM algorithm based on various constraints like Gap, Compactness, Item, Recency, Profitability and Length along with Frequency constraint. Incorporation of six additional constraints is as well to ensure that all patterns are recently active (Recency, active for certain time span (Compactness, profitable and indicative of next timeline for purchase (Length―Item―Gap. The article also attempts to throw light on how proposed Constraint-based Prefix Span algorithm is helpful to understand buying behaviour of customer which is in formative stage.

  16. Generalized framework for context-specific metabolic model extraction methods

    Directory of Open Access Journals (Sweden)

    Semidán eRobaina Estévez

    2014-09-01

    Full Text Available Genome-scale metabolic models are increasingly applied to investigate the physiology not only of simple prokaryotes, but also eukaryotes, such as plants, characterized with compartmentalized cells of multiple types. While genome-scale models aim at including the entirety of known metabolic reactions, mounting evidence has indicated that only a subset of these reactions is active in a given context, including: developmental stage, cell type, or environment. As a result, several methods have been proposed to reconstruct context-specific models from existing genome-scale models by integrating various types of high-throughput data. Here we present a mathematical framework that puts all existing methods under one umbrella and provides the means to better understand their functioning, highlight similarities and differences, and to help users in selecting a most suitable method for an application.

  17. FAME, the flux analysis and modelling environment

    NARCIS (Netherlands)

    Boele, J.; Olivier, B.G.; Teusink, B.

    2012-01-01

    Background: The creation and modification of genome-scale metabolic models is a task that requires specialized software tools. While these are available, subsequently running or visualizing a model often relies on disjoint code, which adds additional actions to the analysis routine and, in our

  18. Study on the unified constraint parameter for characterizing in-plane and out-of-plane constraint based on the equivalent plastic strain

    International Nuclear Information System (INIS)

    Yang Jie; Wang Guozhen; Xuan Fuzhen; Tu Shandong

    2013-01-01

    Background: Constraint can significantly alter the material's fracture toughness. Purpose: In order to increase accuracy of the structural integrity assessment. It needs to consider the effect of constraint on the fracture toughness of nuclear power materials and structures. A unified measure which can reflect both in-plane and out-of-plane constraint is needed. Methods: In this paper, the finite element numerical simulation method was used, a unified measure and characterization parameter of in-plane and out-of-plane constraint based on crack-tip equivalent plastic strain have been investigated. Results: The results show that the area surrounded by ε p isoline has a good relevance with the material's fracture toughness on different constraint conditions, so it may be a suitable parameter. Based on the area A PEEQ , a unified constraint characterization parameter √A p is defined. It was found that there exists a sole linear relation between the normalized fracture toughness J IC /J re f and √A p regardless of the in-plane, out-of-plane constraint and the selection of the p isolines. The sole J IC /J re f-√A p line exists for a certain material. For different materials, the slope of J IC /J re f-√A p reference line is different. The material whose slope is larger has a higher J IC /J re f and is more sensitive to constraint at the same magnitude of normalized unified parameter. Conclusions: The unified J IC /J re f -√A p reference line may be used to assess the safety of a cracked component with any constraint levels regardless of in-plane or out-of-plane constraint or both. (authors)

  19. Why does yeast ferment? A flux balance analysis study.

    NARCIS (Netherlands)

    Simeonides, E.; Murabito, E.; Smalbone, K.; Westerhoff, H.V.

    2010-01-01

    Advances in biological techniques have led to the availability of genome-scale metabolic reconstructions for yeast. The size and complexity of such networks impose limits on what types of analyses one can perform. Constraint-based modelling overcomes some of these restrictions by using

  20. The Systems Biology Markup Language (SBML) Level 3 Package: Flux Balance Constraints.

    Science.gov (United States)

    Olivier, Brett G; Bergmann, Frank T

    2015-09-04

    Constraint-based modeling is a well established modelling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size, genome scale models are typically analysed using constraint-based optimization techniques. One widely used method is Flux Balance Analysis (FBA) which, for example, requires a modelling description to include: the definition of a stoichiometric matrix, an objective function and bounds on the values that fluxes can obtain at steady state. The Flux Balance Constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modelling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. The FBC package establishes a base level for the unambiguous exchange of genome-scale, constraint-based models, that can be built upon by the community to meet future needs (e. g. by extending it to cover dynamic FBC models).

  1. Exploring Networks at the genome scale

    NARCIS (Netherlands)

    Lam, M.C.; Puchalka, J.; Diez, M.S.; Martins Dos Santos, V.A.P.

    2010-01-01

    Systems biology is aimed at achieving a holistic understanding of living organisms, while synthetic biology seeks to design and construct new living organisms with targeted functionalities. Genome sequencing and the fields of ‘omics’ technology have proven a goldmine of information for scientists

  2. Analysis of Genome-Scale Data

    NARCIS (Netherlands)

    Kemmeren, P.P.C.W.

    2005-01-01

    The genetic material of every cell in an organism is stored inside DNA in the form of genes, which together form the genome. The information stored in the DNA is translated to RNA and subsequently to proteins, which form complex biological systems. The availability of whole genome sequences has

  3. Analysis of Genome-Scale Data

    OpenAIRE

    Kemmeren, P.P.C.W.

    2005-01-01

    The genetic material of every cell in an organism is stored inside DNA in the form of genes, which together form the genome. The information stored in the DNA is translated to RNA and subsequently to proteins, which form complex biological systems. The availability of whole genome sequences has given rise to the parallel development of other high-throughput approaches such as determining mRNA expression level changes, gene-deletion phenotypes, chromosomal location of DNA binding proteins, cel...

  4. Hierarchical energy and frequency security pricing in a smart microgrid: An equilibrium-inspired epsilon constraint based multi-objective decision making approach

    International Nuclear Information System (INIS)

    Rezaei, Navid; Kalantar, Mohsen

    2015-01-01

    Highlights: • Proposing a multi-objective security pricing mechanism for islanded microgrids. • Generating Pareto points using epsilon constraint methodology. • Best compromise solution using a novel decision making approach. • An equilibrium-inspired technique is used as an efficient decision making method. • Stochastic management of hierarchical reserves in a droop controlled microgrid. - Abstract: The present paper formulates a frequency security constrained energy management system for an islanded microgrid. Static and dynamic securities of the microgrids have been modeled in depth based on droop control paradigm. The derived frequency dependent modeling is incorporated into a multi-objective energy management system. Microgrid central controller is in charge to determine optimal prices of energy and frequency security such that technical, economic and environmental targets are satisfied simultaneously. The associated prices are extracted based on calculating related Lagrange multipliers corresponding to providing the microgrid hourly energy and reserve requirements. Besides, to generate optimal Pareto solutions of the proposed multi-objective framework augmented epsilon constraint method is applied. Moreover, a novel methodology on the basis of Nash equilibrium strategy is devised and employed to select the best compromise solution from the generated Pareto front. Comprehensive analysis tool is implemented in a typical test microgrid and executed over a 24 h scheduling time horizon. The energy, primary and secondary frequency control reserves have been scheduled appropriately in three different case-studies which are defined based on the microgrid various operational policies. The optimization results verify that the operational policies adopted by means of the microgrid central controller have direct impacts on determined energy and security prices. The illustrative implementations can give the microgrid central controller an insight view to provide

  5. Final Report Coupling in silico microbial models with reactive transport models to predict the fate of contaminants in the subsurface.

    Energy Technology Data Exchange (ETDEWEB)

    Lovley, Derek R.

    2012-10-31

    This project successfully accomplished its goal of coupling genome-scale metabolic models with hydrological and geochemical models to predict the activity of subsurface microorganisms during uranium bioremediation. Furthermore, it was demonstrated how this modeling approach can be used to develop new strategies to optimize bioremediation. The approach of coupling genome-scale metabolic models with reactive transport modeling is now well enough established that it has been adopted by other DOE investigators studying uranium bioremediation. Furthermore, the basic principles developed during our studies will be applicable to much broader investigations of microbial activities, not only for other types of bioremediation, but microbial metabolism in diversity of environments. This approach has the potential to make an important contribution to predicting the impact of environmental perturbations on the cycling of carbon and other biogeochemical cycles.

  6. Semi-automated curation of metabolic models via flux balance analysis: a case study with Mycoplasma gallisepticum.

    Directory of Open Access Journals (Sweden)

    Eddy J Bautista

    Full Text Available Primarily used for metabolic engineering and synthetic biology, genome-scale metabolic modeling shows tremendous potential as a tool for fundamental research and curation of metabolism. Through a novel integration of flux balance analysis and genetic algorithms, a strategy to curate metabolic networks and facilitate identification of metabolic pathways that may not be directly inferable solely from genome annotation was developed. Specifically, metabolites involved in unknown reactions can be determined, and potentially erroneous pathways can be identified. The procedure developed allows for new fundamental insight into metabolism, as well as acting as a semi-automated curation methodology for genome-scale metabolic modeling. To validate the methodology, a genome-scale metabolic model for the bacterium Mycoplasma gallisepticum was created. Several reactions not predicted by the genome annotation were postulated and validated via the literature. The model predicted an average growth rate of 0.358±0.12[Formula: see text], closely matching the experimentally determined growth rate of M. gallisepticum of 0.244±0.03[Formula: see text]. This work presents a powerful algorithm for facilitating the identification and curation of previously known and new metabolic pathways, as well as presenting the first genome-scale reconstruction of M. gallisepticum.

  7. Constraint-based Word Segmentation for Chinese

    DEFF Research Database (Denmark)

    Christiansen, Henning; Bo, Li

    2014-01-01

    -hoc and statistically based methods. In this paper, we show experiments of implementing different approaches to CWSP in the framework of CHR Grammars [Christiansen, 2005] that provides a constraint solving approach to language analysis. CHR Grammars are based upon Constraint Handling Rules, CHR [Frühwirth, 1998, 2009......], which is a declarative, high-level programming language for specification and implementation of constraint solvers....

  8. Constraint-based verification of imperative programs

    OpenAIRE

    Beyene, Tewodros Awgichew

    2011-01-01

    work presented in the context of the European Master’s program in Computational Logic, as the partial requirement for obtaining Master of Science degree in Computational Logic The continuous reduction in the cost of computing ever since the first days of computers has resulted in the ubiquity of computing systems today; there is no any sphere of life in the daily routine of human beings that is not directly or indirectly influenced by computer systems anymore. But this high reliance ...

  9. Cyanobacterial Biofuels: Strategies and Developments on Network and Modeling.

    Science.gov (United States)

    Klanchui, Amornpan; Raethong, Nachon; Prommeenate, Peerada; Vongsangnak, Wanwipa; Meechai, Asawin

    Cyanobacteria, the phototrophic microorganisms, have attracted much attention recently as a promising source for environmentally sustainable biofuels production. However, barriers for commercial markets of cyanobacteria-based biofuels concern the economic feasibility. Miscellaneous strategies for improving the production performance of cyanobacteria have thus been developed. Among these, the simple ad hoc strategies resulting in failure to optimize fully cell growth coupled with desired product yield are explored. With the advancement of genomics and systems biology, a new paradigm toward systems metabolic engineering has been recognized. In particular, a genome-scale metabolic network reconstruction and modeling is a crucial systems-based tool for whole-cell-wide investigation and prediction. In this review, the cyanobacterial genome-scale metabolic models, which offer a system-level understanding of cyanobacterial metabolism, are described. The main process of metabolic network reconstruction and modeling of cyanobacteria are summarized. Strategies and developments on genome-scale network and modeling through the systems metabolic engineering approach are advanced and employed for efficient cyanobacterial-based biofuels production.

  10. Quantitative intracellular flux modeling and applications in biotherapeutic development and production using CHO cell cultures.

    Science.gov (United States)

    Huang, Zhuangrong; Lee, Dong-Yup; Yoon, Seongkyu

    2017-12-01

    Chinese hamster ovary (CHO) cells have been widely used for producing many recombinant therapeutic proteins. Constraint-based modeling, such as flux balance analysis (FBA) and metabolic flux analysis (MFA), has been developing rapidly for the quantification of intracellular metabolic flux distribution at a systematic level. Such methods would produce detailed maps of flows through metabolic networks, which contribute significantly to better understanding of metabolism in cells. Although these approaches have been extensively established in microbial systems, their application to mammalian cells is sparse. This review brings together the recent development of constraint-based models and their applications in CHO cells. The further development of constraint-based modeling approaches driven by multi-omics datasets is discussed, and a framework of potential modeling application in cell culture engineering is proposed. Improved cell culture system understanding will enable robust developments in cell line and bioprocess engineering thus accelerating consistent process quality control in biopharmaceutical manufacturing. © 2017 Wiley Periodicals, Inc.

  11. Novel Multiscale Modeling Tool Applied to Pseudomonas aeruginosa Biofilm Formation

    OpenAIRE

    Biggs, Matthew B.; Papin, Jason A.

    2013-01-01

    Multiscale modeling is used to represent biological systems with increasing frequency and success. Multiscale models are often hybrids of different modeling frameworks and programming languages. We present the MATLAB-NetLogo extension (MatNet) as a novel tool for multiscale modeling. We demonstrate the utility of the tool with a multiscale model of Pseudomonas aeruginosa biofilm formation that incorporates both an agent-based model (ABM) and constraint-based metabolic modeling. The hybrid mod...

  12. Investigating genotype-phenotype relationships in Saccharomyces cerevisiae metabolic network through stoichiometric modeling

    DEFF Research Database (Denmark)

    Brochado, Ana Rita

    processes. Metabolism is an extensively studied and characterised subcellular system, for which several modeling approaches have been proposed over the last 20 years. Nowadays, stoichiometric modeling of metabolism is done at the genome scale and it has diverse applications, many of them for helping....... This chapter aims at providing the reader with relevant state-of-the-art information concerning Systems Biology, Genome-Scale Metabolic Modeling and Metabolic Engineering. Particular attention is given to the yeast Saccharomyces cerevisiae, the eukaryotic model organism used thought the thesis.......A holistic view of the cell is fundamental for gaining insights into genotype to phenotype relationships. Systems Biology is a discipline within Biology, which uses such holistic approach by focusing on the development and application of tools for studying the structure and dynamics of cellular...

  13. Genome-based Modeling and Design of Metabolic Interactions in Microbial Communities.

    Science.gov (United States)

    Mahadevan, Radhakrishnan; Henson, Michael A

    2012-01-01

    Biotechnology research is traditionally focused on individual microbial strains that are perceived to have the necessary metabolic functions, or the capability to have these functions introduced, to achieve a particular task. For many important applications, the development of such omnipotent microbes is an extremely challenging if not impossible task. By contrast, nature employs a radically different strategy based on synergistic combinations of different microbial species that collectively achieve the desired task. These natural communities have evolved to exploit the native metabolic capabilities of each species and are highly adaptive to changes in their environments. However, microbial communities have proven difficult to study due to a lack of suitable experimental and computational tools. With the advent of genome sequencing, omics technologies, bioinformatics and genome-scale modeling, researchers now have unprecedented capabilities to analyze and engineer the metabolism of microbial communities. The goal of this review is to summarize recent applications of genome-scale metabolic modeling to microbial communities. A brief introduction to lumped community models is used to motivate the need for genome-level descriptions of individual species and their metabolic interactions. The review of genome-scale models begins with static modeling approaches, which are appropriate for communities where the extracellular environment can be assumed to be time invariant or slowly varying. Dynamic extensions of the static modeling approach are described, and then applications of genome-scale models for design of synthetic microbial communities are reviewed. The review concludes with a summary of metagenomic tools for analyzing community metabolism and an outlook for future research.

  14. Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models

    Directory of Open Access Journals (Sweden)

    Joseph A. Wayman

    2015-03-01

    Full Text Available Cell-free systems offer many advantages for the study, manipulation and modeling of metabolism compared to in vivo processes. Many of the challenges confronting genome-scale kinetic modeling can potentially be overcome in a cell-free system. For example, there is no complex transcriptional regulation to consider, transient metabolic measurements are easier to obtain, and we no longer have to consider cell growth. Thus, cell-free operation holds several significant advantages for model development, identification and validation. Theoretically, genome-scale cell-free kinetic models may be possible for industrially important organisms, such as E. coli, if a simple, tractable framework for integrating allosteric regulation with enzyme kinetics can be formulated. Toward this unmet need, we present an effective biochemical network modeling framework for building dynamic cell-free metabolic models. The key innovation of our approach is the integration of simple effective rules encoding complex allosteric regulation with traditional kinetic pathway modeling. We tested our approach by modeling the time evolution of several hypothetical cell-free metabolic networks. We found that simple effective rules, when integrated with traditional enzyme kinetic expressions, captured complex allosteric patterns such as ultrasensitivity or non-competitive inhibition in the absence of mechanistic information. Second, when integrated into network models, these rules captured classic regulatory patterns such as product-induced feedback inhibition. Lastly, we showed, at least for the network architectures considered here, that we could simultaneously estimate kinetic parameters and allosteric connectivity from synthetic data starting from an unbiased collection of possible allosteric structures using particle swarm optimization. However, when starting with an initial population that was heavily enriched with incorrect structures, our particle swarm approach could converge

  15. Finding Deadlocks of Event-B Models by Constraint Solving

    DEFF Research Database (Denmark)

    Hallerstede, Stefan; Leuschel, Michael

    we propose a constraint-based approach to nding deadlocks employing the ProB constraint solver to nd values for the constants and variables of formal models that describe a deadlocking state. We discuss the principles of the technique implemented in ProB's Prolog kernel and present some results...

  16. Roles of Solvent Accessibility and Gene Expression in Modeling Protein Sequence Evolution

    OpenAIRE

    Kuangyu Wang; Shuhui Yu; Xiang Ji; Clemens Lakner; Alexander Griffing; Jeffrey L. Thorne

    2015-01-01

    Models of protein evolution tend to ignore functional constraints, although structural constraints are sometimes incorporated. Here we propose a probabilistic framework for codon substitution that evaluates joint effects of relative solvent accessibility (RSA), a structural constraint; and gene expression, a functional constraint. First, we explore the relationship between RSA and codon usage at the genomic scale as well as at the individual gene scale. Motivated by these results, we construc...

  17. Generating Genome-Scale Candidate Gene Lists for Pharmacogenomics

    DEFF Research Database (Denmark)

    Hansen, Niclas Tue; Brunak, Søren; Altman, R. B.

    2009-01-01

    A critical task in pharmacogenomics is identifying genes that may be important modulators of drug response. High-throughput experimental methods are often plagued by false positives and do not take advantage of existing knowledge. Candidate gene lists can usefully summarize existing knowledge...

  18. IRiS: construction of ARG networks at genomic scales.

    Science.gov (United States)

    Javed, Asif; Pybus, Marc; Melé, Marta; Utro, Filippo; Bertranpetit, Jaume; Calafell, Francesc; Parida, Laxmi

    2011-09-01

    Given a set of extant haplotypes IRiS first detects high confidence recombination events in their shared genealogy. Next using the local sequence topology defined by each detected event, it integrates these recombinations into an ancestral recombination graph. While the current system has been calibrated for human population data, it is easily extendible to other species as well. IRiS (Identification of Recombinations in Sequences) binary files are available for non-commercial use in both Linux and Microsoft Windows, 32 and 64 bit environments from https://researcher.ibm.com/researcher/view_project.php?id = 2303 parida@us.ibm.com.

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

    DEFF Research Database (Denmark)

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

    2003-01-01

    The metabolic network in the yeast Saccharomyces cerevisiae was reconstructed using currently available genomic, biochemical, and physiological information. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, and transport steps between the compartments...

  20. Putting computational modeling at the fingertips of bench biologists

    DEFF Research Database (Denmark)

    Sonnenschein, Nikolaus; Herrgard, Markus

    Communication is key in successful collaborations between theoretical and experimental life scientists. In our line of work we integrate physiological and systems-level data of cell factories with constraint-based Modeling approaches to predict suitable targets for metabolic engineering. The most......-level programming environment Mathematica and an open source metabolic modeling package (MASS Toolbox) to quickly prototype maps and other interactive widgets. In the long term, we would like to make those publicly accessible using open web technologies....

  1. Optimizing Maintenance of Constraint-Based Database Caches

    Science.gov (United States)

    Klein, Joachim; Braun, Susanne

    Caching data reduces user-perceived latency and often enhances availability in case of server crashes or network failures. DB caching aims at local processing of declarative queries in a DBMS-managed cache close to the application. Query evaluation must produce the same results as if done at the remote database backend, which implies that all data records needed to process such a query must be present and controlled by the cache, i. e., to achieve “predicate-specific” loading and unloading of such record sets. Hence, cache maintenance must be based on cache constraints such that “predicate completeness” of the caching units currently present can be guaranteed at any point in time. We explore how cache groups can be maintained to provide the data currently needed. Moreover, we design and optimize loading and unloading algorithms for sets of records keeping the caching units complete, before we empirically identify the costs involved in cache maintenance.

  2. A Constraint-Based Understanding of Design Spaces

    DEFF Research Database (Denmark)

    Biskjaer, Michael Mose; Dalsgaard, Peter; Halskov, Kim

    2014-01-01

    space schema, can identify the properties of the prospective product that s/he can form. Through a case study, we show how design space schemas can support designers in various ways, including gaining an overview of the design process, documenting it, reflecting on it, and developing design concepts...

  3. Leisure market segmentation : an integrated preferences/constraints-based approach

    NARCIS (Netherlands)

    Stemerding, M.P.; Oppewal, H.; Beckers, T.A.M.; Timmermans, H.J.P.

    1996-01-01

    Traditional segmentation schemes are often based on a grouping of consumers with similar preference functions. The research steps, ultimately leading to such segmentation schemes, are typically independent. In the present article, a new integrated approach to segmentation is introduced, which

  4. Widening the Knowledge Acquisition Bottleneck for Constraint-Based Tutors

    Science.gov (United States)

    Suraweera, Pramuditha; Mitrovic, Antonija; Martin, Brent

    2010-01-01

    Intelligent Tutoring Systems (ITS) are effective tools for education. However, developing them is a labour-intensive and time-consuming process. A major share of the effort is devoted to acquiring the domain knowledge that underlies the system's intelligence. The goal of this research is to reduce this knowledge acquisition bottleneck and better…

  5. Towards constraint-based aggregation of energy flexibilities

    DEFF Research Database (Denmark)

    Valsomatzis, Emmanouil; Pedersen, Torben Bach; Abello, Alberto

    2016-01-01

    present the problem of aggregating energy flexibilities taking into account grid capacity limitations and introduce a heuristic aggregation technique. We show through an experimental setup that our proposed technique, compared to a baseline approach, not only leads to a valid unit commitment result......The aggregation of energy flexibilities enables individual producers and/or consumers with small loads to directly participate in the emerging energy markets. On the other hand, aggregation of such flexibilities might also create problems to the operation of the electrical grid. In this paper, we...

  6. Constraint-based local search for container stowage slot planning

    DEFF Research Database (Denmark)

    Pacino, Dario; Jensen, Rune Møller; Bebbington, Tom

    2012-01-01

    -sea vessels. This paper describes the constrained-based local search algorithm used in the second phase of this approach where individual containers are assigned to slots in each bay section. The algorithm can solve this problem in an average of 0.18 seconds per bay, corresponding to a 20 seconds runtime...

  7. An accurate description of Aspergillus niger organic acid batch fermentation through dynamic metabolic modelling.

    Science.gov (United States)

    Upton, Daniel J; McQueen-Mason, Simon J; Wood, A Jamie

    2017-01-01

    Aspergillus niger fermentation has provided the chief source of industrial citric acid for over 50 years. Traditional strain development of this organism was achieved through random mutagenesis, but advances in genomics have enabled the development of genome-scale metabolic modelling that can be used to make predictive improvements in fermentation performance. The parent citric acid-producing strain of A. niger , ATCC 1015, has been described previously by a genome-scale metabolic model that encapsulates its response to ambient pH. Here, we report the development of a novel double optimisation modelling approach that generates time-dependent citric acid fermentation using dynamic flux balance analysis. The output from this model shows a good match with empirical fermentation data. Our studies suggest that citric acid production commences upon a switch to phosphate-limited growth and this is validated by fitting to empirical data, which confirms the diauxic growth behaviour and the role of phosphate storage as polyphosphate. The calibrated time-course model reflects observed metabolic events and generates reliable in silico data for industrially relevant fermentative time series, and for the behaviour of engineered strains suggesting that our approach can be used as a powerful tool for predictive metabolic engineering.

  8. The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

    DEFF Research Database (Denmark)

    Birkel, Garrett W.; Ghosh, Amit; Kumar, Vinay S.

    2017-01-01

    analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed.Results: The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes......, it introduces the capability to use C-13 labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale C-13 Metabolic Flux Analysis (2S-C-13 MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable...... insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs.Conclusions: jQMM will facilitate the design...

  9. GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes

    Directory of Open Access Journals (Sweden)

    Nakayama Yoichi

    2006-03-01

    Full Text Available Abstract Background Successful realization of a "systems biology" approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology. Results We developed the Genome-based Modeling (GEM System for the purpose of automatically prototyping simulation models of cell-wide metabolic pathways from genome sequences and other public biological information. Models generated by the GEM System include an entire Escherichia coli metabolism model comprising 968 reactions of 1195 metabolites, achieving 100% coverage when compared with the KEGG database, 92.38% with the EcoCyc database, and 95.06% with iJR904 genome-scale model. Conclusion The GEM System prototypes qualitative models to reduce the labor-intensive tasks required for systems biology research. Models of over 90 bacterial genomes are available at our web site.

  10. Concepts, challenges, and successes in modeling thermodynamics of metabolism.

    Science.gov (United States)

    Cannon, William R

    2014-01-01

    The modeling of the chemical reactions involved in metabolism is a daunting task. Ideally, the modeling of metabolism would use kinetic simulations, but these simulations require knowledge of the thousands of rate constants involved in the reactions. The measurement of rate constants is very labor intensive, and hence rate constants for most enzymatic reactions are not available. Consequently, constraint-based flux modeling has been the method of choice because it does not require the use of the rate constants of the law of mass action. However, this convenience also limits the predictive power of constraint-based approaches in that the law of mass action is used only as a constraint, making it difficult to predict metabolite levels or energy requirements of pathways. An alternative to both of these approaches is to model metabolism using simulations of states rather than simulations of reactions, in which the state is defined as the set of all metabolite counts or concentrations. While kinetic simulations model reactions based on the likelihood of the reaction derived from the law of mass action, states are modeled based on likelihood ratios of mass action. Both approaches provide information on the energy requirements of metabolic reactions and pathways. However, modeling states rather than reactions has the advantage that the parameters needed to model states (chemical potentials) are much easier to determine than the parameters needed to model reactions (rate constants). Herein, we discuss recent results, assumptions, and issues in using simulations of state to model metabolism.

  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. New paradigms for metabolic modeling of human cells

    DEFF Research Database (Denmark)

    Mardinoglu, Adil; Nielsen, Jens

    2015-01-01

    review recent work on reconstruction of GEMs for human cell/tissue types and cancer, and the use of GEMs for identification of metabolic changes occurring in response to disease development. We further discuss how GEMs can be used for the development of efficient therapeutic strategies. Finally......, challenges in integration of cell/tissue models for simulation of whole body functions as well as integration of GEMs with other biological networks for generating complete cell/tissue models are presented.......Abnormalities in cellular functions are associated with the progression of human diseases, often resulting in metabolic reprogramming. GEnome-scale metabolic Models (GEMs) have enabled studying global metabolic reprogramming in connection with disease development in a systematic manner. Here we...

  13. Amino acid catabolism-directed biofuel production in Clostridium sticklandii: An insight into model-driven systems engineering

    Directory of Open Access Journals (Sweden)

    C Sangavai

    2017-12-01

    Full Text Available Model-driven systems engineering has been more fascinating process for the microbial production of biofuel and bio-refineries in chemical and pharmaceutical industries. Genome-scale modeling and simulations have been guided for metabolic engineering of Clostridium species for the production of organic solvents and organic acids. Among them, Clostridium sticklandii is one of the potential organisms to be exploited as a microbial cell factory for biofuel production. It is a hyper-ammonia producing bacterium and is able to catabolize amino acids as important carbon and energy sources via Stickland reactions and the development of the specific pathways. Current genomic and metabolic aspects of this bacterium are comprehensively reviewed herein, which provided information for learning about protein catabolism-directed biofuel production. It has a metabolic potential to drive energy and direct solventogenesis as well as acidogenesis from protein catabolism. It produces by-products such as ethanol, acetate, n-butanol, n-butyrate and hydrogen from amino acid catabolism. Model-driven systems engineering of this organism would improve the performance of the industrial sectors and enhance the industrial economy by using protein-based waste in environment-friendly ways. Keywords: Biofuel, Amino acid catabolism, Genome-scale model, Metabolic engineering, Systems biology, ABE fermentation, Clostridium sticklandii

  14. Development of Computational Tools for Metabolic Model Curation, Flux Elucidation and Strain Design

    Energy Technology Data Exchange (ETDEWEB)

    Maranas, Costas D

    2012-05-21

    An overarching goal of the Department of Energy mission is the efficient deployment and engineering of microbial and plant systems to enable biomass conversion in pursuit of high energy density liquid biofuels. This has spurred the pace at which new organisms are sequenced and annotated. This torrent of genomic information has opened the door to understanding metabolism in not just skeletal pathways and a handful of microorganisms but for truly genome-scale reconstructions derived for hundreds of microbes and plants. Understanding and redirecting metabolism is crucial because metabolic fluxes are unique descriptors of cellular physiology that directly assess the current cellular state and quantify the effect of genetic engineering interventions. At the same time, however, trying to keep pace with the rate of genomic data generation has ushered in a number of modeling and computational challenges related to (i) the automated assembly, testing and correction of genome-scale metabolic models, (ii) metabolic flux elucidation using labeled isotopes, and (iii) comprehensive identification of engineering interventions leading to the desired metabolism redirection.

  15. Unifying Model-Based and Reactive Programming within a Model-Based Executive

    Science.gov (United States)

    Williams, Brian C.; Gupta, Vineet; Norvig, Peter (Technical Monitor)

    1999-01-01

    Real-time, model-based, deduction has recently emerged as a vital component in AI's tool box for developing highly autonomous reactive systems. Yet one of the current hurdles towards developing model-based reactive systems is the number of methods simultaneously employed, and their corresponding melange of programming and modeling languages. This paper offers an important step towards unification. We introduce RMPL, a rich modeling language that combines probabilistic, constraint-based modeling with reactive programming constructs, while offering a simple semantics in terms of hidden state Markov processes. We introduce probabilistic, hierarchical constraint automata (PHCA), which allow Markov processes to be expressed in a compact representation that preserves the modularity of RMPL programs. Finally, a model-based executive, called Reactive Burton is described that exploits this compact encoding to perform efficIent simulation, belief state update and control sequence generation.

  16. Novel multiscale modeling tool applied to Pseudomonas aeruginosa biofilm formation.

    Directory of Open Access Journals (Sweden)

    Matthew B Biggs

    Full Text Available Multiscale modeling is used to represent biological systems with increasing frequency and success. Multiscale models are often hybrids of different modeling frameworks and programming languages. We present the MATLAB-NetLogo extension (MatNet as a novel tool for multiscale modeling. We demonstrate the utility of the tool with a multiscale model of Pseudomonas aeruginosa biofilm formation that incorporates both an agent-based model (ABM and constraint-based metabolic modeling. The hybrid model correctly recapitulates oxygen-limited biofilm metabolic activity and predicts increased growth rate via anaerobic respiration with the addition of nitrate to the growth media. In addition, a genome-wide survey of metabolic mutants and biofilm formation exemplifies the powerful analyses that are enabled by this computational modeling tool.

  17. Novel multiscale modeling tool applied to Pseudomonas aeruginosa biofilm formation.

    Science.gov (United States)

    Biggs, Matthew B; Papin, Jason A

    2013-01-01

    Multiscale modeling is used to represent biological systems with increasing frequency and success. Multiscale models are often hybrids of different modeling frameworks and programming languages. We present the MATLAB-NetLogo extension (MatNet) as a novel tool for multiscale modeling. We demonstrate the utility of the tool with a multiscale model of Pseudomonas aeruginosa biofilm formation that incorporates both an agent-based model (ABM) and constraint-based metabolic modeling. The hybrid model correctly recapitulates oxygen-limited biofilm metabolic activity and predicts increased growth rate via anaerobic respiration with the addition of nitrate to the growth media. In addition, a genome-wide survey of metabolic mutants and biofilm formation exemplifies the powerful analyses that are enabled by this computational modeling tool.

  18. A novel low-parameter computational model to aid in-silico glycoengineering

    DEFF Research Database (Denmark)

    Spahn, Philipp N.; Hansen, Anders Holmgaard; Hansen, Henning Gram

    2015-01-01

    benefit from computational models that would better meet the requirements for industrial utilization. Here, we introduce a novel approach combining constraints-based and stochastic techniques to derive a computational model that can predict the effects of gene knockouts on protein glycoprofiles while...... it does not follow any direct equivalent of a genetic code. Instead, its complex biogenesis in the Golgi apparatus (Figure 1A) integrates a variety of influencing factors most of which are only incompletely understood. Various attempts have been undertaken so far to computationally model the process...

  19. MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases

    Directory of Open Access Journals (Sweden)

    Kumar Akhil

    2012-01-01

    Full Text Available Abstract Background Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models, stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity. Description MetRxn is a knowledgebase that includes standardized metabolite and reaction descriptions by integrating information from BRENDA, KEGG, MetaCyc, Reactome.org and 44 metabolic models into a single unified data set. All metabolite entries have matched synonyms, resolved protonation states, and are linked to unique structures. All reaction entries are elementally and charge balanced. This is accomplished through the use of a workflow of lexicographic, phonetic, and structural comparison algorithms. MetRxn allows for the download of standardized versions of existing genome-scale metabolic models and the use of metabolic information for the rapid reconstruction of new ones. Conclusions The standardization in description allows for the direct comparison of the metabolite and reaction content between metabolic models and databases and the exhaustive prospecting of pathways for biotechnological production. This ever-growing dataset currently consists of over 76,000 metabolites participating in more than 72,000 reactions (including unresolved entries. MetRxn is hosted on a web-based platform that uses relational database models (MySQL.

  20. FAME, the Flux Analysis and Modeling Environment

    Directory of Open Access Journals (Sweden)

    Boele Joost

    2012-01-01

    Full Text Available Abstract Background The creation and modification of genome-scale metabolic models is a task that requires specialized software tools. While these are available, subsequently running or visualizing a model often relies on disjoint code, which adds additional actions to the analysis routine and, in our experience, renders these applications suboptimal for routine use by (systems biologists. Results The Flux Analysis and Modeling Environment (FAME is the first web-based modeling tool that combines the tasks of creating, editing, running, and analyzing/visualizing stoichiometric models into a single program. Analysis results can be automatically superimposed on familiar KEGG-like maps. FAME is written in PHP and uses the Python-based PySCeS-CBM for its linear solving capabilities. It comes with a comprehensive manual and a quick-start tutorial, and can be accessed online at http://f-a-m-e.org/. Conclusions With FAME, we present the community with an open source, user-friendly, web-based "one stop shop" for stoichiometric modeling. We expect the application will be of substantial use to investigators and educators alike.

  1. Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification.

    Science.gov (United States)

    Cankorur-Cetinkaya, Ayca; Dikicioglu, Duygu; Oliver, Stephen G

    2017-11-01

    Genome-scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene-reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome-scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA, and steroid biosynthetic processes, cellular alcohol metabolic process, and oxidation-reduction process. Furthermore, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development

  2. Modelling

    CERN Document Server

    Spädtke, P

    2013-01-01

    Modeling of technical machines became a standard technique since computer became powerful enough to handle the amount of data relevant to the specific system. Simulation of an existing physical device requires the knowledge of all relevant quantities. Electric fields given by the surrounding boundary as well as magnetic fields caused by coils or permanent magnets have to be known. Internal sources for both fields are sometimes taken into account, such as space charge forces or the internal magnetic field of a moving bunch of charged particles. Used solver routines are briefly described and some bench-marking is shown to estimate necessary computing times for different problems. Different types of charged particle sources will be shown together with a suitable model to describe the physical model. Electron guns are covered as well as different ion sources (volume ion sources, laser ion sources, Penning ion sources, electron resonance ion sources, and H$^-$-sources) together with some remarks on beam transport.

  3. Automatic Generation of Symbolic Model for Parameterized Synchronous Systems

    Institute of Scientific and Technical Information of China (English)

    Wei-Wen Xu

    2004-01-01

    With the purpose of making the verification of parameterized system more general and easier, in this paper, a new and intuitive language PSL (Parameterized-system Specification Language) is proposed to specify a class of parameterized synchronous systems. From a PSL script, an automatic method is proposed to generate a constraint-based symbolic model. The model can concisely symbolically represent the collections of global states by counting the number of processes in a given state. Moreover, a theorem has been proved that there is a simulation relation between the original system and its symbolic model. Since the abstract and symbolic techniques are exploited in the symbolic model, state-explosion problem in traditional verification methods is efficiently avoided. Based on the proposed symbolic model, a reachability analysis procedure is implemented using ANSI C++ on UNIX platform. Thus, a complete tool for verifying the parameterized synchronous systems is obtained and tested for some cases. The experimental results show that the method is satisfactory.

  4. Systematic integration of experimental data and models in systems biology.

    Science.gov (United States)

    Li, Peter; Dada, Joseph O; Jameson, Daniel; Spasic, Irena; Swainston, Neil; Carroll, Kathleen; Dunn, Warwick; Khan, Farid; Malys, Naglis; Messiha, Hanan L; Simeonidis, Evangelos; Weichart, Dieter; Winder, Catherine; Wishart, Jill; Broomhead, David S; Goble, Carole A; Gaskell, Simon J; Kell, Douglas B; Westerhoff, Hans V; Mendes, Pedro; Paton, Norman W

    2010-11-29

    The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources. Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis. Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.

  5. Computational metabolic engineering strategies for growth-coupled biofuel production by Synechocystis

    Directory of Open Access Journals (Sweden)

    Kiyan Shabestary

    2016-12-01

    Full Text Available Chemical and fuel production by photosynthetic cyanobacteria is a promising technology but to date has not reached competitive rates and titers. Genome-scale metabolic modeling can reveal limitations in cyanobacteria metabolism and guide genetic engineering strategies to increase chemical production. Here, we used constraint-based modeling and optimization algorithms on a genome-scale model of Synechocystis PCC6803 to find ways to improve productivity of fermentative, fatty-acid, and terpene-derived fuels. OptGene and MOMA were used to find heuristics for knockout strategies that could increase biofuel productivity. OptKnock was used to find a set of knockouts that led to coupling between biofuel and growth. Our results show that high productivity of fermentation or reversed beta-oxidation derived alcohols such as 1-butanol requires elimination of NADH sinks, while terpenes and fatty-acid based fuels require creating imbalances in intracellular ATP and NADPH production and consumption. The FBA-predicted productivities of these fuels are at least 10-fold higher than those reported so far in the literature. We also discuss the physiological and practical feasibility of implementing these knockouts. This work gives insight into how cyanobacteria could be engineered to reach competitive biofuel productivities. Keywords: Cyanobacteria, Modeling, Flux balance analysis, Biofuel, MOMA, OptFlux, OptKnock

  6. A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism

    KAUST Repository

    Hefzi, Hooman; Ang, Kok  Siong; Hanscho, Michael; Bordbar, Aarash; Ruckerbauer, David; Lakshmanan, Meiyappan; Orellana, Camila  A.; Baycin-Hizal, Deniz; Huang, Yingxiang; Ley, Daniel; Martinez, Veronica  S.; Kyriakopoulos, Sarantos; Jimé nez, Natalia  E.; Zielinski, Daniel  C.; Quek, Lake-Ee; Wulff, Tune; Arnsdorf, Johnny; Li, Shangzhong; Lee, Jae  Seong; Paglia, Giuseppe; Loira, Nicolas; Spahn, Philipp  N.; Pedersen, Lasse  E.; Gutierrez, Jahir  M.; King, Zachary  A.; Lund, Anne  Mathilde; Nagarajan, Harish; Thomas, Alex; Abdel-Haleem, Alyaa M.; Zanghellini, Juergen; Kildegaard, Helene  F.; Voldborg, Bjø rn  G.; Gerdtzen, Ziomara  P.; Betenbaugh, Michael  J.; Palsson, Bernhard  O.; Andersen, Mikael  R.; Nielsen, Lars  K.; Borth, Nicole; Lee, Dong-Yup; Lewis, Nathan  E.

    2016-01-01

    Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess

  7. Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

    KAUST Repository

    Alzahrani, Majed A.

    2017-07-10

    Growth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such recently emerging high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment. In this dissertation, we first demonstrate that detecting such “co-fit” gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype data sets for E. coli, proteobacteria, and yeast.

  8. Reconstructing the Backbone of the Saccharomycotina Yeast Phylogeny Using Genome-Scale Data

    Directory of Open Access Journals (Sweden)

    Xing-Xing Shen

    2016-12-01

    Full Text Available Understanding the phylogenetic relationships among the yeasts of the subphylum Saccharomycotina is a prerequisite for understanding the evolution of their metabolisms and ecological lifestyles. In the last two decades, the use of rDNA and multilocus data sets has greatly advanced our understanding of the yeast phylogeny, but many deep relationships remain unsupported. In contrast, phylogenomic analyses have involved relatively few taxa and lineages that were often selected with limited considerations for covering the breadth of yeast biodiversity. Here we used genome sequence data from 86 publicly available yeast genomes representing nine of the 11 known major lineages and 10 nonyeast fungal outgroups to generate a 1233-gene, 96-taxon data matrix. Species phylogenies reconstructed using two different methods (concatenation and coalescence and two data matrices (amino acids or the first two codon positions yielded identical and highly supported relationships between the nine major lineages. Aside from the lineage comprised by the family Pichiaceae, all other lineages were monophyletic. Most interrelationships among yeast species were robust across the two methods and data matrices. However, eight of the 93 internodes conflicted between analyses or data sets, including the placements of: the clade defined by species that have reassigned the CUG codon to encode serine, instead of leucine; the clade defined by a whole genome duplication; and the species Ascoidea rubescens. These phylogenomic analyses provide a robust roadmap for future comparative work across the yeast subphylum in the disciplines of taxonomy, molecular genetics, evolutionary biology, ecology, and biotechnology. To further this end, we have also provided a BLAST server to query the 86 Saccharomycotina genomes, which can be found at http://y1000plus.org/blast.

  9. Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.

    Directory of Open Access Journals (Sweden)

    David Burstein

    2009-07-01

    Full Text Available A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors via the Icm/Dot type-IV secretion system and to date, approximately 100 effectors have been identified by various experimental and computational techniques. Effector identification is a critical first step towards the understanding of the pathogenesis system in L. pneumophila as well as in other bacterial pathogens. Here, we formulate the task of effector identification as a classification problem: each L. pneumophila open reading frame (ORF was classified as either effector or not. We computationally defined a set of features that best distinguish effectors from non-effectors. These features cover a wide range of characteristics including taxonomical dispersion, regulatory data, genomic organization, similarity to eukaryotic proteomes and more. Machine learning algorithms utilizing these features were then applied to classify all the ORFs within the L. pneumophila genome. Using this approach we were able to predict and experimentally validate 40 new effectors, reaching a success rate of above 90%. Increasing the number of validated effectors to around 140, we were able to gain novel insights into their characteristics. Effectors were found to have low G+C content, supporting the hypothesis that a large number of effectors originate via horizontal gene transfer, probably from their protozoan host. In addition, effectors were found to cluster in specific genomic regions. Finally, we were able to provide a novel description of the C-terminal translocation signal required for effector translocation by the Icm/Dot secretion system. To conclude, we have discovered 40 novel L. pneumophila effectors, predicted over a hundred additional highly probable effectors, and shown the applicability of machine learning algorithms for the identification and characterization of bacterial pathogenesis determinants.

  10. Genome-scale examination of NBS-encoding genes in blueberry

    Science.gov (United States)

    Blueberry is an important crop worldwide. It is, however, susceptible to a variety of diseases, which can lead to losses in yield and fruit quality. Although screening studies have identified resistant germplasm for some important diseases, still little is known about the molecular basis underlying...

  11. Genome-scale Evaluation of the Biotechnological Potential of Red Sea Bacilli Strains

    KAUST Repository

    Othoum, Ghofran K.

    2018-01-01

    production of industrial enzymes has encouraged the screening of new environments for efficient microbial cell factories. The unique ecological niche of the Red Sea points to the promising metabolic and biosynthetic potential of its microbial system. Here

  12. X-Chromosome Control of Genome-Scale Recombination Rates in House Mice.

    Science.gov (United States)

    Dumont, Beth L

    2017-04-01

    Sex differences in recombination are widespread in mammals, but the causes of this pattern are poorly understood. Previously, males from two interfertile subspecies of house mice, Mus musculus musculus and M. m. castaneus , were shown to exhibit a ∼30% difference in their global crossover frequencies. Much of this crossover rate divergence is explained by six autosomal loci and a large-effect locus on the X chromosome. Intriguingly, the allelic effects at this X-linked locus are transgressive, with the allele conferring increased crossover rate being transmitted by the low crossover rate M. m. castaneus parent. Despite the pronounced divergence between males, females from these subspecies exhibit similar crossover rates, raising the question of how recombination is genetically controlled in this sex. Here, I analyze publicly available genotype data from early generations of the Collaborative Cross, an eight-way panel of recombinant inbred strains, to estimate crossover frequencies in female mice with sex-chromosome genotypes of diverse subspecific origins. Consistent with the transgressive influence of the X chromosome in males, I show that females inheriting an M. m. castaneus X possess higher average crossover rates than females lacking the M. m. castaneus X chromosome. The differential inheritance of the X chromosome in males and females provides a simple genetic explanation for sex-limited evolution of this trait. Further, the presence of X-linked and autosomal crossover rate modifiers with antagonistic effects hints at an underlying genetic conflict fueled by selection for distinct crossover rate optima in males and females. Copyright © 2017 by the Genetics Society of America.

  13. A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks.

    Science.gov (United States)

    Fleming, R M T; Maes, C M; Saunders, M A; Ye, Y; Palsson, B Ø

    2012-01-07

    We derive a convex optimization problem on a steady-state nonequilibrium network of biochemical reactions, with the property that energy conservation and the second law of thermodynamics both hold at the problem solution. This suggests a new variational principle for biochemical networks that can be implemented in a computationally tractable manner. We derive the Lagrange dual of the optimization problem and use strong duality to demonstrate that a biochemical analogue of Tellegen's theorem holds at optimality. Each optimal flux is dependent on a free parameter that we relate to an elementary kinetic parameter when mass action kinetics is assumed. Copyright © 2011 Elsevier Ltd. All rights reserved.

  14. Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

    KAUST Repository

    Alzahrani, Majed A.

    2017-01-01

    for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large

  15. Uncovering buffered pleiotropy: a genome-scale screen for mel-28 genetic interactors in Caenorhabditis elegans.

    Science.gov (United States)

    Fernandez, Anita G; Mis, Emily K; Lai, Allison; Mauro, Michael; Quental, Angela; Bock, Carly; Piano, Fabio

    2014-01-10

    mel-28 (maternal-effect-lethal-28) encodes a conserved protein required for nuclear envelope function and chromosome segregation in Caenorhabditis elegans. Because mel-28 is a strict maternal-effect lethal gene, its function is required in the early embryo but appears to be dispensable for larval development. We wanted to test the idea that mel-28 has postembryonic roles that are buffered by the contributions of other genes. To find genes that act coordinately with mel-28, we did an RNA interference-based genetic interaction screen using mel-28 and wild-type larvae. We screened 18,364 clones and identified 65 genes that cause sterility in mel-28 but not wild-type worms. Some of these genes encode components of the nuclear pore. In addition we identified genes involved in dynein and dynactin function, vesicle transport, and cell-matrix attachments. By screening mel-28 larvae we have bypassed the requirement for mel-28 in the embryo, uncovering pleiotropic functions for mel-28 later in development that are normally provided by other genes. This work contributes toward revealing the gene networks that underlie cellular processes and reveals roles for a maternal-effect lethal gene later in development.

  16. A phylum-level phylogenetic classification of zygomycete fungi based on genome-scale data

    Science.gov (United States)

    Zygomycete fungi were classified as a single phylum, Zygomycota, based on sexual reproduction by zygospores, frequent asexual reproduction by sporangia, absence of multicellular sporocarps, and production of coenocytic hyphae, all with some exceptions. Molecular phylogenies based on one or a few gen...

  17. RRW: repeated random walks on genome-scale protein networks for local cluster discovery

    Directory of Open Access Journals (Sweden)

    Can Tolga

    2009-09-01

    Full Text Available Abstract Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL, and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters.

  18. Genome-scale prediction of proteins with long intrinsically disordered regions.

    Science.gov (United States)

    Peng, Zhenling; Mizianty, Marcin J; Kurgan, Lukasz

    2014-01-01

    Proteins with long disordered regions (LDRs), defined as having 30 or more consecutive disordered residues, are abundant in eukaryotes, and these regions are recognized as a distinct class of biologically functional domains. LDRs facilitate various cellular functions and are important for target selection in structural genomics. Motivated by the lack of methods that directly predict proteins with LDRs, we designed Super-fast predictor of proteins with Long Intrinsically DisordERed regions (SLIDER). SLIDER utilizes logistic regression that takes an empirically chosen set of numerical features, which consider selected physicochemical properties of amino acids, sequence complexity, and amino acid composition, as its inputs. Empirical tests show that SLIDER offers competitive predictive performance combined with low computational cost. It outperforms, by at least a modest margin, a comprehensive set of modern disorder predictors (that can indirectly predict LDRs) and is 16 times faster compared to the best currently available disorder predictor. Utilizing our time-efficient predictor, we characterized abundance and functional roles of proteins with LDRs over 110 eukaryotic proteomes. Similar to related studies, we found that eukaryotes have many (on average 30.3%) proteins with LDRs with majority of proteomes having between 25 and 40%, where higher abundance is characteristic to proteomes that have larger proteins. Our first-of-its-kind large-scale functional analysis shows that these proteins are enriched in a number of cellular functions and processes including certain binding events, regulation of catalytic activities, cellular component organization, biogenesis, biological regulation, and some metabolic and developmental processes. A webserver that implements SLIDER is available at http://biomine.ece.ualberta.ca/SLIDER/. Copyright © 2013 Wiley Periodicals, Inc.

  19. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex.

    Science.gov (United States)

    Konermann, Silvana; Brigham, Mark D; Trevino, Alexandro E; Joung, Julia; Abudayyeh, Omar O; Barcena, Clea; Hsu, Patrick D; Habib, Naomi; Gootenberg, Jonathan S; Nishimasu, Hiroshi; Nureki, Osamu; Zhang, Feng

    2015-01-29

    Systematic interrogation of gene function requires the ability to perturb gene expression in a robust and generalizable manner. Here we describe structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci. We used these engineered Cas9 activation complexes to investigate single-guide RNA (sgRNA) targeting rules for effective transcriptional activation, to demonstrate multiplexed activation of ten genes simultaneously, and to upregulate long intergenic non-coding RNA (lincRNA) transcripts. We also synthesized a library consisting of 70,290 guides targeting all human RefSeq coding isoforms to screen for genes that, upon activation, confer resistance to a BRAF inhibitor. The top hits included genes previously shown to be able to confer resistance, and novel candidates were validated using individual sgRNA and complementary DNA overexpression. A gene expression signature based on the top screening hits correlated with markers of BRAF inhibitor resistance in cell lines and patient-derived samples. These results collectively demonstrate the potential of Cas9-based activators as a powerful genetic perturbation technology.

  20. Researchers use Modified CRISPR Systems to Modulate Gene Expression on a Genomic Scale

    Science.gov (United States)

    Cancer Target Discovery and Development Network (CTD2) researchers at the University of California, San Francisco, developed a CRISPR system that can regulate both gene repression and activation with fewer off-target effects.

  1. A Genome-Scale Resource for the Functional Characterization of Arabidopsis Transcription Factors

    Directory of Open Access Journals (Sweden)

    Jose L. Pruneda-Paz

    2014-07-01

    Full Text Available Extensive transcriptional networks play major roles in cellular and organismal functions. Transcript levels are in part determined by the combinatorial and overlapping functions of multiple transcription factors (TFs bound to gene promoters. Thus, TF-promoter interactions provide the basic molecular wiring of transcriptional regulatory networks. In plants, discovery of the functional roles of TFs is limited by an increased complexity of network circuitry due to a significant expansion of TF families. Here, we present the construction of a comprehensive collection of Arabidopsis TFs clones created to provide a versatile resource for uncovering TF biological functions. We leveraged this collection by implementing a high-throughput DNA binding assay and identified direct regulators of a key clock gene (CCA1 that provide molecular links between different signaling modules and the circadian clock. The resources introduced in this work will significantly contribute to a better understanding of the transcriptional regulatory landscape of plant genomes.

  2. Genome-scale detection of positive selection in nine primates predicts human-virus evolutionary conflicts.

    Science.gov (United States)

    van der Lee, Robin; Wiel, Laurens; van Dam, Teunis J P; Huynen, Martijn A

    2017-10-13

    Hotspots of rapid genome evolution hold clues about human adaptation. We present a comparative analysis of nine whole-genome sequenced primates to identify high-confidence targets of positive selection. We find strong statistical evidence for positive selection in 331 protein-coding genes (3%), pinpointing 934 adaptively evolving codons (0.014%). Our new procedure is stringent and reveals substantial artefacts (20% of initial predictions) that have inflated previous estimates. The final 331 positively selected genes (PSG) are strongly enriched for innate and adaptive immunity, secreted and cell membrane proteins (e.g. pattern recognition, complement, cytokines, immune receptors, MHC, Siglecs). We also find evidence for positive selection in reproduction and chromosome segregation (e.g. centromere-associated CENPO, CENPT), apolipoproteins, smell/taste receptors and mitochondrial proteins. Focusing on the virus-host interaction, we retrieve most evolutionary conflicts known to influence antiviral activity (e.g. TRIM5, MAVS, SAMHD1, tetherin) and predict 70 novel cases through integration with virus-human interaction data. Protein structure analysis further identifies positive selection in the interaction interfaces between viruses and their cellular receptors (CD4-HIV; CD46-measles, adenoviruses; CD55-picornaviruses). Finally, primate PSG consistently show high sequence variation in human exomes, suggesting ongoing evolution. Our curated dataset of positive selection is a rich source for studying the genetics underlying human (antiviral) phenotypes. Procedures and data are available at https://github.com/robinvanderlee/positive-selection. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  3. Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.

    Science.gov (United States)

    Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

    2008-06-18

    Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology.

  4. Reconstructing the backbone of the Saccharomycotina yeast phylogeny using genome-scale data

    Science.gov (United States)

    Understanding the phylogenetic relationships among the yeasts of the subphylum Saccharomycotina is a prerequisite for understanding the evolution of their metabolisms and ecological lifestyles. In the last two decades, the use of rDNA and multi-locus data sets has greatly advanced our understanding ...

  5. Reconstructing the Backbone of the Saccharomycotina Yeast Phylogeny Using Genome-Scale Data

    Science.gov (United States)

    Shen, Xing-Xing; Zhou, Xiaofan; Kominek, Jacek; Kurtzman, Cletus P.; Hittinger, Chris Todd; Rokas, Antonis

    2016-01-01

    Understanding the phylogenetic relationships among the yeasts of the subphylum Saccharomycotina is a prerequisite for understanding the evolution of their metabolisms and ecological lifestyles. In the last two decades, the use of rDNA and multilocus data sets has greatly advanced our understanding of the yeast phylogeny, but many deep relationships remain unsupported. In contrast, phylogenomic analyses have involved relatively few taxa and lineages that were often selected with limited considerations for covering the breadth of yeast biodiversity. Here we used genome sequence data from 86 publicly available yeast genomes representing nine of the 11 known major lineages and 10 nonyeast fungal outgroups to generate a 1233-gene, 96-taxon data matrix. Species phylogenies reconstructed using two different methods (concatenation and coalescence) and two data matrices (amino acids or the first two codon positions) yielded identical and highly supported relationships between the nine major lineages. Aside from the lineage comprised by the family Pichiaceae, all other lineages were monophyletic. Most interrelationships among yeast species were robust across the two methods and data matrices. However, eight of the 93 internodes conflicted between analyses or data sets, including the placements of: the clade defined by species that have reassigned the CUG codon to encode serine, instead of leucine; the clade defined by a whole genome duplication; and the species Ascoidea rubescens. These phylogenomic analyses provide a robust roadmap for future comparative work across the yeast subphylum in the disciplines of taxonomy, molecular genetics, evolutionary biology, ecology, and biotechnology. To further this end, we have also provided a BLAST server to query the 86 Saccharomycotina genomes, which can be found at http://y1000plus.org/blast. PMID:27672114

  6. A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism

    DEFF Research Database (Denmark)

    Hefzi, Hooman; Ang, Kok Siong; Hanscho, Michael

    2016-01-01

    Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways...

  7. A genome scale RNAi screen identifies GLI1 as a novel gene regulating vorinostat sensitivity.

    Science.gov (United States)

    Falkenberg, K J; Newbold, A; Gould, C M; Luu, J; Trapani, J A; Matthews, G M; Simpson, K J; Johnstone, R W

    2016-07-01

    Vorinostat is an FDA-approved histone deacetylase inhibitor (HDACi) that has proven clinical success in some patients; however, it remains unclear why certain patients remain unresponsive to this agent and other HDACis. Constitutive STAT (signal transducer and activator of transcription) activation, overexpression of prosurvival Bcl-2 proteins and loss of HR23B have been identified as potential biomarkers of HDACi resistance; however, none have yet been used to aid the clinical utility of HDACi. Herein, we aimed to further elucidate vorinostat-resistance mechanisms through a functional genomics screen to identify novel genes that when knocked down by RNA interference (RNAi) sensitized cells to vorinostat-induced apoptosis. A synthetic lethal functional screen using a whole-genome protein-coding RNAi library was used to identify genes that when knocked down cooperated with vorinostat to induce tumor cell apoptosis in otherwise resistant cells. Through iterative screening, we identified 10 vorinostat-resistance candidate genes that sensitized specifically to vorinostat. One of these vorinostat-resistance genes was GLI1, an oncogene not previously known to regulate the activity of HDACi. Treatment of vorinostat-resistant cells with the GLI1 small-molecule inhibitor, GANT61, phenocopied the effect of GLI1 knockdown. The mechanism by which GLI1 loss of function sensitized tumor cells to vorinostat-induced apoptosis is at least in part through interactions with vorinostat to alter gene expression in a manner that favored apoptosis. Upon GLI1 knockdown and vorinostat treatment, BCL2L1 expression was repressed and overexpression of BCL2L1 inhibited GLI1-knockdown-mediated vorinostat sensitization. Taken together, we present the identification and characterization of GLI1 as a new HDACi resistance gene, providing a strong rationale for development of GLI1 inhibitors for clinical use in combination with HDACi therapy.

  8. Metabolic modeling of synthesis gas fermentation in bubble column reactors.

    Science.gov (United States)

    Chen, Jin; Gomez, Jose A; Höffner, Kai; Barton, Paul I; Henson, Michael A

    2015-01-01

    A promising route to renewable liquid fuels and chemicals is the fermentation of synthesis gas (syngas) streams to synthesize desired products such as ethanol and 2,3-butanediol. While commercial development of syngas fermentation technology is underway, an unmet need is the development of integrated metabolic and transport models for industrially relevant syngas bubble column reactors. We developed and evaluated a spatiotemporal metabolic model for bubble column reactors with the syngas fermenting bacterium Clostridium ljungdahlii as the microbial catalyst. Our modeling approach involved combining a genome-scale reconstruction of C. ljungdahlii metabolism with multiphase transport equations that govern convective and dispersive processes within the spatially varying column. The reactor model was spatially discretized to yield a large set of ordinary differential equations (ODEs) in time with embedded linear programs (LPs) and solved using the MATLAB based code DFBAlab. Simulations were performed to analyze the effects of important process and cellular parameters on key measures of reactor performance including ethanol titer, ethanol-to-acetate ratio, and CO and H2 conversions. Our computational study demonstrated that mathematical modeling provides a complementary tool to experimentation for understanding, predicting, and optimizing syngas fermentation reactors. These model predictions could guide future cellular and process engineering efforts aimed at alleviating bottlenecks to biochemical production in syngas bubble column reactors.

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

    Directory of Open Access Journals (Sweden)

    Kim Hyun

    2011-12-01

    Full Text Available Abstract Background 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. Results 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. Conclusions 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.

  10. 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.

  11. Metabolic engineering tools in model cyanobacteria.

    Science.gov (United States)

    Carroll, Austin L; Case, Anna E; Zhang, Angela; Atsumi, Shota

    2018-03-26

    Developing sustainable routes for producing chemicals and fuels is one of the most important challenges in metabolic engineering. Photoautotrophic hosts are particularly attractive because of their potential to utilize light as an energy source and CO 2 as a carbon substrate through photosynthesis. Cyanobacteria are unicellular organisms capable of photosynthesis and CO 2 fixation. While engineering in heterotrophs, such as Escherichia coli, has result in a plethora of tools for strain development and hosts capable of producing valuable chemicals efficiently, these techniques are not always directly transferable to cyanobacteria. However, recent efforts have led to an increase in the scope and scale of chemicals that cyanobacteria can produce. Adaptations of important metabolic engineering tools have also been optimized to function in photoautotrophic hosts, which include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9, 13 C Metabolic Flux Analysis (MFA), and Genome-Scale Modeling (GSM). This review explores innovations in cyanobacterial metabolic engineering, and highlights how photoautotrophic metabolism has shaped their development. Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  12. Applications of computational modeling in metabolic engineering of yeast.

    Science.gov (United States)

    Kerkhoven, Eduard J; Lahtvee, Petri-Jaan; Nielsen, Jens

    2015-02-01

    Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.

  13. Integrating cellular metabolism into a multiscale whole-body model.

    Directory of Open Access Journals (Sweden)

    Markus Krauss

    Full Text Available Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development.

  14. Integrating Cellular Metabolism into a Multiscale Whole-Body Model

    Science.gov (United States)

    Krauss, Markus; Schaller, Stephan; Borchers, Steffen; Findeisen, Rolf; Lippert, Jörg; Kuepfer, Lars

    2012-01-01

    Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development. PMID:23133351

  15. Context-Specific Metabolic Model Extraction Based on Regularized Least Squares Optimization.

    Directory of Open Access Journals (Sweden)

    Semidán Robaina Estévez

    Full Text Available Genome-scale metabolic models have proven highly valuable in investigating cell physiology. Recent advances include the development of methods to extract context-specific models capable of describing metabolism under more specific scenarios (e.g., cell types. Yet, none of the existing computational approaches allows for a fully automated model extraction and determination of a flux distribution independent of user-defined parameters. Here we present RegrEx, a fully automated approach that relies solely on context-specific data and ℓ1-norm regularization to extract a context-specific model and to provide a flux distribution that maximizes its correlation to data. Moreover, the publically available implementation of RegrEx was used to extract 11 context-specific human models using publicly available RNAseq expression profiles, Recon1 and also Recon2, the most recent human metabolic model. The comparison of the performance of RegrEx and its contending alternatives demonstrates that the proposed method extracts models for which both the structure, i.e., reactions included, and the flux distributions are in concordance with the employed data. These findings are supported by validation and comparison of method performance on additional data not used in context-specific model extraction. Therefore, our study sets the ground for applications of other regularization techniques in large-scale metabolic modeling.

  16. A constraint-based bottom-up counterpart to definite clause grammars

    DEFF Research Database (Denmark)

    Christiansen, Henning

    2004-01-01

    A new grammar formalism, CHR Grammars (CHRG), is proposed that provides a constraint-solving approach to language analysis, built on top of the programming language of Constraint Handling Rules in the same way as Definite Clause Grammars (DCG) on Prolog. CHRG works bottom-up and adds the following......, integrity constraints, operators a la assumption grammars, and to incorporate other constraint solvers. (iv)~Context-sensitive rules that apply for disambiguation, coordination in natural language and tagger-like rules....

  17. Investigating Constraint-Based Approaches for the Development of Agile Plans

    Science.gov (United States)

    2014-06-01

    Company Cbt Tm will have to clear two routes, namely DIAM along A log team Plato Route know 4.2. The f synch are p tasks interv availa links...tactical sp ration estim (a) (b) sks that are relat ission end is delay , which  is deemed t occurrence.  In  t ilitary and ts to check, a

  18. Normal and friction stabilization techniques for interactive rigid body constraint-based contact force computations

    DEFF Research Database (Denmark)

    Silcowitz-Hansen, Morten; Abel, Sarah Maria Niebe; Erleben, Kenny

    2010-01-01

    We present a novel, yet simple, method for stabilization of normal forces. A normal stabilization term, carefully designed from hypotheses about interactive usability, is added to the contact force problem. Further, we propose friction stabilization as a completely new stabilization paradigm...

  19. Affine-Invariant Geometric Constraints-Based High Accuracy Simultaneous Localization and Mapping

    Directory of Open Access Journals (Sweden)

    Gangchen Hua

    2017-01-01

    Full Text Available In this study we describe a new appearance-based loop-closure detection method for online incremental simultaneous localization and mapping (SLAM using affine-invariant-based geometric constraints. Unlike other pure bag-of-words-based approaches, our proposed method uses geometric constraints as a supplement to improve accuracy. By establishing an affine-invariant hypothesis, the proposed method excludes incorrect visual words and calculates the dispersion of correctly matched visual words to improve the accuracy of the likelihood calculation. In addition, camera’s intrinsic parameters and distortion coefficients are adequate for this method. 3D measuring is not necessary. We use the mechanism of Long-Term Memory and Working Memory (WM to manage the memory. Only a limited size of the WM is used for loop-closure detection; therefore the proposed method is suitable for large-scale real-time SLAM. We tested our method using the CityCenter and Lip6Indoor datasets. Our proposed method results can effectively correct the typical false-positive localization of previous methods, thus gaining better recall ratios and better precision.

  20. Constraint-based query distribution framework for an integrated global schema

    DEFF Research Database (Denmark)

    Malik, Ahmad Kamran; Qadir, Muhammad Abdul; Iftikhar, Nadeem

    2009-01-01

    and replicated data sources. The provided system is all XML-based which poses query in XML form, transforms, and integrates local results in an XML document. Contributions include the use of constraints in our existing global schema which help in source selection and query optimization, and a global query...

  1. C-mixture and multi-constraints based genetic algorithm for collaborative data publishing

    Directory of Open Access Journals (Sweden)

    Yogesh R. Kulkarni

    2018-04-01

    Full Text Available Due to increasing need of using distributed databases, high demand presents on sharing data to easily update and access the useful information without any interruption. The sharing of distributed databases causes a serious issue of securing information since the databases consist of sensitive personal information. To preserve the sensitive information and at the same time, releasing the useful information, a significant effort is made by the researchers under privacy preserving data publishing that have been receiving considerable attention in recent years. In this work, a new privacy measure, called c-mixture is introduced to maintain the privacy constraint without affecting utility of the database. In order to apply the proposed privacy measure to privacy preserving data publishing, a new algorithm called, CPGEN is developed using genetic algorithm and multi-objective constraints. The proposed multi-objective optimization considered the multiple privacy constraints along with the utility measurement to measure the importance. Also, the proposed CPGEN is adapted to handle the cold-start problem which commonly happened in distributed databases. The proposed algorithm is experimented with adult dataset and quantitative performance is analyzed using generalized information loss and average equivalence class size metric. From the experimentation, we proved that the proposed algorithm maintained the privacy and utility as compared with the existing algorithm. Keywords: Privacy, Utility, Distributed databases, Data publishing, Optimization, Sensitive information

  2. Iguana production in Central America: prospects and constraints based on stakeholders' perceptions.

    NARCIS (Netherlands)

    Eilers, C.H.A.M.; Koops, W.J.; Udo, H.M.J.; Keulen, van H.; Noordhuizen, J.P.T.M.

    2001-01-01

    Iguana production in Nicaragua, Costa Rica and Panama is a complex system with various stakeholders. In order to explore the opinions of stakeholders about iguana production, the system was discussed with two stakeholder groups: Farmers and Organizations. Organizations consisted of officials from

  3. Constraint-Muse: A Soft-Constraint Based System for Music Therapy

    Science.gov (United States)

    Hölzl, Matthias; Denker, Grit; Meier, Max; Wirsing, Martin

    Monoidal soft constraints are a versatile formalism for specifying and solving multi-criteria optimization problems with dynamically changing user preferences. We have developed a prototype tool for interactive music creation, called Constraint Muse, that uses monoidal soft constraints to ensure that a dynamically generated melody harmonizes with input from other sources. Constraint Muse provides an easy to use interface based on Nintendo Wii controllers and is intended to be used in music therapy for people with Parkinson’s disease and for children with high-functioning autism or Asperger’s syndrome.

  4. Timing Constraints Based High Performance Des Design And Implementation On 28nm FPGA

    DEFF Research Database (Denmark)

    Thind, Vandana; Pandey, Sujeet; Hussain, Dil muhammed Akbar

    2018-01-01

    in this work, we are going to implement DES Algorithm on 28nm Artix-7 FPGA. To achieve high performance design goal, we are using minimum period, maximum frequency, minimum low pulse, minimum high pulse for different cases of worst case slack, maximum delay, setup time, hold time and data skew path....... The cases on which analysis is done are like worst case slack, best case achievable, timing error and timing score, which help in differentiating the amount of timing constraint at two different frequencies. We analyzed that in timing analysis there is maximum of 19.56% of variation in worst case slack, 0...

  5. An Efficient Energy Constraint Based UAV Path Planning for Search and Coverage

    Directory of Open Access Journals (Sweden)

    German Gramajo

    2017-01-01

    Full Text Available A path planning strategy for a search and coverage mission for a small UAV that maximizes the area covered based on stored energy and maneuverability constraints is presented. The proposed formulation has a high level of autonomy, without requiring an exact choice of optimization parameters, and is appropriate for real-time implementation. The computed trajectory maximizes spatial coverage while closely satisfying terminal constraints on the position of the vehicle and minimizing the time of flight. Comparisons of this formulation to a path planning algorithm based on those with time constraint show equivalent coverage performance but improvement in prediction of overall mission duration and accuracy of the terminal position of the vehicle.

  6. An Efficient Energy Constraint Based UAV Path Planning for Search and Coverage

    OpenAIRE

    Gramajo, German; Shankar, Praveen

    2017-01-01

    A path planning strategy for a search and coverage mission for a small UAV that maximizes the area covered based on stored energy and maneuverability constraints is presented. The proposed formulation has a high level of autonomy, without requiring an exact choice of optimization parameters, and is appropriate for real-time implementation. The computed trajectory maximizes spatial coverage while closely satisfying terminal constraints on the position of the vehicle and minimizing the time of ...

  7. Time constraint based on zircon dating for the Jacareacanga group (Tapajos province, Amazon craton, Brazil)

    International Nuclear Information System (INIS)

    Almeida, M.E.; Ferreira, A.L; Macambira, M.J.B.; Sachett, C.R

    2001-01-01

    During long time the Jacareacanga meta-volcano sedimentary sequence have been interpreted as Archean greenstone belt terrain. However, recent data are indicating younger U-Pb ages about 2.1 Ga. In the Tapajos Province (Amazon Craton), the Cuiu-Cuiu Complex (2.00-2.03 Ga), Creporizao granitoids (1.99-1.96 Ga) and Jacareacanga Group are the oldest rocks. The Jacareancaga Group is composed by quartzmica schists, quartzites, ferruginous quartzite, metachert, and minor talc-tremolite-chlorite schist, actinolite-epidote schist, hornfels, metargilites and metawackes metamorphosed in low to medium-grade conditions. The aim of the present paper is to estabilish the maximum age of Jacareacanga sedimentation and identify probable sources in Espirito Santo region (Espirito Santo muscovite-biotite schist). In this research, similar and new results are obtained by zircon evaporation methodology. This research shows geochronological data about the Espirito Santo muscovite-biotite schist related to Jacareacanga Group (Ferreira et al., 2000) in Tapajos Province (Amazon Craton). The area is located near Amazonas and Para States boundary (Northern Brazil) and the sample was obtained at Espirito Santo (garimpo) small-scale gold mine (06 o 00min.48seg.S,58 o 08min.17seg.W) (au)

  8. Construction and completion of flux balance models from pathway databases.

    Science.gov (United States)

    Latendresse, Mario; Krummenacker, Markus; Trupp, Miles; Karp, Peter D

    2012-02-01

    Flux balance analysis (FBA) is a well-known technique for genome-scale modeling of metabolic flux. Typically, an FBA formulation requires the accurate specification of four sets: biochemical reactions, biomass metabolites, nutrients and secreted metabolites. The development of FBA models can be time consuming and tedious because of the difficulty in assembling completely accurate descriptions of these sets, and in identifying errors in the composition of these sets. For example, the presence of a single non-producible metabolite in the biomass will make the entire model infeasible. Other difficulties in FBA modeling are that model distributions, and predicted fluxes, can be cryptic and difficult to understand. We present a multiple gap-filling method to accelerate the development of FBA models using a new tool, called MetaFlux, based on mixed integer linear programming (MILP). The method suggests corrections to the sets of reactions, biomass metabolites, nutrients and secretions. The method generates FBA models directly from Pathway/Genome Databases. Thus, FBA models developed in this framework are easily queried and visualized using the Pathway Tools software. Predicted fluxes are more easily comprehended by visualizing them on diagrams of individual metabolic pathways or of metabolic maps. MetaFlux can also remove redundant high-flux loops, solve FBA models once they are generated and model the effects of gene knockouts. MetaFlux has been validated through construction of FBA models for Escherichia coli and Homo sapiens. Pathway Tools with MetaFlux is freely available to academic users, and for a fee to commercial users. Download from: biocyc.org/download.shtml. mario.latendresse@sri.com Supplementary data are available at Bioinformatics online.

  9. The relative age effect in sport: a developmental systems model.

    Science.gov (United States)

    Wattie, Nick; Schorer, Jörg; Baker, Joseph

    2015-01-01

    The policies that dictate the participation structure of many youth sport systems involve the use of a set selection date (e.g. 31 December), which invariably produces relative age differences between those within the selection year (e.g. 1 January to 31 December). Those born early in the selection year (e.g. January) are relatively older—by as much as 12 months minus 1 day—than those born later in the selection year (e.g. December). Research in the area of sport has identified a number of significant developmental effects associated with such relative age differences. However, a theoretical framework that describes the breadth and complexity of relative age effects (RAEs) in sport does not exist in the literature. This paper reviews and summarizes the existing literature on relative age in sport, and proposes a constraints-based developmental systems model for RAEs in sport.

  10. Multiscale modeling for fluid transport in nanosystems.

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jonathan W.; Jones, Reese E.; Mandadapu, Kranthi Kiran; Templeton, Jeremy Alan; Zimmerman, Jonathan A.

    2013-09-01

    Atomistic-scale behavior drives performance in many micro- and nano-fluidic systems, such as mircrofludic mixers and electrical energy storage devices. Bringing this information into the traditionally continuum models used for engineering analysis has proved challenging. This work describes one such approach to address this issue by developing atomistic-to-continuum multi scale and multi physics methods to enable molecular dynamics (MD) representations of atoms to incorporated into continuum simulations. Coupling is achieved by imposing constraints based on fluxes of conserved quantities between the two regions described by one of these models. The impact of electric fields and surface charges are also critical, hence, methodologies to extend finite-element (FE) MD electric field solvers have been derived to account for these effects. Finally, the continuum description can have inconsistencies with the coarse-grained MD dynamics, so FE equations based on MD statistics were derived to facilitate the multi scale coupling. Examples are shown relevant to nanofluidic systems, such as pore flow, Couette flow, and electric double layer.

  11. An overview of bioinformatics methods for modeling biological pathways in yeast.

    Science.gov (United States)

    Hou, Jie; Acharya, Lipi; Zhu, Dongxiao; Cheng, Jianlin

    2016-03-01

    The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein-protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways inS. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  12. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

  13. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

  14. Predicate Argument Structure Analysis for Use Case Description Modeling

    Science.gov (United States)

    Takeuchi, Hironori; Nakamura, Taiga; Yamaguchi, Takahira

    In a large software system development project, many documents are prepared and updated frequently. In such a situation, support is needed for looking through these documents easily to identify inconsistencies and to maintain traceability. In this research, we focus on the requirements documents such as use cases and consider how to create models from the use case descriptions in unformatted text. In the model construction, we propose a few semantic constraints based on the features of the use cases and use them for a predicate argument structure analysis to assign semantic labels to actors and actions. With this approach, we show that we can assign semantic labels without enhancing any existing general lexical resources such as case frame dictionaries and design a less language-dependent model construction architecture. By using the constructed model, we consider a system for quality analysis of the use cases and automated test case generation to keep the traceability between document sets. We evaluated the reuse of the existing use cases and generated test case steps automatically with the proposed prototype system from real-world use cases in the development of a system using a packaged application. Based on the evaluation, we show how to construct models with high precision from English and Japanese use case data. Also, we could generate good test cases for about 90% of the real use cases through the manual improvement of the descriptions based on the feedback from the quality analysis system.

  15. Declarative event based models of concurrency and refinement in psi-calculi

    DEFF Research Database (Denmark)

    Normann, Håkon; Johansen, Christian; Hildebrandt, Thomas

    2015-01-01

    Psi-calculi constitute a parametric framework for nominal process calculi, where constraint based process calculi and process calculi for mobility can be defined as instances. We apply here the framework of psi-calculi to provide a foundation for the exploration of declarative event-based process...... calculi with support for run-time refinement. We first provide a representation of the model of finite prime event structures as an instance of psi-calculi and prove that the representation respects the semantics up to concurrency diamonds and action refinement. We then proceed to give a psi......-calculi representation of Dynamic Condition Response Graphs, which conservatively extends prime event structures to allow finite representations of (omega) regular finite (and infinite) behaviours and have been shown to support run-time adaptation and refinement. We end by outlining the final aim of this research, which...

  16. Genome-scale diversity and niche adaptation analysis of Lactococcus lactis by comparative genome hybridization using multi-strain arrays.

    NARCIS (Netherlands)

    Siezen, R.J.; Bayjanov, J.R.; Felis, G.E.; van der Sijde, M.R.; Starrenburg, M.; Molenaar, D.; Wels, M.; Hijum, S.A.; van Hylckama Vlieg, J.E.T.

    2011-01-01

    Lactococcus lactis produces lactic acid and is widely used in the manufacturing of various fermented dairy products. However, the species is also frequently isolated from non-dairy niches, such as fermented plant material. Recently, these non-dairy strains have gained increasing interest, as they

  17. Genome-Scale Analysis Reveals Sst2 as the Principal Regulator of Mating Pheromone Signaling in the Yeast Saccharomyces cerevisiae†

    Science.gov (United States)

    Chasse, Scott A.; Flanary, Paul; Parnell, Stephen C.; Hao, Nan; Cha, Jiyoung Y.; Siderovski, David P.; Dohlman, Henrik G.

    2006-01-01

    A common property of G protein-coupled receptors is that they become less responsive with prolonged stimulation. Regulators of G protein signaling (RGS proteins) are well known to accelerate G protein GTPase activity and do so by stabilizing the transition state conformation of the G protein α subunit. In the yeast Saccharomyces cerevisiae there are four RGS-homologous proteins (Sst2, Rgs2, Rax1, and Mdm1) and two Gα proteins (Gpa1 and Gpa2). We show that Sst2 is the only RGS protein that binds selectively to the transition state conformation of Gpa1. The other RGS proteins also bind Gpa1 and modulate pheromone signaling, but to a lesser extent and in a manner clearly distinct from Sst2. To identify other candidate pathway regulators, we compared pheromone responses in 4,349 gene deletion mutants representing nearly all nonessential genes in yeast. A number of mutants produced an increase (sst2, bar1, asc1, and ygl024w) or decrease (cla4) in pheromone sensitivity or resulted in pheromone-independent signaling (sst2, pbs2, gas1, and ygl024w). These findings suggest that Sst2 is the principal regulator of Gpa1-mediated signaling in vivo but that other proteins also contribute in distinct ways to pathway regulation. PMID:16467474

  18. SECOM: A novel hash seed and community detection based-approach for genome-scale protein domain identification

    KAUST Repository

    Fan, Ming

    2012-06-28

    With rapid advances in the development of DNA sequencing technologies, a plethora of high-throughput genome and proteome data from a diverse spectrum of organisms have been generated. The functional annotation and evolutionary history of proteins are usually inferred from domains predicted from the genome sequences. Traditional database-based domain prediction methods cannot identify novel domains, however, and alignment-based methods, which look for recurring segments in the proteome, are computationally demanding. Here, we propose a novel genome-wide domain prediction method, SECOM. Instead of conducting all-against-all sequence alignment, SECOM first indexes all the proteins in the genome by using a hash seed function. Local similarity can thus be detected and encoded into a graph structure, in which each node represents a protein sequence and each edge weight represents the shared hash seeds between the two nodes. SECOM then formulates the domain prediction problem as an overlapping community-finding problem in this graph. A backward graph percolation algorithm that efficiently identifies the domains is proposed. We tested SECOM on five recently sequenced genomes of aquatic animals. Our tests demonstrated that SECOM was able to identify most of the known domains identified by InterProScan. When compared with the alignment-based method, SECOM showed higher sensitivity in detecting putative novel domains, while it was also three orders of magnitude faster. For example, SECOM was able to predict a novel sponge-specific domain in nucleoside-triphosphatase (NTPases). Furthermore, SECOM discovered two novel domains, likely of bacterial origin, that are taxonomically restricted to sea anemone and hydra. SECOM is an open-source program and available at http://sfb.kaust.edu.sa/Pages/Software.aspx. © 2012 Fan et al.

  19. Genome-scale data reveal that endemic Poecilia populations from small sulphidic springs display no evidence of inbreeding.

    Science.gov (United States)

    Brown, Anthony P; Greenway, Ryan; Morgan, Samuel; Quackenbush, Corey R; Giordani, Luca; Arias-Rodriguez, Lenin; Tobler, Michael; Kelley, Joanna L

    2017-10-01

    Populations with limited ranges can be highly vulnerable to changes in their environment and are, thus, of high conservation concern. Populations that experience human-induced range reductions are often highly inbred and lack genetic diversity, but it is unknown whether this is also the case for populations with naturally small ranges. The fishes Poecilia sulphuraria (listed as critically endangered) and Poecilia thermalis, which are endemic to small hydrogen sulphide-rich springs in southern Mexico, are examples of such populations with inherently small habitats. We used geometric morphometrics and population genetics to quantify phenotypic and genetic variation within and among two populations of P. sulphuraria and one population of P. thermalis. Principal component analyses revealed phenotypic and genetic differences among the populations. Evidence for inbreeding was low compared to populations that have undergone habitat reduction. The genetic data were also used to infer the demographic history of these populations to obtain estimates for effective population sizes and migration rates. Effective population sizes were large given the small habitats of these populations. Our results imply that these three endemic extremophile populations should each be considered separately for conservation purposes. Additionally, this study suggests that populations in naturally small habitats may have lower rates of inbreeding and higher genetic diversity than expected, and therefore may be better equipped to handle environmental perturbations than anticipated. We caution, however, that the inferred lack of inbreeding and the large effective population sizes could potentially be a result of colonization by genetically diverse ancestors. © 2017 John Wiley & Sons Ltd.

  20. Meta-Analysis of Heterogeneous Data Sources for Genome-Scale Identification of Risk Genes in Complex Phenotypes

    DEFF Research Database (Denmark)

    Pers, Tune Hannes; Hansen, Niclas Tue; Hansen, Kasper Lage

    2011-01-01

    Meta‐analyses of large‐scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome‐wide association (GWA) studies, protein......) with an odds ratio of 1.28 [1.12–1.48], which replicates a previous case‐control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available...

  1. Genome-scale study of the importance of binding site context for transcription factor binding and gene regulation

    Directory of Open Access Journals (Sweden)

    Ronne Hans

    2008-11-01

    Full Text Available Abstract Background The rate of mRNA transcription is controlled by transcription factors that bind to specific DNA motifs in promoter regions upstream of protein coding genes. Recent results indicate that not only the presence of a motif but also motif context (for example the orientation of a motif or its location relative to the coding sequence is important for gene regulation. Results In this study we present ContextFinder, a tool that is specifically aimed at identifying cases where motif context is likely to affect gene regulation. We used ContextFinder to examine the role of motif context in S. cerevisiae both for DNA binding by transcription factors and for effects on gene expression. For DNA binding we found significant patterns of motif location bias, whereas motif orientations did not seem to matter. Motif context appears to affect gene expression even more than it affects DNA binding, as biases in both motif location and orientation were more frequent in promoters of co-expressed genes. We validated our results against data on nucleosome positioning, and found a negative correlation between preferred motif locations and nucleosome occupancy. Conclusion We conclude that the requirement for stable binding of transcription factors to DNA and their subsequent function in gene regulation can impose constraints on motif context.

  2. SECOM: A novel hash seed and community detection based-approach for genome-scale protein domain identification

    KAUST Repository

    Fan, Ming; Wong, Ka-Chun; Ryu, Tae Woo; Ravasi, Timothy; Gao, Xin

    2012-01-01

    With rapid advances in the development of DNA sequencing technologies, a plethora of high-throughput genome and proteome data from a diverse spectrum of organisms have been generated. The functional annotation and evolutionary history of proteins are usually inferred from domains predicted from the genome sequences. Traditional database-based domain prediction methods cannot identify novel domains, however, and alignment-based methods, which look for recurring segments in the proteome, are computationally demanding. Here, we propose a novel genome-wide domain prediction method, SECOM. Instead of conducting all-against-all sequence alignment, SECOM first indexes all the proteins in the genome by using a hash seed function. Local similarity can thus be detected and encoded into a graph structure, in which each node represents a protein sequence and each edge weight represents the shared hash seeds between the two nodes. SECOM then formulates the domain prediction problem as an overlapping community-finding problem in this graph. A backward graph percolation algorithm that efficiently identifies the domains is proposed. We tested SECOM on five recently sequenced genomes of aquatic animals. Our tests demonstrated that SECOM was able to identify most of the known domains identified by InterProScan. When compared with the alignment-based method, SECOM showed higher sensitivity in detecting putative novel domains, while it was also three orders of magnitude faster. For example, SECOM was able to predict a novel sponge-specific domain in nucleoside-triphosphatase (NTPases). Furthermore, SECOM discovered two novel domains, likely of bacterial origin, that are taxonomically restricted to sea anemone and hydra. SECOM is an open-source program and available at http://sfb.kaust.edu.sa/Pages/Software.aspx. © 2012 Fan et al.

  3. Researchers use Modified CRISPR Systems to Modulate Gene Expression on a Genomic Scale | Office of Cancer Genomics

    Science.gov (United States)

    The genetic engineering system, clustered regularly interspaced short palindromic repeats (CRISPR), has conventionally been used to inactivate genes by making targeted double stranded cuts in DNA. While CRISPR is a useful tool, it can only be used to create loss-of-function modifications and often causes off-target effects due to the disruptive mechanism by which it works. CTD2 researchers at the University of California, San Francisco recently addressed these shortcomings in a publication in Cell.

  4. Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment

    KAUST Repository

    Rapakoulia, Trisevgeni; Gao, Xin; Huang, Yi; de Hoon, Michiel; Okada-Hatakeyama, Mariko; Suzuki, Harukazu; Arner, Erik

    2017-01-01

    Motivation: Drug combination therapy for treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect, while reducing the likelihood of drug resistance. In order to reduce time and cost spent

  5. Cartilage-selective genes identified in genome-scale analysis of non-cartilage and cartilage gene expression

    Directory of Open Access Journals (Sweden)

    Cohn Zachary A

    2007-06-01

    Full Text Available Abstract Background Cartilage plays a fundamental role in the development of the human skeleton. Early in embryogenesis, mesenchymal cells condense and differentiate into chondrocytes to shape the early skeleton. Subsequently, the cartilage anlagen differentiate to form the growth plates, which are responsible for linear bone growth, and the articular chondrocytes, which facilitate joint function. However, despite the multiplicity of roles of cartilage during human fetal life, surprisingly little is known about its transcriptome. To address this, a whole genome microarray expression profile was generated using RNA isolated from 18–22 week human distal femur fetal cartilage and compared with a database of control normal human tissues aggregated at UCLA, termed Celsius. Results 161 cartilage-selective genes were identified, defined as genes significantly expressed in cartilage with low expression and little variation across a panel of 34 non-cartilage tissues. Among these 161 genes were cartilage-specific genes such as cartilage collagen genes and 25 genes which have been associated with skeletal phenotypes in humans and/or mice. Many of the other cartilage-selective genes do not have established roles in cartilage or are novel, unannotated genes. Quantitative RT-PCR confirmed the unique pattern of gene expression observed by microarray analysis. Conclusion Defining the gene expression pattern for cartilage has identified new genes that may contribute to human skeletogenesis as well as provided further candidate genes for skeletal dysplasias. The data suggest that fetal cartilage is a complex and transcriptionally active tissue and demonstrate that the set of genes selectively expressed in the tissue has been greatly underestimated.

  6. Multi-tissue computational modeling analyzes pathophysiology of type 2 diabetes in MKR mice.

    Directory of Open Access Journals (Sweden)

    Amit Kumar

    Full Text Available Computational models using metabolic reconstructions for in silico simulation of metabolic disorders such as type 2 diabetes mellitus (T2DM can provide a better understanding of disease pathophysiology and avoid high experimentation costs. There is a limited amount of computational work, using metabolic reconstructions, performed in this field for the better understanding of T2DM. In this study, a new algorithm for generating tissue-specific metabolic models is presented, along with the resulting multi-confidence level (MCL multi-tissue model. The effect of T2DM on liver, muscle, and fat in MKR mice was first studied by microarray analysis and subsequently the changes in gene expression of frank T2DM MKR mice versus healthy mice were applied to the multi-tissue model to test the effect. Using the first multi-tissue genome-scale model of all metabolic pathways in T2DM, we found out that branched-chain amino acids' degradation and fatty acids oxidation pathway is downregulated in T2DM MKR mice. Microarray data showed low expression of genes in MKR mice versus healthy mice in the degradation of branched-chain amino acids and fatty-acid oxidation pathways. In addition, the flux balance analysis using the MCL multi-tissue model showed that the degradation pathways of branched-chain amino acid and fatty acid oxidation were significantly downregulated in MKR mice versus healthy mice. Validation of the model was performed using data derived from the literature regarding T2DM. Microarray data was used in conjunction with the model to predict fluxes of various other metabolic pathways in the T2DM mouse model and alterations in a number of pathways were detected. The Type 2 Diabetes MCL multi-tissue model may explain the high level of branched-chain amino acids and free fatty acids in plasma of Type 2 Diabetic subjects from a metabolic fluxes perspective.

  7. Background based Gaussian mixture model lesion segmentation in PET

    Energy Technology Data Exchange (ETDEWEB)

    Soffientini, Chiara Dolores, E-mail: chiaradolores.soffientini@polimi.it; Baselli, Giuseppe [DEIB, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan 20133 (Italy); De Bernardi, Elisabetta [Department of Medicine and Surgery, Tecnomed Foundation, University of Milano—Bicocca, Monza 20900 (Italy); Zito, Felicia; Castellani, Massimo [Nuclear Medicine Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, Milan 20122 (Italy)

    2016-05-15

    Purpose: Quantitative {sup 18}F-fluorodeoxyglucose positron emission tomography is limited by the uncertainty in lesion delineation due to poor SNR, low resolution, and partial volume effects, subsequently impacting oncological assessment, treatment planning, and follow-up. The present work develops and validates a segmentation algorithm based on statistical clustering. The introduction of constraints based on background features and contiguity priors is expected to improve robustness vs clinical image characteristics such as lesion dimension, noise, and contrast level. Methods: An eight-class Gaussian mixture model (GMM) clustering algorithm was modified by constraining the mean and variance parameters of four background classes according to the previous analysis of a lesion-free background volume of interest (background modeling). Hence, expectation maximization operated only on the four classes dedicated to lesion detection. To favor the segmentation of connected objects, a further variant was introduced by inserting priors relevant to the classification of neighbors. The algorithm was applied to simulated datasets and acquired phantom data. Feasibility and robustness toward initialization were assessed on a clinical dataset manually contoured by two expert clinicians. Comparisons were performed with respect to a standard eight-class GMM algorithm and to four different state-of-the-art methods in terms of volume error (VE), Dice index, classification error (CE), and Hausdorff distance (HD). Results: The proposed GMM segmentation with background modeling outperformed standard GMM and all the other tested methods. Medians of accuracy indexes were VE <3%, Dice >0.88, CE <0.25, and HD <1.2 in simulations; VE <23%, Dice >0.74, CE <0.43, and HD <1.77 in phantom data. Robustness toward image statistic changes (±15%) was shown by the low index changes: <26% for VE, <17% for Dice, and <15% for CE. Finally, robustness toward the user-dependent volume initialization was

  8. MicrobesFlux: a web platform for drafting metabolic models from the KEGG database

    Directory of Open Access Journals (Sweden)

    Feng Xueyang

    2012-08-01

    Full Text Available Abstract Background Concurrent with the efforts currently underway in mapping microbial genomes using high-throughput sequencing methods, systems biologists are building metabolic models to characterize and predict cell metabolisms. One of the key steps in building a metabolic model is using multiple databases to collect and assemble essential information about genome-annotations and the architecture of the metabolic network for a specific organism. To speed up metabolic model development for a large number of microorganisms, we need a user-friendly platform to construct metabolic networks and to perform constraint-based flux balance analysis based on genome databases and experimental results. Results We have developed a semi-automatic, web-based platform (MicrobesFlux for generating and reconstructing metabolic models for annotated microorganisms. MicrobesFlux is able to automatically download the metabolic network (including enzymatic reactions and metabolites of ~1,200 species from the KEGG database (Kyoto Encyclopedia of Genes and Genomes and then convert it to a metabolic model draft. The platform also provides diverse customized tools, such as gene knockouts and the introduction of heterologous pathways, for users to reconstruct the model network. The reconstructed metabolic network can be formulated to a constraint-based flux model to predict and analyze the carbon fluxes in microbial metabolisms. The simulation results can be exported in the SBML format (The Systems Biology Markup Language. Furthermore, we also demonstrated the platform functionalities by developing an FBA model (including 229 reactions for a recent annotated bioethanol producer, Thermoanaerobacter sp. strain X514, to predict its biomass growth and ethanol production. Conclusion MicrobesFlux is an installation-free and open-source platform that enables biologists without prior programming knowledge to develop metabolic models for annotated microorganisms in the KEGG

  9. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R. [Division of Science and Math, New York University Abu Dhabi, Abu Dhabi (United Arab Emirates); Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi (United Arab Emirates); Jijakli, Kenan [Division of Science and Math, New York University Abu Dhabi, Abu Dhabi (United Arab Emirates); Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi (United Arab Emirates); Engineering Division, Biofinery, Manhattan, KS (United States); Salehi-Ashtiani, Kourosh, E-mail: ksa3@nyu.edu [Division of Science and Math, New York University Abu Dhabi, Abu Dhabi (United Arab Emirates); Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi (United Arab Emirates)

    2014-12-10

    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.

  11. Next-Gen3: Sequencing, Modeling, and Advanced Biofuels - Final Technical Report

    Energy Technology Data Exchange (ETDEWEB)

    Zengler, Karsten [Univ. of California, San Diego, CA (United States). Dept. of Pediatrics; Palsson, Bernhard [Univ. of California, San Diego, CA (United States). Dept. of Bioengineering; Lewis, Nathan [Univ. of California, San Diego, CA (United States). Dept. of Pediatrics

    2017-12-27

    Successful, scalable implementation of biofuels is dependent on the efficient and near complete utilization of diverse biomass sources. One approach is to utilize the large recalcitrant biomass fraction (or any organic waste stream) through the thermochemical conversion of organic compounds to syngas, a mixture of carbon monoxide (CO), carbon dioxide (CO2), and hydrogen (H2), which can subsequently be metabolized by acetogenic microorganisms to produce next-gen biofuels. The goal of this proposal was to advance the development of the acetogen Clostridium ljungdahlii as a chassis organism for next-gen biofuel production from cheap, renewable sources and to detail the interconnectivity of metabolism, energy conservation, and regulation of acetogens using next-gen sequencing and next-gen modeling. To achieve this goal we determined optimization of carbon and energy utilization through differential translational efficiency in C. ljungdahlii. Furthermore, we reconstructed a next-generation model of all major cellular processes, such as macromolecular synthesis and transcriptional regulation and deployed this model to predicting proteome allocation, overflow metabolism, and metal requirements in this model acetogen. In addition we explored the evolutionary significance of tRNA operon structure using the next-gen model and determined the optimal operon structure for bioproduction. Our study substantially enhanced the knowledgebaase for chemolithoautotrophs and their potential for advanced biofuel production. It provides next-generation modeling capability, offer innovative tools for genome-scale engineering, and provide novel methods to utilize next-generation models for the design of tunable systems that produce commodity chemicals from inexpensive sources.

  12. Characterization and identification of microRNA core promoters in four model species.

    Directory of Open Access Journals (Sweden)

    Xuefeng Zhou

    2007-03-01

    Full Text Available MicroRNAs are short, noncoding RNAs that play important roles in post-transcriptional gene regulation. Although many functions of microRNAs in plants and animals have been revealed in recent years, the transcriptional mechanism of microRNA genes is not well-understood. To elucidate the transcriptional regulation of microRNA genes, we study and characterize, in a genome scale, the promoters of intergenic microRNA genes in Caenorhabditis elegans, Homo sapiens, Arabidopsis thaliana, and Oryza sativa. We show that most known microRNA genes in these four species have the same type of promoters as protein-coding genes have. To further characterize the promoters of microRNA genes, we developed a novel promoter prediction method, called common query voting (CoVote, which is more effective than available promoter prediction methods. Using this new method, we identify putative core promoters of most known microRNA genes in the four model species. Moreover, we characterize the promoters of microRNA genes in these four species. We discover many significant, characteristic sequence motifs in these core promoters, several of which match or resemble the known cis-acting elements for transcription initiation. Among these motifs, some are conserved across different species while some are specific to microRNA genes of individual species.

  13. New types of experimental data shape the use of enzyme kinetics for dynamic network modeling.

    Science.gov (United States)

    Tummler, Katja; Lubitz, Timo; Schelker, Max; Klipp, Edda

    2014-01-01

    Since the publication of Leonor Michaelis and Maude Menten's paper on the reaction kinetics of the enzyme invertase in 1913, molecular biology has evolved tremendously. New measurement techniques allow in vivo characterization of the whole genome, proteome or transcriptome of cells, whereas the classical enzyme essay only allows determination of the two Michaelis-Menten parameters V and K(m). Nevertheless, Michaelis-Menten kinetics are still commonly used, not only in the in vitro context of enzyme characterization but also as a rate law for enzymatic reactions in larger biochemical reaction networks. In this review, we give an overview of the historical development of kinetic rate laws originating from Michaelis-Menten kinetics over the past 100 years. Furthermore, we briefly summarize the experimental techniques used for the characterization of enzymes, and discuss web resources that systematically store kinetic parameters and related information. Finally, describe the novel opportunities that arise from using these data in dynamic mathematical modeling. In this framework, traditional in vitro approaches may be combined with modern genome-scale measurements to foster thorough understanding of the underlying complex mechanisms. © 2013 FEBS.

  14. Technologies and Approaches to Elucidate and Model the Virulence Program of Salmonella.

    Energy Technology Data Exchange (ETDEWEB)

    McDermott, Jason E.; Yoon, Hyunjin; Nakayasu, Ernesto S.; Metz, Thomas O.; Hyduke, Daniel R.; Kidwai, Afshan S.; Palsson, Bernhard O.; Adkins, Joshua N.; Heffron, Fred

    2011-04-01

    Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated a large amount of data and driven development of computational approaches required for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird’s eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.

  15. Demographic modelling reveals a history of divergence with gene flow for a glacially tied stonefly in a changing post-Pleistocene landscape

    Science.gov (United States)

    Hotaling, Scott; Muhlfeld, Clint C.; Giersch, J. Joseph; Ali, Omar; Jordan, Steve; Miller, Michael R.; Luikart, Gordon; Weisrock, David W.

    2018-01-01

    AimClimate warming is causing extensive loss of glaciers in mountainous regions, yet our understanding of how glacial recession influences evolutionary processes and genetic diversity is limited. Linking genetic structure with the influences shaping it can improve understanding of how species respond to environmental change. Here, we used genome-scale data and demographic modelling to resolve the evolutionary history of Lednia tumana, a rare, aquatic insect endemic to alpine streams. We also employed a range of widely used data filtering approaches to quantify how they influenced population structure results.LocationAlpine streams in the Rocky Mountains of Glacier National Park, Montana, USA.TaxonLednia tumana, a stonefly (Order Plecoptera) in the family Nemouridae.MethodsWe generated single nucleotide polymorphism data through restriction-site associated DNA sequencing to assess contemporary patterns of genetic structure for 11 L. tumana populations. Using identified clusters, we assessed demographic history through model selection and parameter estimation in a coalescent framework. During population structure analyses, we filtered our data to assess the influence of singletons, missing data and total number of markers on results.ResultsContemporary patterns of population structure indicate that L. tumana exhibits a pattern of isolation-by-distance among populations within three genetic clusters that align with geography. Mean pairwise genetic differentiation (FST) among populations was 0.033. Coalescent-based demographic modelling supported divergence with gene flow among genetic clusters since the end of the Pleistocene (~13-17 kya), likely reflecting the south-to-north recession of ice sheets that accumulated during the Wisconsin glaciation.Main conclusionsWe identified a link between glacial retreat, evolutionary history and patterns of genetic diversity for a range-restricted stonefly imperiled by climate change. This finding included a history of

  16. Supervised variational model with statistical inference and its application in medical image segmentation.

    Science.gov (United States)

    Li, Changyang; Wang, Xiuying; Eberl, Stefan; Fulham, Michael; Yin, Yong; Dagan Feng, David

    2015-01-01

    Automated and general medical image segmentation can be challenging because the foreground and the background may have complicated and overlapping density distributions in medical imaging. Conventional region-based level set algorithms often assume piecewise constant or piecewise smooth for segments, which are implausible for general medical image segmentation. Furthermore, low contrast and noise make identification of the boundaries between foreground and background difficult for edge-based level set algorithms. Thus, to address these problems, we suggest a supervised variational level set segmentation model to harness the statistical region energy functional with a weighted probability approximation. Our approach models the region density distributions by using the mixture-of-mixtures Gaussian model to better approximate real intensity distributions and distinguish statistical intensity differences between foreground and background. The region-based statistical model in our algorithm can intuitively provide better performance on noisy images. We constructed a weighted probability map on graphs to incorporate spatial indications from user input with a contextual constraint based on the minimization of contextual graphs energy functional. We measured the performance of our approach on ten noisy synthetic images and 58 medical datasets with heterogeneous intensities and ill-defined boundaries and compared our technique to the Chan-Vese region-based level set model, the geodesic active contour model with distance regularization, and the random walker model. Our method consistently achieved the highest Dice similarity coefficient when compared to the other methods.

  17. Estimating Evapotranspiration from an Improved Two-Source Energy Balance Model Using ASTER Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Qifeng Zhuang

    2015-11-01

    Full Text Available Reliably estimating the turbulent fluxes of latent and sensible heat at the Earth’s surface by remote sensing is important for research on the terrestrial hydrological cycle. This paper presents a practical approach for mapping surface energy fluxes using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER images from an improved two-source energy balance (TSEB model. The original TSEB approach may overestimate latent heat flux under vegetative stress conditions, as has also been reported in recent research. We replaced the Priestley-Taylor equation used in the original TSEB model with one that uses plant moisture and temperature constraints based on the PT-JPL model to obtain a more accurate canopy latent heat flux for model solving. The collected ASTER data and field observations employed in this study are over corn fields in arid regions of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER area, China. The results were validated by measurements from eddy covariance (EC systems, and the surface energy flux estimates of the improved TSEB model are similar to the ground truth. A comparison of the results from the original and improved TSEB models indicates that the improved method more accurately estimates the sensible and latent heat fluxes, generating more precise daily evapotranspiration (ET estimate under vegetative stress conditions.

  18. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.

    Directory of Open Access Journals (Sweden)

    Caroline Colijn

    2009-08-01

    Full Text Available Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression, extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB. Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.

  19. Enhanced hexose fermentation by Saccharomyces cerevisiae through integration of stoichiometric modeling and genetic screening.

    Science.gov (United States)

    Quarterman, Josh; Kim, Soo Rin; Kim, Pan-Jun; Jin, Yong-Su

    2015-01-20

    In order to determine beneficial gene deletions for ethanol production by the yeast Saccharomyces cerevisiae, we performed an in silico gene deletion experiment based on a genome-scale metabolic model. Genes coding for two oxidative phosphorylation reactions (cytochrome c oxidase and ubiquinol cytochrome c reductase) were identified by the model-based simulation as potential deletion targets for enhancing ethanol production and maintaining acceptable overall growth rate in oxygen-limited conditions. Since the two target enzymes are composed of multiple subunits, we conducted a genetic screening study to evaluate the in silico results and compare the effect of deleting various portions of the respiratory enzyme complexes. Over two-thirds of the knockout mutants identified by the in silico study did exhibit experimental behavior in qualitative agreement with model predictions, but the exceptions illustrate the limitation of using a purely stoichiometric model-based approach. Furthermore, there was a substantial quantitative variation in phenotype among the various respiration-deficient mutants that were screened in this study, and three genes encoding respiratory enzyme subunits were identified as the best knockout targets for improving hexose fermentation in microaerobic conditions. Specifically, deletion of either COX9 or QCR9 resulted in higher ethanol production rates than the parental strain by 37% and 27%, respectively, with slight growth disadvantages. Also, deletion of QCR6 led to improved ethanol production rate by 24% with no growth disadvantage. The beneficial effects of these gene deletions were consistently demonstrated in different strain backgrounds and with four common hexoses. The combination of stoichiometric modeling and genetic screening using a systematic knockout collection was useful for narrowing a large set of gene targets and identifying targets of interest. Copyright © 2014 Elsevier B.V. All rights reserved.

  20. Towards Constraint-based High Performance Cloud System in the Process of Cloud Computing Adoption in an Organization

    OpenAIRE

    Simalango, Mikael Fernandus; Kang, Mun-Young; Oh, Sangyoon

    2010-01-01

    Cloud computing is penetrating into various domains and environments, from theoretical computer science to economy, from marketing hype to educational curriculum and from R&D lab to enterprise IT infrastructure. Yet, the currently developing state of cloud computing leaves several issues to address and also affects cloud computing adoption by organizations. In this paper, we explain how the transition into the cloud can occur in an organization and describe the mechanism for transforming lega...

  1. Selecting Measures to Evaluate Complex Sociotechnical Systems: An Empirical Comparison of a Task-based and Constraint-based Method

    Science.gov (United States)

    2013-07-01

    personnel selection, work methods, labour standards and an individual’s motivation to perform work. His work became less relevant as tasks became more...people were employed to do and was able to show that non-physical factors such as job satisfaction and the psychological states of workers contributed...all threats, flight conditions, consequences of their actions (for example, damaging the aircraft during a “hard” landing) and expressed satisfaction

  2. Multi-faceted tourist travel decisions : a constraint-based conceptual framework to describe tourists' sequential choices of travel components

    NARCIS (Netherlands)

    Dellaert, Benedict; Ettema, D.F.; Lindh, Christer

    This paper introduces a first step towards analyzing tourist travel choice in situations where tourists may: (i) temporally separate their choice of different components of the travel package, e.g. tourists may choose travel destination before accommodation, and (ii) face a structure of constraints

  3. Genetic Optimization Algorithm for Metabolic Engineering Revisited

    Directory of Open Access Journals (Sweden)

    Tobias B. Alter

    2018-05-01

    Full Text Available To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and the proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i multiple, non-linear engineering objectives; (ii the identification of gene target-sets according to logical gene-protein-reaction associations; (iii minimization of the number of network perturbations; and (iv the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

  4. 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.

  5. Connected Component Model for Multi-Object Tracking.

    Science.gov (United States)

    He, Zhenyu; Li, Xin; You, Xinge; Tao, Dacheng; Tang, Yuan Yan

    2016-08-01

    In multi-object tracking, it is critical to explore the data associations by exploiting the temporal information from a sequence of frames rather than the information from the adjacent two frames. Since straightforwardly obtaining data associations from multi-frames is an NP-hard multi-dimensional assignment (MDA) problem, most existing methods solve this MDA problem by either developing complicated approximate algorithms, or simplifying MDA as a 2D assignment problem based upon the information extracted only from adjacent frames. In this paper, we show that the relation between associations of two observations is the equivalence relation in the data association problem, based on the spatial-temporal constraint that the trajectories of different objects must be disjoint. Therefore, the MDA problem can be equivalently divided into independent subproblems by equivalence partitioning. In contrast to existing works for solving the MDA problem, we develop a connected component model (CCM) by exploiting the constraints of the data association and the equivalence relation on the constraints. Based upon CCM, we can efficiently obtain the global solution of the MDA problem for multi-object tracking by optimizing a sequence of independent data association subproblems. Experiments on challenging public data sets demonstrate that our algorithm outperforms the state-of-the-art approaches.

  6. Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states.

    Science.gov (United States)

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

    2017-02-28

    Bradyrhizobium diazoefficiens is a rhizobium able to convert atmospheric nitrogen into ammonium by establishing mutualistic symbiosis with soybean. It has been recognized as an important parent strain for microbial agents and is widely applied in agricultural and environmental fields. In order to study the metabolic properties of symbiotic nitrogen fixation and the differences between a free-living cell and a symbiotic bacteroid, a genome-scale metabolic network of B. diazoefficiens USDA110 was constructed and analyzed. The metabolic network, iYY1101, contains 1031 reactions, 661 metabolites, and 1101 genes in total. Metabolic models reflecting free-living and symbiotic states were determined by defining the corresponding objective functions and substrate input sets, and were further constrained by high-throughput transcriptomic and proteomic data. Constraint-based flux analysis was used to compare the metabolic capacities and the effects on the metabolic targets of genes and reactions between the two physiological states. The results showed that a free-living rhizobium possesses a steady state flux distribution for sustaining a complex supply of biomass precursors while a symbiotic bacteroid maintains a relatively condensed one adapted to nitrogen-fixation. Our metabolic models may serve as a promising platform for better understanding the symbiotic nitrogen fixation of this species.

  7. Metabolic Trade-offs between Biomass Synthesis and Photosynthate Export at Different Light Intensities in a Genome–Scale Metabolic Model of Rice

    Directory of Open Access Journals (Sweden)

    Mark Graham Poolman

    2014-11-01

    Full Text Available Previously we have used a genome scale model of rice metabolism to describe how metabolism reconfigures at different light intensities in an expanding leaf of rice. Although this established that the metabolism of the leaf was adequatelyrepresented, in the model, the scenario was not that of the typical function of the leaf --- to provide material for the rest of the plant. Here we extend our analysis to explore the transition to a source leaf as export of photosynthate increases at the expense of making leaf biomass precursors, again as a function of light intensity. In particular we investigate whether, when the leaf is making a smaller range of compounds for export to the phloem, the same changes occur in the interactions between mitochondrial and chloroplast metabolism as seen in biomass synthesis for growth when light intensity increases. Our results show that the same changes occur qualitatively, though there are slight quantitative differences reflecting differences in the energy and redox requirements for the different metabolic outputs.

  8. Modeling and Calibration of a Novel One-Mirror Galvanometric Laser Scanner

    Directory of Open Access Journals (Sweden)

    Chengyi Yu

    2017-01-01

    Full Text Available A laser stripe sensor has limited application when a point cloud of geometric samples on the surface of the object needs to be collected, so a galvanometric laser scanner is designed by using a one-mirror galvanometer element as its mechanical device to drive the laser stripe to sweep along the object. A novel mathematical model is derived for the proposed galvanometer laser scanner without any position assumptions and then a model-driven calibration procedure is proposed. Compared with available model-driven approaches, the influence of machining and assembly errors is considered in the proposed model. Meanwhile, a plane-constraint-based approach is proposed to extract a large number of calibration points effectively and accurately to calibrate the galvanometric laser scanner. Repeatability and accuracy of the galvanometric laser scanner are evaluated on the automobile production line to verify the efficiency and accuracy of the proposed calibration method. Experimental results show that the proposed calibration approach yields similar measurement performance compared with a look-up table calibration method.

  9. Three-dimensional model of a selective theophylline-binding RNA molecule

    Energy Technology Data Exchange (ETDEWEB)

    Tung, Chang-Shung; Oprea, T.I.; Hummer, G.; Garcia, A.E.

    1995-07-01

    We propose a three-dimensional (3D) model for an RNA molecule that selectively binds theophylline but not caffeine. This RNA, which was found using SELEX [Jenison, R.D., et al., Science (1994) 263:1425] is 10,000 times more specific for theophylline (Kd=320 nM) than for caffeine (Kd=3.5 mM), although the two ligands are identical except for a methyl group substituted at N7 (present only in caffeine). The binding affinity for ten xanthine-based ligands was used to derive a Comparative Molecular Field Analysis (CoMFA) model (R{sup 2} = 0.93 for 3 components, with cross-validated R{sup 2} of 0.73), using the SYBYL and GOLPE programs. A pharmacophoric map was generated to locate steric and electrostatic interactions between theophylline and the RNA binding site. This information was used to identify putative functional groups of the binding pocket and to generate distance constraints. Based on a model for the secondary structure (Jenison et al., idem), the 3D structure of this RNA was then generated using the following method: each helical region of the RNA molecule was treated as a rigid body; single-stranded loops with specific end-to-end distances were generated. The structures of RNA-xanthine complexes were studied using a modified Monte Carlo algorithm. The detailed structure of an RNA-ligand complex model, as well as possible explanations for the theophylline selectivity will be discussed.

  10. Solving a Novel Inventory Location Model with Stochastic Constraints and (R,s,S Inventory Control Policy

    Directory of Open Access Journals (Sweden)

    Guillermo Cabrera

    2013-01-01

    Full Text Available We solve a novel inventory-location model with a stochastic capacity constraint based on a periodic inventory control (ILM-PR policy. The ILM-PR policy implies several changes with regard to other previous models proposed in the literature, which consider continuous review as their inventory policy. One of these changes is the inclusion of the undershoot concept, which has not been considered in previous ILM models in the literature. Based on our model, we are able to design a distribution network for a two-level supply chain, addressing both warehouse location and customer assignment decisions, whilst taking into consideration several aspects of inventory planning, in particular, evaluating the impact of the inventory control review period on the network configuration and system costs. Because the model is a very hard-to solve combinatorial nonlinear optimisation problem, we implemented two heuristics to solve it, namely, Tabu Search and Particle Swarm Optimisation. These approaches were tested over small instances in which they were able to find the optimal solution in just a few seconds. Because the model is a new one, a set of medium-size instances is provided that can be useful as a benchmark in future research. The heuristics showed a good convergence rate when applied to those instances. The results confirm that decision making over the inventory control policy has effects on the distribution network design.

  11. Economics of membrane occupancy and respiro-fermentation

    Science.gov (United States)

    Zhuang, Kai; Vemuri, Goutham N; Mahadevan, Radhakrishnan

    2011-01-01

    The simultaneous utilization of efficient respiration and inefficient fermentation even in the presence of abundant oxygen is a puzzling phenomenon commonly observed in bacteria, yeasts, and cancer cells. Despite extensive research, the biochemical basis for this phenomenon remains obscure. We hypothesize that the outcome of a competition for membrane space between glucose transporters and respiratory chain (which we refer to as economics of membrane occupancy) proteins influences respiration and fermentation. By incorporating a sole constraint based on this concept in the genome-scale metabolic model of Escherichia coli, we were able to simulate respiro-fermentation. Further analysis of the impact of this constraint revealed differential utilization of the cytochromes and faster glucose uptake under anaerobic conditions than under aerobic conditions. Based on these simulations, we propose that bacterial cells manage the composition of their cytoplasmic membrane to maintain optimal ATP production by switching between oxidative and substrate-level phosphorylation. These results suggest that the membrane occupancy constraint may be a fundamental governing constraint of cellular metabolism and physiology, and establishes a direct link between cell morphology and physiology. PMID:21694717

  12. Capturing the essence of a metabolic network: a flux balance analysis approach.

    Science.gov (United States)

    Murabito, Ettore; Simeonidis, Evangelos; Smallbone, Kieran; Swinton, Jonathan

    2009-10-07

    As genome-scale metabolic reconstructions emerge, tools to manage their size and complexity will be increasingly important. Flux balance analysis (FBA) is a constraint-based approach widely used to study the metabolic capabilities of cellular or subcellular systems. FBA problems are highly underdetermined and many different phenotypes can satisfy any set of constraints through which the metabolic system is represented. Two of the main concerns in FBA are exploring the space of solutions for a given metabolic network and finding a specific phenotype which is representative for a given task such as maximal growth rate. Here, we introduce a recursive algorithm suitable for overcoming both of these concerns. The method proposed is able to find the alternate optimal patterns of active reactions of an FBA problem and identify the minimal subnetwork able to perform a specific task as optimally as the whole. Our method represents an alternative to and an extension of other approaches conceived for exploring the space of solutions of an FBA problem. It may also be particularly helpful in defining a scaffold of reactions upon which to build up a dynamic model, when the important pathways of the system have not yet been well-defined.

  13. Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks.

    Science.gov (United States)

    Xu, Zixiang; Sun, Jibin; Wu, Qiaqing; Zhu, Dunming

    2017-12-11

    Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can't be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

  14. Integrating tracer-based metabolomics data and metabolic fluxes in a linear fashion via Elementary Carbon Modes.

    Science.gov (United States)

    Pey, Jon; Rubio, Angel; Theodoropoulos, Constantinos; Cascante, Marta; Planes, Francisco J

    2012-07-01

    Constraints-based modeling is an emergent area in Systems Biology that includes an increasing set of methods for the analysis of metabolic networks. In order to refine its predictions, the development of novel methods integrating high-throughput experimental data is currently a key challenge in the field. In this paper, we present a novel set of constraints that integrate tracer-based metabolomics data from Isotope Labeling Experiments and metabolic fluxes in a linear fashion. These constraints are based on Elementary Carbon Modes (ECMs), a recently developed concept that generalizes Elementary Flux Modes at the carbon level. To illustrate the effect of our ECMs-based constraints, a Flux Variability Analysis approach was applied to a previously published metabolic network involving the main pathways in the metabolism of glucose. The addition of our ECMs-based constraints substantially reduced the under-determination resulting from a standard application of Flux Variability Analysis, which shows a clear progress over the state of the art. In addition, our approach is adjusted to deal with combinatorial explosion of ECMs in genome-scale metabolic networks. This extension was applied to infer the maximum biosynthetic capacity of non-essential amino acids in human metabolism. Finally, as linearity is the hallmark of our approach, its importance is discussed at a methodological, computational and theoretical level and illustrated with a practical application in the field of Isotope Labeling Experiments. Copyright © 2012 Elsevier Inc. All rights reserved.

  15. From Network Analysis to Functional Metabolic Modeling of the Human Gut Microbiota.

    Science.gov (United States)

    Bauer, Eugen; Thiele, Ines

    2018-01-01

    An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.

  16. Seismogenic Potential of a Gouge-filled Fault and the Criterion for Its Slip Stability: Constraints From a Microphysical Model

    Science.gov (United States)

    Chen, Jianye; Niemeijer, A. R.

    2017-12-01

    Physical constraints for the parameters of the rate-and-state friction (RSF) laws have been mostly lacking. We presented such constraints based on a microphysical model and demonstrated the general applicability to granular fault gouges deforming under hydrothermal conditions in a companion paper. In this paper, we examine the transition velocities for contrasting frictional behavior (i.e., strengthening to weakening and vice versa) and the slip stability of the model. The model predicts a steady state friction coefficient that increases with slip rate at very low and high slip rates and decreases in between. This allows the transition velocities to be theoretically obtained and the unstable slip regime (Vs→w static stress drop (Δμs) associated with self-sustained oscillations or stick slips. Numerical implementation of the model predicts frictional behavior that exhibits consecutive transitions from stable sliding, via periodic oscillations, to unstable stick slips with decreasing elastic stiffness or loading rate, and gives Kc, Wc, Δμs, Vs→w, and Vw→s values that are consistent with the analytical predictions. General scaling relations of these parameters given by the model are consistent with previous interpretations in the context of RSF laws and agree well with previous experiments, testifying to high validity. From these physics-based expressions that allow a more reliable extrapolation to natural conditions, we discuss the seismological implications for natural faults and present topics for future work.

  17. Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling.

    Science.gov (United States)

    Vijayakumar, Supreeta; Conway, Max; Lió, Pietro; Angione, Claudio

    2017-05-30

    Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  18. An inexact multi-objective dynamic model and its application in China for the management of municipal solid waste

    International Nuclear Information System (INIS)

    Su, J.; Xi, B.D.; Liu, H.L.; Jiang, Y.H.; Warith, M.A.

    2008-01-01

    With the development of science and technology, solid waste management has become a serious environmental problem for most communities all over the world. This paper presents a multi-objective optimization model for the management of municipal solid waste (MSW) via an uncertainty approach. In this model, the system cost of solid waste management and the environmental impact are considered as the main objectives, and some necessary constraints based on the characteristics of China are included; additionally, Pollution loss theory is applied to evaluate the environmental impact. This model is applied to the City of Fo Shan, China. Compared with the primary project of Fo Shan, which is provided by the government, the results of the optimization procedure show that the overall system cost could be reduced by $1-2.4/ton, i.e., $3.7 million/yr. The model presented in this paper was proven to be an effective response to the multi-objective solid waste management problem, and provides a new technique to optimize MSW management and operation. Why is the optimization result better? By analyzing the modelling with respect to function, constraints, and results, we conclude that the profit would be augmented due to the amount of the waste that would be treated by recycling technology, which would increase rapidly during the planning time; thus, the total system cost could be reduced accordingly

  19. Thermodynamic modelling and solar reactor design for syngas production through SCWG of algae

    Science.gov (United States)

    Venkataraman, Mahesh B.; Rahbari, Alireza; Pye, John

    2017-06-01

    Conversion of algal biomass into value added products, such as liquid fuels, using solar-assisted supercritical water gasification (SCWG) offers a promising approach for clean fuel production. SCWG has significant advantages over conventional gasification in terms of flexibility of feedstock, faster intrinsic kinetics and lower char formation. A relatively unexplored avenue in SCWG is the use of non-renewable source of energy for driving the endothermic gasification. The use of concentrated solar thermal to provide the process heat is attractive, especially in the case of expensive feedstocks such as algae. This study attempts to identify the key parameters and constraints in designing a solar cavity receiver/reactor for on-sun SCWG of algal biomass. A tubular plug-flow reactor, operating at 24 MPa and 400-600 °C with a solar input of 20MWth is modelled. Solar energy is utilized to increase the temperature of the reaction medium (10 wt.% algae solution) from 400 to 605 °C and simultaneously drive the gasification. The model additionally incorporates material constraints based on the allowable stresses for a commercially available Ni-based alloy (Inconel 625), and exergy accounting for the cavity reactor. A parametric evaluation of the steady state performance and quantification of the losses through wall conduction, external radiation and convection, internal convection, frictional pressure drop, mixing and chemical irreversibility, is presented.

  20. Forecasting craniofacial growth in individuals with class III malocclusion by computational modelling.

    Science.gov (United States)

    Auconi, Pietro; Scazzocchio, Marco; Defraia, Efisio; McNamara, James A; Franchi, Lorenzo

    2014-04-01

    To develop a mathematical model that adequately represented the pattern of craniofacial growth in class III subject consistently, with the goal of using this information to make growth predictions that could be amenable to longitudinal verification and clinical use. A combination of computational techniques (i.e. Fuzzy clustering and Network analysis) was applied to cephalometric data derived from 429 untreated growing female patients with class III malocclusion to visualize craniofacial growth dynamics and correlations. Four age groups of subjects were examined individually: from 7 to 9 years of age, from 10 to 12 years, from 13 to 14 years, and from 15 to 17 years. The connections between pathway components of class III craniofacial growth can be visualized from Network profiles. Fuzzy clustering analysis was able to define further growth patterns and coherences of the traditionally reported dentoskeletal characteristics of this structural imbalance. Craniofacial growth can be visualized as a biological, space-constraint-based optimization process; the prediction of individual growth trajectories depends on the rate of membership to a specific 'winner' cluster, i.e. on a specific individual growth strategy. The reliability of the information thus gained was tested to forecast craniofacial growth of 28 untreated female class III subjects followed longitudinally. The combination of Fuzzy clustering and Network algorithms allowed the development of principles for combining multiple auxological cephalometric features into a joint global model and to predict the individual risk of the facial pattern imbalance during growth.

  1. Modelling Practice

    DEFF Research Database (Denmark)

    Cameron, Ian; Gani, Rafiqul

    2011-01-01

    This chapter deals with the practicalities of building, testing, deploying and maintaining models. It gives specific advice for each phase of the modelling cycle. To do this, a modelling framework is introduced which covers: problem and model definition; model conceptualization; model data...... requirements; model construction; model solution; model verification; model validation and finally model deployment and maintenance. Within the adopted methodology, each step is discussedthrough the consideration of key issues and questions relevant to the modelling activity. Practical advice, based on many...

  2. Leadership Models.

    Science.gov (United States)

    Freeman, Thomas J.

    This paper discusses six different models of organizational structure and leadership, including the scalar chain or pyramid model, the continuum model, the grid model, the linking pin model, the contingency model, and the circle or democratic model. Each model is examined in a separate section that describes the model and its development, lists…

  3. The Genome-Based Metabolic Systems Engineering to Boost Levan Production in a Halophilic Bacterial Model.

    Science.gov (United States)

    Aydin, Busra; Ozer, Tugba; Oner, Ebru Toksoy; Arga, Kazim Yalcin

    2018-03-01

    Metabolic systems engineering is being used to redirect microbial metabolism for the overproduction of chemicals of interest with the aim of transforming microbial hosts into cellular factories. In this study, a genome-based metabolic systems engineering approach was designed and performed to improve biopolymer biosynthesis capability of a moderately halophilic bacterium Halomonas smyrnensis AAD6 T producing levan, which is a fructose homopolymer with many potential uses in various industries and medicine. For this purpose, the genome-scale metabolic model for AAD6 T was used to characterize the metabolic resource allocation, specifically to design metabolic engineering strategies for engineered bacteria with enhanced levan production capability. Simulations were performed in silico to determine optimal gene knockout strategies to develop new strains with enhanced levan production capability. The majority of the gene knockout strategies emphasized the vital role of the fructose uptake mechanism, and pointed out the fructose-specific phosphotransferase system (PTS fru ) as the most promising target for further metabolic engineering studies. Therefore, the PTS fru of AAD6 T was restructured with insertional mutagenesis and triparental mating techniques to construct a novel, engineered H. smyrnensis strain, BMA14. Fermentation experiments were carried out to demonstrate the high efficiency of the mutant strain BMA14 in terms of final levan concentration, sucrose consumption rate, and sucrose conversion efficiency, when compared to the AAD6 T . The genome-based metabolic systems engineering approach presented in this study might be considered an efficient framework to redirect microbial metabolism for the overproduction of chemicals of interest, and the novel strain BMA14 might be considered a potential microbial cell factory for further studies aimed to design levan production processes with lower production costs.

  4. Models and role models.

    Science.gov (United States)

    ten Cate, Jacob M

    2015-01-01

    Developing experimental models to understand dental caries has been the theme in our research group. Our first, the pH-cycling model, was developed to investigate the chemical reactions in enamel or dentine, which lead to dental caries. It aimed to leverage our understanding of the fluoride mode of action and was also utilized for the formulation of oral care products. In addition, we made use of intra-oral (in situ) models to study other features of the oral environment that drive the de/remineralization balance in individual patients. This model addressed basic questions, such as how enamel and dentine are affected by challenges in the oral cavity, as well as practical issues related to fluoride toothpaste efficacy. The observation that perhaps fluoride is not sufficiently potent to reduce dental caries in the present-day society triggered us to expand our knowledge in the bacterial aetiology of dental caries. For this we developed the Amsterdam Active Attachment biofilm model. Different from studies on planktonic ('single') bacteria, this biofilm model captures bacteria in a habitat similar to dental plaque. With data from the combination of these models, it should be possible to study separate processes which together may lead to dental caries. Also products and novel agents could be evaluated that interfere with either of the processes. Having these separate models in place, a suggestion is made to design computer models to encompass the available information. Models but also role models are of the utmost importance in bringing and guiding research and researchers. 2015 S. Karger AG, Basel

  5. Model(ing) Law

    DEFF Research Database (Denmark)

    Carlson, Kerstin

    The International Criminal Tribunal for the former Yugoslavia (ICTY) was the first and most celebrated of a wave of international criminal tribunals (ICTs) built in the 1990s designed to advance liberalism through international criminal law. Model(ing) Justice examines the case law of the ICTY...

  6. Models and role models

    NARCIS (Netherlands)

    ten Cate, J.M.

    2015-01-01

    Developing experimental models to understand dental caries has been the theme in our research group. Our first, the pH-cycling model, was developed to investigate the chemical reactions in enamel or dentine, which lead to dental caries. It aimed to leverage our understanding of the fluoride mode of

  7. Computational Modeling of Fluctuations in Energy and Metabolic Pathways of Methanogenic Archaea

    Energy Technology Data Exchange (ETDEWEB)

    Luthey-Schulten, Zaida [Univ. of Illinois, Urbana-Champaign, IL (United States). Dept. of Chemistry; Carl R. Woese Inst. for Genomic Biology

    2017-01-04

    The methanogenic archaea, anaerobic microbes that convert CO2 and H2 and/or other small organic fermentation products into methane, play an unusually large role in the global carbon cycle. As they perform the final step in the anaerobic breakdown of biomass, methanogens are a biogenic source of an estimated one billion tons methane each year. Depending on the location, produced methane can be considered as either a greenhouse gas (agricultural byproduct), sequestered carbon storage (methane hydrate deposits), or a potential energy source (organic wastewater treatment). These microbes therefore represent an important target for biotechnology applications. Computational models of methanogens with predictive power are useful aids in the adaptation of methanogenic systems, but need to connect processes of wide-ranging time and length scales. In this project, we developed several computational methodologies for modeling the dynamic behavior of entire cells that connects stochastic reaction-diffusion dynamics of individual biochemical pathways with genome-scale modeling of metabolic networks. While each of these techniques were in the realm of well-defined computational methods, here we integrated them to develop several entirely new approaches to systems biology. The first scientific aim of the project was to model how noise in a biochemical pathway propagates into cellular phenotypes. Genetic circuits have been optimized by evolution to regulate molecular processes despite stochastic noise, but the effect of such noise on a cellular biochemical networks is currently unknown. An integrated stochastic/systems model of Escherichia coli species was created to analyze how noise in protein expression gives—and therefore noise in metabolic fluxes—gives rise to multiple cellular phenotype in isogenic population. After the initial work developing and validating methods that allow characterization of the heterogeneity in the model organism E. coli, the project shifted toward

  8. ELMs and constraints on the H-mode pedestal: A model based on peeling-ballooning modes

    International Nuclear Information System (INIS)

    Snyder, P.B.; Ferron, J.R.; Wilson, H.R.

    2003-01-01

    We propose a model for Edge Localized Modes (ELMs) and pedestal constraint based upon theoretical analysis of instabilities which can limit the pedestal height and drive ELMs. The sharp pressure gradients, and resulting bootstrap current, in the pedestal region provide free energy to drive peeling and ballooning modes. The interaction of peeling-ballooning coupling, ballooning mode second stability, and finite-Larmor-radius effects results in coupled peeling-ballooning modes of intermediate wavelength generally being the limiting instability. A highly efficient new MHD code, ELITE, is used to calculate quantitative stability constraints on the pedestal, including con straits on the pedestal height. Because of the impact of collisionality on the bootstrap current, these pedestal constraints are dependant on the density and temperature separately, rather than simply on the pressure. A model of various ELM types is developed, and quantitatively compared to data. A number of observations agree with predictions, including ELM onset times, ELM depth and variation in pedestal height with collisionality and discharge shape. Stability analysis of series of model equilibria are used both o predict and interpret pedestal trends in existing experiments and to project pedestal constraints for future burning plasma tokamak designs. (author)

  9. Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks

    Science.gov (United States)

    Chiappino-Pepe, Anush; Ataman, Meriç

    2017-01-01

    Novel antimalarial therapies are urgently needed for the fight against drug-resistant parasites. The metabolism of malaria parasites in infected cells is an attractive source of drug targets but is rather complex. Computational methods can handle this complexity and allow integrative analyses of cell metabolism. In this study, we present a genome-scale metabolic model (iPfa) of the deadliest malaria parasite, Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA). Using previous absolute concentration data of the intraerythrocytic parasite, we applied TFA to iPfa and predicted up to 63 essential genes and 26 essential pairs of genes. Of the 63 genes, 35 have been experimentally validated and reported in the literature, and 28 have not been experimentally tested and include previously hypothesized or novel predictions of essential metabolic capabilities. Without metabolomics data, four of the genes would have been incorrectly predicted to be non-essential. TFA also indicated that substrate channeling should exist in two metabolic pathways to ensure the thermodynamic feasibility of the flux. Finally, analysis of the metabolic capabilities of P. falciparum led to the identification of both the minimal nutritional requirements and the genes that can become indispensable upon substrate inaccessibility. This model provides novel insight into the metabolic needs and capabilities of the malaria parasite and highlights metabolites and pathways that should be measured and characterized to identify potential thermodynamic bottlenecks and substrate channeling. The hypotheses presented seek to guide experimental studies to facilitate a better understanding of the parasite metabolism and the identification of targets for more efficient intervention. PMID:28333921

  10. Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks.

    Directory of Open Access Journals (Sweden)

    Anush Chiappino-Pepe

    2017-03-01

    Full Text Available Novel antimalarial therapies are urgently needed for the fight against drug-resistant parasites. The metabolism of malaria parasites in infected cells is an attractive source of drug targets but is rather complex. Computational methods can handle this complexity and allow integrative analyses of cell metabolism. In this study, we present a genome-scale metabolic model (iPfa of the deadliest malaria parasite, Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA. Using previous absolute concentration data of the intraerythrocytic parasite, we applied TFA to iPfa and predicted up to 63 essential genes and 26 essential pairs of genes. Of the 63 genes, 35 have been experimentally validated and reported in the literature, and 28 have not been experimentally tested and include previously hypothesized or novel predictions of essential metabolic capabilities. Without metabolomics data, four of the genes would have been incorrectly predicted to be non-essential. TFA also indicated that substrate channeling should exist in two metabolic pathways to ensure the thermodynamic feasibility of the flux. Finally, analysis of the metabolic capabilities of P. falciparum led to the identification of both the minimal nutritional requirements and the genes that can become indispensable upon substrate inaccessibility. This model provides novel insight into the metabolic needs and capabilities of the malaria parasite and highlights metabolites and pathways that should be measured and characterized to identify potential thermodynamic bottlenecks and substrate channeling. The hypotheses presented seek to guide experimental studies to facilitate a better understanding of the parasite metabolism and the identification of targets for more efficient intervention.

  11. Energy metabolism and glutamate-glutamine cycle in the brain: a stoichiometric modeling perspective.

    Science.gov (United States)

    Massucci, Francesco A; DiNuzzo, Mauro; Giove, Federico; Maraviglia, Bruno; Castillo, Isaac Perez; Marinari, Enzo; De Martino, Andrea

    2013-10-10

    The energetics of cerebral activity critically relies on the functional and metabolic interactions between neurons and astrocytes. Important open questions include the relation between neuronal versus astrocytic energy demand, glucose uptake and intercellular lactate transfer, as well as their dependence on the level of activity. We have developed a large-scale, constraint-based network model of the metabolic partnership between astrocytes and glutamatergic neurons that allows for a quantitative appraisal of the extent to which stoichiometry alone drives the energetics of the system. We find that the velocity of the glutamate-glutamine cycle (Vcyc) explains part of the uncoupling between glucose and oxygen utilization at increasing Vcyc levels. Thus, we are able to characterize different activation states in terms of the tissue oxygen-glucose index (OGI). Calculations show that glucose is taken up and metabolized according to cellular energy requirements, and that partitioning of the sugar between different cell types is not significantly affected by Vcyc. Furthermore, both the direction and magnitude of the lactate shuttle between neurons and astrocytes turn out to depend on the relative cell glucose uptake while being roughly independent of Vcyc. These findings suggest that, in absence of ad hoc activity-related constraints on neuronal and astrocytic metabolism, the glutamate-glutamine cycle does not control the relative energy demand of neurons and astrocytes, and hence their glucose uptake and lactate exchange.

  12. Energy metabolism and glutamate-glutamine cycle in the brain: a stoichiometric modeling perspective

    Science.gov (United States)

    2013-01-01

    Background The energetics of cerebral activity critically relies on the functional and metabolic interactions between neurons and astrocytes. Important open questions include the relation between neuronal versus astrocytic energy demand, glucose uptake and intercellular lactate transfer, as well as their dependence on the level of activity. Results We have developed a large-scale, constraint-based network model of the metabolic partnership between astrocytes and glutamatergic neurons that allows for a quantitative appraisal of the extent to which stoichiometry alone drives the energetics of the system. We find that the velocity of the glutamate-glutamine cycle (Vcyc) explains part of the uncoupling between glucose and oxygen utilization at increasing Vcyc levels. Thus, we are able to characterize different activation states in terms of the tissue oxygen-glucose index (OGI). Calculations show that glucose is taken up and metabolized according to cellular energy requirements, and that partitioning of the sugar between different cell types is not significantly affected by Vcyc. Furthermore, both the direction and magnitude of the lactate shuttle between neurons and astrocytes turn out to depend on the relative cell glucose uptake while being roughly independent of Vcyc. Conclusions These findings suggest that, in absence of ad hoc activity-related constraints on neuronal and astrocytic metabolism, the glutamate-glutamine cycle does not control the relative energy demand of neurons and astrocytes, and hence their glucose uptake and lactate exchange. PMID:24112710

  13. Modelling SDL, Modelling Languages

    Directory of Open Access Journals (Sweden)

    Michael Piefel

    2007-02-01

    Full Text Available Today's software systems are too complex to implement them and model them using only one language. As a result, modern software engineering uses different languages for different levels of abstraction and different system aspects. Thus to handle an increasing number of related or integrated languages is the most challenging task in the development of tools. We use object oriented metamodelling to describe languages. Object orientation allows us to derive abstract reusable concept definitions (concept classes from existing languages. This language definition technique concentrates on semantic abstractions rather than syntactical peculiarities. We present a set of common concept classes that describe structure, behaviour, and data aspects of high-level modelling languages. Our models contain syntax modelling using the OMG MOF as well as static semantic constraints written in OMG OCL. We derive metamodels for subsets of SDL and UML from these common concepts, and we show for parts of these languages that they can be modelled and related to each other through the same abstract concepts.

  14. Genome-scale multilocus microsatellite typing of Trypanosoma cruzi discrete typing unit I reveals phylogeographic structure and specific genotypes linked to human infection.

    Directory of Open Access Journals (Sweden)

    Martin S Llewellyn

    2009-05-01

    Full Text Available Trypanosoma cruzi is the most important parasitic infection in Latin America and is also genetically highly diverse, with at least six discrete typing units (DTUs reported: Tc I, IIa, IIb, IIc, IId, and IIe. However, the current six-genotype classification is likely to be a poor reflection of the total genetic diversity present in this undeniably ancient parasite. To determine whether epidemiologically important information is "hidden" at the sub-DTU level, we developed a 48-marker panel of polymorphic microsatellite loci to investigate population structure among 135 samples from across the geographic distribution of TcI. This DTU is the major cause of resurgent human disease in northern South America but also occurs in silvatic triatomine vectors and mammalian reservoir hosts throughout the continent. Based on a total dataset of 12,329 alleles, we demonstrate that silvatic TcI populations are extraordinarily genetically diverse, show spatial structuring on a continental scale, and have undergone recent biogeographic expansion into the southern United States of America. Conversely, the majority of human strains sampled are restricted to two distinct groups characterised by a considerable reduction in genetic diversity with respect to isolates from silvatic sources. In Venezuela, most human isolates showed little identity with known local silvatic strains, despite frequent invasion of the domestic setting by infected adult vectors. Multilocus linkage indices indicate predominantly clonal parasite propagation among all populations. However, excess homozygosity among silvatic strains and raised heterozygosity among domestic populations suggest that some level of genetic recombination cannot be ruled out. The epidemiological significance of these findings is discussed.

  15. Genome-scale comparison of expanded gene families in Plasmodium ovale wallikeri and Plasmodium ovale curtisi with Plasmodium malariae and with other Plasmodium species.

    Science.gov (United States)

    Ansari, Hifzur Rahman; Templeton, Thomas J; Subudhi, Amit Kumar; Ramaprasad, Abhinay; Tang, Jianxia; Lu, Feng; Naeem, Raeece; Hashish, Yasmeen; Oguike, Mary C; Benavente, Ernest Diez; Clark, Taane G; Sutherland, Colin J; Barnwell, John W; Culleton, Richard; Cao, Jun; Pain, Arnab

    2016-10-01

    Malaria in humans is caused by six species of Plasmodium parasites, of which the nuclear genome sequences for the two Plasmodium ovale spp., P. ovale curtisi and P. ovale wallikeri, and Plasmodium malariae have not yet been analyzed. Here we present an analysis of the nuclear genome sequences of these three parasites, and describe gene family expansions therein. Plasmodium ovale curtisi and P. ovale wallikeri are genetically distinct but morphologically indistinguishable and have sympatric ranges through the tropics of Africa, Asia and Oceania. Both P. ovale spp. show expansion of the surfin variant gene family, and an amplification of the Plasmodium interspersed repeat (pir) superfamily which results in an approximately 30% increase in genome size. For comparison, we have also analyzed the draft nuclear genome of P. malariae, a malaria parasite causing mild malaria symptoms with a quartan life cycle, long-term chronic infections, and wide geographic distribution. Plasmodium malariae shows only a moderate level of expansion of pir genes, and unique expansions of a highly diverged transmembrane protein family with over 550 members and the gamete P25/27 gene family. The observed diversity in the P. ovale wallikeri and P. ovale curtisi surface antigens, combined with their phylogenetic separation, supports consideration that the two parasites be given species status. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  16. Revealing genome-scale transcriptional regulatory landscape of OmpR highlights its expanded regulatory roles under osmotic stress in Escherichia coli K-12 MG1655

    DEFF Research Database (Denmark)

    Seo, Sang Woo; Gao, Ye; Kim, Donghyuk

    2017-01-01

    ) belong to OmpR regulon. Among them, 26 genes show more than two-fold changes in expression level in an OmpR knock-out strain. Specifically, we find that: 1) OmpR regulates mostly membrane-located gene products involved in diverse fundamental biological processes, such as narU (encoding nitrate/nitrite...

  17. Non-essential genes form the hubs of genome scale protein function and environmental gene expression networks in Salmonella enterica serovar Typhimurium

    DEFF Research Database (Denmark)

    Rosenkrantz, Jesper T.; Aarts, Henk; Abee, Tjakko

    2013-01-01

    B proved unsuccessful. No difference between mutants and the wild type strain was observed during growth at unfavorable temperatures, pH values, NaCl concentrations and in the presence of H2O2. Eight mutants were evaluated for virulence in C57/BL6 mice and none differed from the wild type strain. Notably...

  18. Fixing Formalin: A Method to Recover Genomic-Scale DNA Sequence Data from Formalin-Fixed Museum Specimens Using High-Throughput Sequencing.

    Directory of Open Access Journals (Sweden)

    Sarah M Hykin

    Full Text Available For 150 years or more, specimens were routinely collected and deposited in natural history collections without preserving fresh tissue samples for genetic analysis. In the case of most herpetological specimens (i.e. amphibians and reptiles, attempts to extract and sequence DNA from formalin-fixed, ethanol-preserved specimens-particularly for use in phylogenetic analyses-has been laborious and largely ineffective due to the highly fragmented nature of the DNA. As a result, tens of thousands of specimens in herpetological collections have not been available for sequence-based phylogenetic studies. Massively parallel High-Throughput Sequencing methods and the associated bioinformatics, however, are particularly suited to recovering meaningful genetic markers from severely degraded/fragmented DNA sequences such as DNA damaged by formalin-fixation. In this study, we compared previously published DNA extraction methods on three tissue types subsampled from formalin-fixed specimens of Anolis carolinensis, followed by sequencing. Sufficient quality DNA was recovered from liver tissue, making this technique minimally destructive to museum specimens. Sequencing was only successful for the more recently collected specimen (collected ~30 ybp. We suspect this could be due either to the conditions of preservation and/or the amount of tissue used for extraction purposes. For the successfully sequenced sample, we found a high rate of base misincorporation. After rigorous trimming, we successfully mapped 27.93% of the cleaned reads to the reference genome, were able to reconstruct the complete mitochondrial genome, and recovered an accurate phylogenetic placement for our specimen. We conclude that the amount of DNA available, which can vary depending on specimen age and preservation conditions, will determine if sequencing will be successful. The technique described here will greatly improve the value of museum collections by making many formalin-fixed specimens available for genetic analysis.

  19. Fixing Formalin: A Method to Recover Genomic-Scale DNA Sequence Data from Formalin-Fixed Museum Specimens Using High-Throughput Sequencing.

    Science.gov (United States)

    Hykin, Sarah M; Bi, Ke; McGuire, Jimmy A

    2015-01-01

    For 150 years or more, specimens were routinely collected and deposited in natural history collections without preserving fresh tissue samples for genetic analysis. In the case of most herpetological specimens (i.e. amphibians and reptiles), attempts to extract and sequence DNA from formalin-fixed, ethanol-preserved specimens-particularly for use in phylogenetic analyses-has been laborious and largely ineffective due to the highly fragmented nature of the DNA. As a result, tens of thousands of specimens in herpetological collections have not been available for sequence-based phylogenetic studies. Massively parallel High-Throughput Sequencing methods and the associated bioinformatics, however, are particularly suited to recovering meaningful genetic markers from severely degraded/fragmented DNA sequences such as DNA damaged by formalin-fixation. In this study, we compared previously published DNA extraction methods on three tissue types subsampled from formalin-fixed specimens of Anolis carolinensis, followed by sequencing. Sufficient quality DNA was recovered from liver tissue, making this technique minimally destructive to museum specimens. Sequencing was only successful for the more recently collected specimen (collected ~30 ybp). We suspect this could be due either to the conditions of preservation and/or the amount of tissue used for extraction purposes. For the successfully sequenced sample, we found a high rate of base misincorporation. After rigorous trimming, we successfully mapped 27.93% of the cleaned reads to the reference genome, were able to reconstruct the complete mitochondrial genome, and recovered an accurate phylogenetic placement for our specimen. We conclude that the amount of DNA available, which can vary depending on specimen age and preservation conditions, will determine if sequencing will be successful. The technique described here will greatly improve the value of museum collections by making many formalin-fixed specimens available for genetic analysis.

  20. Genome-scale comparison of expanded gene families in Plasmodium ovale wallikeri and Plasmodium ovale curtisi with Plasmodium malariae and with other Plasmodium species

    KAUST Repository

    Ansari, Hifzur Rahman; Templeton, Thomas J.; Subudhi, Amit; Ramaprasad, Abhinay; Tang, Jianxia; Lu, Feng; Naeem, Raeece; Hashish, Yasmeen; Oguike, Mary C.; Benavente, Ernest Diez; Clark, Taane G.; Sutherland, Colin J.; Barnwell, John W.; Culleton, Richard; Cao, Jun; Pain, Arnab

    2016-01-01

    Malaria in humans is caused by six species of Plasmodium parasites, of which the nuclear genome sequences for the two Plasmodium ovale spp., P. ovale curtisi and P. ovale wallikeri, and Plasmodium malariae have not yet been analyzed. Here we present an analysis of the nuclear genome sequences of these three parasites, and describe gene family expansions therein. Plasmodium ovale curtisi and P. ovale wallikeri are genetically distinct but morphologically indistinguishable and have sympatric ranges through the tropics of Africa, Asia and Oceania. Both P. ovale spp. show expansion of the surfin variant gene family, and an amplification of the Plasmodium interspersed repeat (pir) superfamily which results in an approximately 30% increase in genome size. For comparison, we have also analyzed the draft nuclear genome of P. malariae, a malaria parasite causing mild malaria symptoms with a quartan life cycle, long-term chronic infections, and wide geographic distribution. Plasmodium malariae shows only a moderate level of expansion of pir genes, and unique expansions of a highly diverged transmembrane protein family with over 550 members and the gamete P25/27 gene family. The observed diversity in the P. ovale wallikeri and P. ovale curtisi surface antigens, combined with their phylogenetic separation, supports consideration that the two parasites be given species status.

  1. Genome-scale comparison of expanded gene families in Plasmodium ovale wallikeri and Plasmodium ovale curtisi with Plasmodium malariae and with other Plasmodium species

    KAUST Repository

    Ansari, Hifzur Rahman

    2016-07-05

    Malaria in humans is caused by six species of Plasmodium parasites, of which the nuclear genome sequences for the two Plasmodium ovale spp., P. ovale curtisi and P. ovale wallikeri, and Plasmodium malariae have not yet been analyzed. Here we present an analysis of the nuclear genome sequences of these three parasites, and describe gene family expansions therein. Plasmodium ovale curtisi and P. ovale wallikeri are genetically distinct but morphologically indistinguishable and have sympatric ranges through the tropics of Africa, Asia and Oceania. Both P. ovale spp. show expansion of the surfin variant gene family, and an amplification of the Plasmodium interspersed repeat (pir) superfamily which results in an approximately 30% increase in genome size. For comparison, we have also analyzed the draft nuclear genome of P. malariae, a malaria parasite causing mild malaria symptoms with a quartan life cycle, long-term chronic infections, and wide geographic distribution. Plasmodium malariae shows only a moderate level of expansion of pir genes, and unique expansions of a highly diverged transmembrane protein family with over 550 members and the gamete P25/27 gene family. The observed diversity in the P. ovale wallikeri and P. ovale curtisi surface antigens, combined with their phylogenetic separation, supports consideration that the two parasites be given species status.

  2. A taxonomic framework for emerging groups of ecologically important marine gammaproteobacteria based on the reconstruction of evolutionary relationships using genome-scale data

    Directory of Open Access Journals (Sweden)

    Stefan eSpring

    2015-04-01

    Full Text Available In recent years a large number of isolates were obtained from saline environments that are phylogenetically related to distinct clades of oligotrophic marine gammaproteobacteria, which were originally identified in seawater samples using cultivation independent methods and are characterized by high seasonal abundances in coastal environments. To date a sound taxonomic framework for the classification of these ecologically important isolates and related species in accordance with their evolutionary relationships is missing.In this study we demonstrate that a reliable allocation of members of the oligotrophic marine gammaproteobacteria (OMG group and related species to higher taxonomic ranks is possible by phylogenetic analyses of whole proteomes but also of the RNA polymerase beta subunit, whereas phylogenetic reconstructions based on 16S rRNA genes alone resulted in unstable tree topologies with only insignificant bootstrap support. The identified clades could be correlated with distinct phenotypic traits illustrating an adaptation to common environmental factors in their evolutionary history. Genome wide gene-content analyses revealed the existence of two distinct ecological guilds within the analyzed lineage of marine gammaproteobacteria which can be distinguished by their trophic strategies. Based on our results a novel order within the class Gammaproteobacteria is proposed, which is designated Cellvibrionales ord. nov. and comprises the five novel families Cellvibrionaceae fam. nov., Halieaceae fam. nov., Microbulbiferaceae fam. nov., Porticoccaceae fam. nov., and Spongiibacteraceae fam. nov.

  3. Inferring metabolic states in uncharacterized environments using gene-expression measurements.

    Directory of Open Access Journals (Sweden)

    Sergio Rossell

    Full Text Available The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of

  4. ELMs and constraints on the H-mode pedestal: A model based on peeling-ballooning modes

    International Nuclear Information System (INIS)

    Snyder, P.B.

    2002-01-01

    Maximizing the pedestal height while maintaining acceptable ELMs is a key issue for optimizing tokamak performance. We present a model for ELMs and pedestal constraints based upon theoretical analysis of edge instabilities which can limit the pedestal height and drive ELMs. Sharp pedestal pressure gradients drive large bootstrap currents which play a complex dual role in the stability physics. Consequently, the dominant modes are often intermediate-n coupled 'peeling-ballooning' modes, driven both by current and the pressure gradient. A highly efficient new MHD code, ELITE, is used to study these modes, and calculate quantitative stability constraints on the pedestal, including direct constraints on the pedestal height. A model of various ELM types is developed, and quantitatively compared to data from several tokamaks. A number of observations agree with predictions, including ELM onset times, ELM depth, and variation in pedestal height with discharge shape. Projections of pedestal stability constraints for Next Step designs, and nonlinear simulations of peeling-ballooning modes using the BOUT code are also presented. (author)

  5. Systems-level computational modeling demonstrates fuel selection switching in high capacity running and low capacity running rats

    Science.gov (United States)

    Qi, Nathan R.

    2018-01-01

    High capacity and low capacity running rats, HCR and LCR respectively, have been bred to represent two extremes of running endurance and have recently demonstrated disparities in fuel usage during transient aerobic exercise. HCR rats can maintain fatty acid (FA) utilization throughout the course of transient aerobic exercise whereas LCR rats rely predominantly on glucose utilization. We hypothesized that the difference between HCR and LCR fuel utilization could be explained by a difference in mitochondrial density. To test this hypothesis and to investigate mechanisms of fuel selection, we used a constraint-based kinetic analysis of whole-body metabolism to analyze transient exercise data from these rats. Our model analysis used a thermodynamically constrained kinetic framework that accounts for glycolysis, the TCA cycle, and mitochondrial FA transport and oxidation. The model can effectively match the observed relative rates of oxidation of glucose versus FA, as a function of ATP demand. In searching for the minimal differences required to explain metabolic function in HCR versus LCR rats, it was determined that the whole-body metabolic phenotype of LCR, compared to the HCR, could be explained by a ~50% reduction in total mitochondrial activity with an additional 5-fold reduction in mitochondrial FA transport activity. Finally, we postulate that over sustained periods of exercise that LCR can partly overcome the initial deficit in FA catabolic activity by upregulating FA transport and/or oxidation processes. PMID:29474500

  6. Modelling the models

    CERN Multimedia

    Anaïs Schaeffer

    2012-01-01

    By analysing the production of mesons in the forward region of LHC proton-proton collisions, the LHCf collaboration has provided key information needed to calibrate extremely high-energy cosmic ray models.   Average transverse momentum (pT) as a function of rapidity loss ∆y. Black dots represent LHCf data and the red diamonds represent SPS experiment UA7 results. The predictions of hadronic interaction models are shown by open boxes (sibyll 2.1), open circles (qgsjet II-03) and open triangles (epos 1.99). Among these models, epos 1.99 shows the best overall agreement with the LHCf data. LHCf is dedicated to the measurement of neutral particles emitted at extremely small angles in the very forward region of LHC collisions. Two imaging calorimeters – Arm1 and Arm2 – take data 140 m either side of the ATLAS interaction point. “The physics goal of this type of analysis is to provide data for calibrating the hadron interaction models – the well-known &...

  7. DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems.

    Science.gov (United States)

    Smith, Robert W; van Rosmalen, Rik P; Martins Dos Santos, Vitor A P; Fleck, Christian

    2018-06-19

    Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed. In this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics. The presented pipeline automates the

  8. Alignment of microbial fitness with engineered product formation: obligatory coupling between acetate production and photoautotrophic growth.

    Science.gov (United States)

    Du, Wei; Jongbloets, Joeri A; van Boxtel, Coco; Pineda Hernández, Hugo; Lips, David; Oliver, Brett G; Hellingwerf, Klaas J; Branco Dos Santos, Filipe

    2018-01-01

    Microbial bioengineering has the potential to become a key contributor to the future development of human society by providing sustainable, novel, and cost-effective production pipelines. However, the sustained productivity of genetically engineered strains is often a challenge, as spontaneous non-producing mutants tend to grow faster and take over the population. Novel strategies to prevent this issue of strain instability are urgently needed. In this study, we propose a novel strategy applicable to all microbial production systems for which a genome-scale metabolic model is available that aligns the production of native metabolites to the formation of biomass. Based on well-established constraint-based analysis techniques such as OptKnock and FVA, we developed an in silico pipeline-FRUITS-that specifically 'Finds Reactions Usable in Tapping Side-products'. It analyses a metabolic network to identify compounds produced in anabolism that are suitable to be coupled to growth by deletion of their re-utilization pathway(s), and computes their respective biomass and product formation rates. When applied to Synechocystis sp. PCC6803, a model cyanobacterium explored for sustainable bioproduction, a total of nine target metabolites were identified. We tested our approach for one of these compounds, acetate, which is used in a wide range of industrial applications. The model-guided engineered strain shows an obligatory coupling between acetate production and photoautotrophic growth as predicted. Furthermore, the stability of acetate productivity in this strain was confirmed by performing prolonged turbidostat cultivations. This work demonstrates a novel approach to stabilize the production of target compounds in cyanobacteria that culminated in the first report of a photoautotrophic growth-coupled cell factory. The method developed is generic and can easily be extended to any other modeled microbial production system.

  9. Melanoma screening: Informing public health policy with quantitative modelling.

    Directory of Open Access Journals (Sweden)

    Stephen Gilmore

    Full Text Available Australia and New Zealand share the highest incidence rates of melanoma worldwide. Despite the substantial increase in public and physician awareness of melanoma in Australia over the last 30 years-as a result of the introduction of publicly funded mass media campaigns that began in the early 1980s -mortality has steadily increased during this period. This increased mortality has led investigators to question the relative merits of primary versus secondary prevention; that is, sensible sun exposure practices versus early detection. Increased melanoma vigilance on the part of the public and among physicians has resulted in large increases in public health expenditure, primarily from screening costs and increased rates of office surgery. Has this attempt at secondary prevention been effective? Unfortunately epidemiologic studies addressing the causal relationship between the level of secondary prevention and mortality are prohibitively difficult to implement-it is currently unknown whether increased melanoma surveillance reduces mortality, and if so, whether such an approach is cost-effective. Here I address the issue of secondary prevention of melanoma with respect to incidence and mortality (and cost per life saved by developing a Markov model of melanoma epidemiology based on Australian incidence and mortality data. The advantages of developing a methodology that can determine constraint-based surveillance outcomes are twofold: first, it can address the issue of effectiveness; and second, it can quantify the trade-off between cost and utilisation of medical resources on one hand, and reduced morbidity and lives saved on the other. With respect to melanoma, implementing the model facilitates the quantitative determination of the relative effectiveness and trade-offs associated with different levels of secondary and tertiary prevention, both retrospectively and prospectively. For example, I show that the surveillance enhancement that began in

  10. Impact of censoring on learning Bayesian networks in survival modelling.

    Science.gov (United States)

    Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola

    2009-11-01

    Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from

  11. Refining multi-model projections of temperature extremes by evaluation against land-atmosphere coupling diagnostics

    Science.gov (United States)

    Sippel, Sebastian; Zscheischler, Jakob; Mahecha, Miguel D.; Orth, Rene; Reichstein, Markus; Vogel, Martha; Seneviratne, Sonia I.

    2017-05-01

    The Earth's land surface and the atmosphere are strongly interlinked through the exchange of energy and matter. This coupled behaviour causes various land-atmosphere feedbacks, and an insufficient understanding of these feedbacks contributes to uncertain global climate model projections. For example, a crucial role of the land surface in exacerbating summer heat waves in midlatitude regions has been identified empirically for high-impact heat waves, but individual climate models differ widely in their respective representation of land-atmosphere coupling. Here, we compile an ensemble of 54 combinations of observations-based temperature (T) and evapotranspiration (ET) benchmarking datasets and investigate coincidences of T anomalies with ET anomalies as a proxy for land-atmosphere interactions during periods of anomalously warm temperatures. First, we demonstrate that a large fraction of state-of-the-art climate models from the Coupled Model Intercomparison Project (CMIP5) archive produces systematically too frequent coincidences of high T anomalies with negative ET anomalies in midlatitude regions during the warm season and in several tropical regions year-round. These coincidences (high T, low ET) are closely related to the representation of temperature variability and extremes across the multi-model ensemble. Second, we derive a land-coupling constraint based on the spread of the T-ET datasets and consequently retain only a subset of CMIP5 models that produce a land-coupling behaviour that is compatible with these benchmark estimates. The constrained multi-model simulations exhibit more realistic temperature extremes of reduced magnitude in present climate in regions where models show substantial spread in T-ET coupling, i.e. biases in the model ensemble are consistently reduced. Also the multi-model simulations for the coming decades display decreased absolute temperature extremes in the constrained ensemble. On the other hand, the differences between projected

  12. Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics

    Directory of Open Access Journals (Sweden)

    S. Sippel

    2017-05-01

    Full Text Available The Earth's land surface and the atmosphere are strongly interlinked through the exchange of energy and matter. This coupled behaviour causes various land–atmosphere feedbacks, and an insufficient understanding of these feedbacks contributes to uncertain global climate model projections. For example, a crucial role of the land surface in exacerbating summer heat waves in midlatitude regions has been identified empirically for high-impact heat waves, but individual climate models differ widely in their respective representation of land–atmosphere coupling. Here, we compile an ensemble of 54 combinations of observations-based temperature (T and evapotranspiration (ET benchmarking datasets and investigate coincidences of T anomalies with ET anomalies as a proxy for land–atmosphere interactions during periods of anomalously warm temperatures. First, we demonstrate that a large fraction of state-of-the-art climate models from the Coupled Model Intercomparison Project (CMIP5 archive produces systematically too frequent coincidences of high T anomalies with negative ET anomalies in midlatitude regions during the warm season and in several tropical regions year-round. These coincidences (high T, low ET are closely related to the representation of temperature variability and extremes across the multi-model ensemble. Second, we derive a land-coupling constraint based on the spread of the T–ET datasets and consequently retain only a subset of CMIP5 models that produce a land-coupling behaviour that is compatible with these benchmark estimates. The constrained multi-model simulations exhibit more realistic temperature extremes of reduced magnitude in present climate in regions where models show substantial spread in T–ET coupling, i.e. biases in the model ensemble are consistently reduced. Also the multi-model simulations for the coming decades display decreased absolute temperature extremes in the constrained ensemble. On the other hand

  13. Study of the Navigation Method for a Snake Robot Based on the Kinematics Model with MEMS IMU.

    Science.gov (United States)

    Zhao, Xu; Dou, Lihua; Su, Zhong; Liu, Ning

    2018-03-16

    A snake robot is a type of highly redundant mobile robot that significantly differs from a tracked robot, wheeled robot and legged robot. To address the issue of a snake robot performing self-localization in the application environment without assistant orientation, an autonomous navigation method is proposed based on the snake robot's motion characteristic constraints. The method realized the autonomous navigation of the snake robot with non-nodes and an external assistant using its own Micro-Electromechanical-Systems (MEMS) Inertial-Measurement-Unit (IMU). First, it studies the snake robot's motion characteristics, builds the kinematics model, and then analyses the motion constraint characteristics and motion error propagation properties. Second, it explores the snake robot's navigation layout, proposes a constraint criterion and the fixed relationship, and makes zero-state constraints based on the motion features and control modes of a snake robot. Finally, it realizes autonomous navigation positioning based on the Extended-Kalman-Filter (EKF) position estimation method under the constraints of its motion characteristics. With the self-developed snake robot, the test verifies the proposed method, and the position error is less than 5% of Total-Traveled-Distance (TDD). In a short-distance environment, this method is able to meet the requirements of a snake robot in order to perform autonomous navigation and positioning in traditional applications and can be extended to other familiar multi-link robots.

  14. Study of the Navigation Method for a Snake Robot Based on the Kinematics Model with MEMS IMU

    Directory of Open Access Journals (Sweden)

    Xu Zhao

    2018-03-01

    Full Text Available A snake robot is a type of highly redundant mobile robot that significantly differs from a tracked robot, wheeled robot and legged robot. To address the issue of a snake robot performing self-localization in the application environment without assistant orientation, an autonomous navigation method is proposed based on the snake robot’s motion characteristic constraints. The method realized the autonomous navigation of the snake robot with non-nodes and an external assistant using its own Micro-Electromechanical-Systems (MEMS Inertial-Measurement-Unit (IMU. First, it studies the snake robot’s motion characteristics, builds the kinematics model, and then analyses the motion constraint characteristics and motion error propagation properties. Second, it explores the snake robot’s navigation layout, proposes a constraint criterion and the fixed relationship, and makes zero-state constraints based on the motion features and control modes of a snake robot. Finally, it realizes autonomous navigation positioning based on the Extended-Kalman-Filter (EKF position estimation method under the constraints of its motion characteristics. With the self-developed snake robot, the test verifies the proposed method, and the position error is less than 5% of Total-Traveled-Distance (TDD. In a short-distance environment, this method is able to meet the requirements of a snake robot in order to perform autonomous navigation and positioning in traditional applications and can be extended to other familiar multi-link robots.

  15. Study of the Navigation Method for a Snake Robot Based on the Kinematics Model with MEMS IMU

    Science.gov (United States)

    Dou, Lihua; Su, Zhong; Liu, Ning

    2018-01-01

    A snake robot is a type of highly redundant mobile robot that significantly differs from a tracked robot, wheeled robot and legged robot. To address the issue of a snake robot performing self-localization in the application environment without assistant orientation, an autonomous navigation method is proposed based on the snake robot’s motion characteristic constraints. The method realized the autonomous navigation of the snake robot with non-nodes and an external assistant using its own Micro-Electromechanical-Systems (MEMS) Inertial-Measurement-Unit (IMU). First, it studies the snake robot’s motion characteristics, builds the kinematics model, and then analyses the motion constraint characteristics and motion error propagation properties. Second, it explores the snake robot’s navigation layout, proposes a constraint criterion and the fixed relationship, and makes zero-state constraints based on the motion features and control modes of a snake robot. Finally, it realizes autonomous navigation positioning based on the Extended-Kalman-Filter (EKF) position estimation method under the constraints of its motion characteristics. With the self-developed snake robot, the test verifies the proposed method, and the position error is less than 5% of Total-Traveled-Distance (TDD). In a short-distance environment, this method is able to meet the requirements of a snake robot in order to perform autonomous navigation and positioning in traditional applications and can be extended to other familiar multi-link robots. PMID:29547515

  16. Modelling Overview

    DEFF Research Database (Denmark)

    Larsen, Lars Bjørn; Vesterager, Johan

    This report provides an overview of the existing models of global manufacturing, describes the required modelling views and associated methods and identifies tools, which can provide support for this modelling activity.The model adopted for global manufacturing is that of an extended enterprise s...

  17. Document Models

    Directory of Open Access Journals (Sweden)

    A.A. Malykh

    2017-08-01

    Full Text Available In this paper, the concept of locally simple models is considered. Locally simple models are arbitrarily complex models built from relatively simple components. A lot of practically important domains of discourse can be described as locally simple models, for example, business models of enterprises and companies. Up to now, research in human reasoning automation has been mainly concentrated around the most intellectually intensive activities, such as automated theorem proving. On the other hand, the retailer business model is formed from ”jobs”, and each ”job” can be modelled and automated more or less easily. At the same time, the whole retailer model as an integrated system is extremely complex. In this paper, we offer a variant of the mathematical definition of a locally simple model. This definition is intended for modelling a wide range of domains. Therefore, we also must take into account the perceptual and psychological issues. Logic is elitist, and if we want to attract to our models as many people as possible, we need to hide this elitism behind some metaphor, to which ’ordinary’ people are accustomed. As such a metaphor, we use the concept of a document, so our locally simple models are called document models. Document models are built in the paradigm of semantic programming. This allows us to achieve another important goal - to make the documentary models executable. Executable models are models that can act as practical information systems in the described domain of discourse. Thus, if our model is executable, then programming becomes redundant. The direct use of a model, instead of its programming coding, brings important advantages, for example, a drastic cost reduction for development and maintenance. Moreover, since the model is well and sound, and not dissolved within programming modules, we can directly apply AI tools, in particular, machine learning. This significantly expands the possibilities for automation and

  18. Model theory

    CERN Document Server

    Chang, CC

    2012-01-01

    Model theory deals with a branch of mathematical logic showing connections between a formal language and its interpretations or models. This is the first and most successful textbook in logical model theory. Extensively updated and corrected in 1990 to accommodate developments in model theoretic methods - including classification theory and nonstandard analysis - the third edition added entirely new sections, exercises, and references. Each chapter introduces an individual method and discusses specific applications. Basic methods of constructing models include constants, elementary chains, Sko

  19. Modeling Methods

    Science.gov (United States)

    Healy, Richard W.; Scanlon, Bridget R.

    2010-01-01

    Simulation models are widely used in all types of hydrologic studies, and many of these models can be used to estimate recharge. Models can provide important insight into the functioning of hydrologic systems by identifying factors that influence recharge. The predictive capability of models can be used to evaluate how changes in climate, water use, land use, and other factors may affect recharge rates. Most hydrological simulation models, including watershed models and groundwater-flow models, are based on some form of water-budget equation, so the material in this chapter is closely linked to that in Chapter 2. Empirical models that are not based on a water-budget equation have also been used for estimating recharge; these models generally take the form of simple estimation equations that define annual recharge as a function of precipitation and possibly other climatic data or watershed characteristics.Model complexity varies greatly. Some models are simple accounting models; others attempt to accurately represent the physics of water movement through each compartment of the hydrologic system. Some models provide estimates of recharge explicitly; for example, a model based on the Richards equation can simulate water movement from the soil surface through the unsaturated zone to the water table. Recharge estimates can be obtained indirectly from other models. For example, recharge is a parameter in groundwater-flow models that solve for hydraulic head (i.e. groundwater level). Recharge estimates can be obtained through a model calibration process in which recharge and other model parameter values are adjusted so that simulated water levels agree with measured water levels. The simulation that provides the closest agreement is called the best fit, and the recharge value used in that simulation is the model-generated estimate of recharge.

  20. Modelling the Transfer and Retention of Nutrients in the Drainage Network of the Danube River

    Science.gov (United States)

    Garnier, J.; Billen, G.; Hannon, E.; Fonbonne, S.; Videnina, Y.; Soulie, M.

    2002-03-01

    in the coastal zone of the Black Sea. The sharp drop in N and P delivery to the Black Sea, observed since 1991, was simulated with a scenario constructed to reproduce new constraints based on documented modifications of human activity in the watershed. Due to the scarcity of data, there is a need for further validation of the model. Nevertheless, the structure of the model allows the specificity of each sub-basin to be taken into account in future management plans.

  1. Galactic models

    International Nuclear Information System (INIS)

    Buchler, J.R.; Gottesman, S.T.; Hunter, J.H. Jr.

    1990-01-01

    Various papers on galactic models are presented. Individual topics addressed include: observations relating to galactic mass distributions; the structure of the Galaxy; mass distribution in spiral galaxies; rotation curves of spiral galaxies in clusters; grand design, multiple arm, and flocculent spiral galaxies; observations of barred spirals; ringed galaxies; elliptical galaxies; the modal approach to models of galaxies; self-consistent models of spiral galaxies; dynamical models of spiral galaxies; N-body models. Also discussed are: two-component models of galaxies; simulations of cloudy, gaseous galactic disks; numerical experiments on the stability of hot stellar systems; instabilities of slowly rotating galaxies; spiral structure as a recurrent instability; model gas flows in selected barred spiral galaxies; bar shapes and orbital stochasticity; three-dimensional models; polar ring galaxies; dynamical models of polar rings

  2. Model-model Perencanaan Strategik

    OpenAIRE

    Amirin, Tatang M

    2005-01-01

    The process of strategic planning, used to be called as long-term planning, consists of several components, including strategic analysis, setting strategic direction (covering of mission, vision, and values), and action planning. Many writers develop models representing the steps of the strategic planning process, i.e. basic planning model, problem-based planning model, scenario model, and organic or self-organizing model.

  3. Event Modeling

    DEFF Research Database (Denmark)

    Bækgaard, Lars

    2001-01-01

    The purpose of this chapter is to discuss conceptual event modeling within a context of information modeling. Traditionally, information modeling has been concerned with the modeling of a universe of discourse in terms of information structures. However, most interesting universes of discourse...... are dynamic and we present a modeling approach that can be used to model such dynamics.We characterize events as both information objects and change agents (Bækgaard 1997). When viewed as information objects events are phenomena that can be observed and described. For example, borrow events in a library can...

  4. SU-F-T-347: An Absolute Dose-Volume Constraint Based Deterministic Optimization Framework for Multi-Co60 Source Focused Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Liang, B; Liu, B; Li, Y; Guo, B; Xu, X; Wei, R; Zhou, F [Beihang University, Beijing, Beijing (China); Wu, Q [Duke University Medical Center, Durham, NC (United States)

    2016-06-15

    Purpose: Treatment plan optimization in multi-Co60 source focused radiotherapy with multiple isocenters is challenging, because dose distribution is normalized to maximum dose during optimization and evaluation. The objective functions are traditionally defined based on relative dosimetric distribution. This study presents an alternative absolute dose-volume constraint (ADC) based deterministic optimization framework (ADC-DOF). Methods: The initial isocenters are placed on the eroded target surface. Collimator size is chosen based on the area of 2D contour on corresponding axial slice. The isocenter spacing is determined by adjacent collimator sizes. The weights are optimized by minimizing the deviation from ADCs using the steepest descent technique. An iterative procedure is developed to reduce the number of isocenters, where the isocenter with lowest weight is removed without affecting plan quality. The ADC-DOF is compared with the genetic algorithm (GA) using the same arbitrary shaped target (254cc), with a 15mm margin ring structure representing normal tissues. Results: For ADC-DOF, the ADCs imposed on target and ring are (D100>10Gy, D50,10, 0<12Gy, 15Gy and 20Gy) and (D40<10Gy). The resulting D100, 50, 10, 0 and D40 are (9.9Gy, 12.0Gy, 14.1Gy and 16.2Gy) and (10.2Gy). The objectives of GA are to maximize 50% isodose target coverage (TC) while minimize the dose delivered to the ring structure, which results in 97% TC and 47.2% average dose in ring structure. For ADC-DOF (GA) techniques, 20 out of 38 (10 out of 12) initial isocenters are used in the final plan, and the computation time is 8.7s (412.2s) on an i5 computer. Conclusion: We have developed a new optimization technique using ADC and deterministic optimization. Compared with GA, ADC-DOF uses more isocenters but is faster and more robust, and achieves a better conformity. For future work, we will focus on developing a more effective mechanism for initial isocenter determination.

  5. Structural constraints-based evaluation of immunogenic avirulent toxins from Clostridium botulinum C2 and C3 toxins as subunit vaccines.

    Science.gov (United States)

    Prisilla, A; Prathiviraj, R; Sasikala, R; Chellapandi, P

    2016-10-01

    Clostridium botulinum (group-III) is an anaerobic bacterium producing C2 and C3 toxins in addition to botulinum neurotoxins in avian and mammalian cells. C2 and C3 toxins are members of bacterial ADP-ribosyltransferase superfamily, which modify the eukaryotic cell surface proteins by ADP-ribosylation reaction. Herein, the mutant proteins with lack of catalytic and pore forming function derived from C2 (C2I and C2II) and C3 toxins were computationally evaluated to understand their structure-function integrity. We have chosen many structural constraints including local structural environment, folding process, backbone conformation, conformational dynamic sub-space, NAD-binding specificity and antigenic determinants for screening of suitable avirulent toxins. A total of 20 avirulent mutants were identified out of 23 mutants, which were experimentally produced by site-directed mutagenesis. No changes in secondary structural elements in particular to α-helices and β-sheets and also in fold rate of all-β classes. Structural stability was maintained by reordered hydrophobic and hydrogen bonding patterns. Molecular dynamic studies suggested that coupled mutations may restrain the binding affinity to NAD(+) or protein substrate upon structural destabilization. Avirulent toxins of this study have stable energetic backbone conformation with a common blue print of folding process. Molecular docking studies revealed that avirulent mutants formed more favorable hydrogen bonding with the side-chain of amino acids near to conserved NAD-binding core, despite of restraining NAD-binding specificity. Thus, structural constraints in the avirulent toxins would determine their immunogenic nature for the prioritization of protein-based subunit vaccine/immunogens to avian and veterinary animals infected with C. botulinum. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Fast Generation of Container Vessel Stowage Plans:Using mixed integer programming for optimal master planning and constraint based local search for slot planning

    OpenAIRE

    Pacino, Dario

    2012-01-01

    Containerization has changed the way the world perceives shipping. It is now possible to establish complex international supply chains that have minimized shipping costs. Over the past two decades, the demand for cost efficient containerized transportation has seen a continuous increase. In order to answer to this demand, shipping companies have deployed bigger container vessels, that nowadays can transport up to 18,000 containers and are wider than the extended Panama Canal. Like busses, con...

  7. Uncertainties in United States agricultural N2O emissions: comparing forward model simulations to atmospheric N2O data.

    Science.gov (United States)

    Nevison, C. D.; Saikawa, E.; Dlugokencky, E. J.; Andrews, A. E.; Sweeney, C.

    2014-12-01

    Atmospheric N2O concentrations have increased from 275 ppb in the preindustrial to about 325 ppb in recent years, a ~20% increase with important implications for both anthropogenic greenhouse forcing and stratospheric ozone recovery. This increase has been driven largely by synthetic fertilizer production and other perturbations to the global nitrogen cycle associated with human agriculture. Several recent regional atmospheric inversion studies have quantified North American agricultural N2O emissions using top-down constraints based on atmospheric N2O data from the National Oceanic and Atmospheric Administration (NOAA) Global Greenhouse Gas Reference Network, including surface, aircraft and tall tower platforms. These studies have concluded that global N2O inventories such as EDGAR may be underestimating the true U.S. anthropogenic N2O source by a factor of 3 or more. However, simple back-of-the-envelope calculations show that emissions of this magnitude are difficult to reconcile with the basic constraints of the global N2O budget. Here, we explore some possible reasons why regional atmospheric inversions might overestimate the U.S. agricultural N2O source. First, the seasonality of N2O agricultural sources is not well known, but can have an important influence on inversion results, particularly when the inversions are based on data that are concentrated in the spring/summer growing season. Second, boundary conditions can strongly influence regional inversions but the boundary conditions used may not adequately account for remote influences on surface data such as the seasonal stratospheric influx of N2O-depleted air. We will present a set of forward model simulations, using the Community Land Model (CLM) and two atmospheric chemistry tracer transport models, MOZART and the Whole Atmosphere Community Climate Model (WACCM), that examine the influence of terrestrial emissions and atmospheric chemistry and dynamics on atmospheric variability in N2O at U.S. and

  8. Modelling survival

    DEFF Research Database (Denmark)

    Ashauer, Roman; Albert, Carlo; Augustine, Starrlight

    2016-01-01

    The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test...

  9. Constitutive Models

    DEFF Research Database (Denmark)

    Sales-Cruz, Mauricio; Piccolo, Chiara; Heitzig, Martina

    2011-01-01

    covered, illustrating several models such as the Wilson equation and NRTL equation, along with their solution strategies. A section shows how to use experimental data to regress the property model parameters using a least squares approach. A full model analysis is applied in each example that discusses...... the degrees of freedom, dependent and independent variables and solution strategy. Vapour-liquid and solid-liquid equilibrium is covered, and applications to droplet evaporation and kinetic models are given....

  10. Interface models

    DEFF Research Database (Denmark)

    Ravn, Anders P.; Staunstrup, Jørgen

    1994-01-01

    This paper proposes a model for specifying interfaces between concurrently executing modules of a computing system. The model does not prescribe a particular type of communication protocol and is aimed at describing interfaces between both software and hardware modules or a combination of the two....... The model describes both functional and timing properties of an interface...

  11. Hydrological models are mediating models

    Science.gov (United States)

    Babel, L. V.; Karssenberg, D.

    2013-08-01

    Despite the increasing role of models in hydrological research and decision-making processes, only few accounts of the nature and function of models exist in hydrology. Earlier considerations have traditionally been conducted while making a clear distinction between physically-based and conceptual models. A new philosophical account, primarily based on the fields of physics and economics, transcends classes of models and scientific disciplines by considering models as "mediators" between theory and observations. The core of this approach lies in identifying models as (1) being only partially dependent on theory and observations, (2) integrating non-deductive elements in their construction, and (3) carrying the role of instruments of scientific enquiry about both theory and the world. The applicability of this approach to hydrology is evaluated in the present article. Three widely used hydrological models, each showing a different degree of apparent physicality, are confronted to the main characteristics of the "mediating models" concept. We argue that irrespective of their kind, hydrological models depend on both theory and observations, rather than merely on one of these two domains. Their construction is additionally involving a large number of miscellaneous, external ingredients, such as past experiences, model objectives, knowledge and preferences of the modeller, as well as hardware and software resources. We show that hydrological models convey the role of instruments in scientific practice by mediating between theory and the world. It results from these considerations that the traditional distinction between physically-based and conceptual models is necessarily too simplistic and refers at best to the stage at which theory and observations are steering model construction. The large variety of ingredients involved in model construction would deserve closer attention, for being rarely explicitly presented in peer-reviewed literature. We believe that devoting

  12. Can climate variability information constrain a hydrological model for an ungauged Costa Rican catchment?

    Science.gov (United States)

    Quesada-Montano, Beatriz; Westerberg, Ida K.; Fuentes-Andino, Diana; Hidalgo-Leon, Hugo; Halldin, Sven

    2017-04-01

    Long-term hydrological data are key to understanding catchment behaviour and for decision making within water management and planning. Given the lack of observed data in many regions worldwide, hydrological models are an alternative for reproducing historical streamflow series. Additional types of information - to locally observed discharge - can be used to constrain model parameter uncertainty for ungauged catchments. Climate variability exerts a strong influence on streamflow variability on long and short time scales, in particular in the Central-American region. We therefore explored the use of climate variability knowledge to constrain the simulated discharge uncertainty of a conceptual hydrological model applied to a Costa Rican catchment, assumed to be ungauged. To reduce model uncertainty we first rejected parameter relationships that disagreed with our understanding of the system. We then assessed how well climate-based constraints applied at long-term, inter-annual and intra-annual time scales could constrain model uncertainty. Finally, we compared the climate-based constraints to a constraint on low-flow statistics based on information obtained from global maps. We evaluated our method in terms of the ability of the model to reproduce the observed hydrograph and the active catchment processes in terms of two efficiency measures, a statistical consistency measure, a spread measure and 17 hydrological signatures. We found that climate variability knowledge was useful for reducing model uncertainty, in particular, unrealistic representation of deep groundwater processes. The constraints based on global maps of low-flow statistics provided more constraining information than those based on climate variability, but the latter rejected slow rainfall-runoff representations that the low flow statistics did not reject. The use of such knowledge, together with information on low-flow statistics and constraints on parameter relationships showed to be useful to

  13. ICRF modelling

    International Nuclear Information System (INIS)

    Phillips, C.K.

    1985-12-01

    This lecture provides a survey of the methods used to model fast magnetosonic wave coupling, propagation, and absorption in tokamaks. The validity and limitations of three distinct types of modelling codes, which will be contrasted, include discrete models which utilize ray tracing techniques, approximate continuous field models based on a parabolic approximation of the wave equation, and full field models derived using finite difference techniques. Inclusion of mode conversion effects in these models and modification of the minority distribution function will also be discussed. The lecture will conclude with a presentation of time-dependent global transport simulations of ICRF-heated tokamak discharges obtained in conjunction with the ICRF modelling codes. 52 refs., 15 figs

  14. Modelling in Business Model design

    NARCIS (Netherlands)

    Simonse, W.L.

    2013-01-01

    It appears that business model design might not always produce a design or model as the expected result. However when designers are involved, a visual model or artefact is produced. To assist strategic managers in thinking about how they can act, the designers challenge is to combine strategy and

  15. Eclipse models

    International Nuclear Information System (INIS)

    Michel, F.C.

    1989-01-01

    Three existing eclipse models for the PSR 1957 + 20 pulsar are discussed in terms of their requirements and the information they yield about the pulsar wind: the interacting wind from a companion model, the magnetosphere model, and the occulting disk model. It is shown out that the wind model requires an MHD wind from the pulsar, with enough particles that the Poynting flux of the wind can be thermalized; in this model, a large flux of energetic radiation from the pulsar is required to accompany the wind and drive the wind off the companion. The magnetosphere model requires an EM wind, which is Poynting flux dominated; the advantage of this model over the wind model is that the plasma density inside the magnetosphere can be orders of magnitude larger than in a magnetospheric tail blown back by wind interaction. The occulting disk model also requires an EM wind so that the interaction would be pushed down onto the companion surface, minimizing direct interaction of the wind with the orbiting macroscopic particles

  16. Ventilation Model

    International Nuclear Information System (INIS)

    Yang, H.

    1999-01-01

    The purpose of this analysis and model report (AMR) for the Ventilation Model is to analyze the effects of pre-closure continuous ventilation in the Engineered Barrier System (EBS) emplacement drifts and provide heat removal data to support EBS design. It will also provide input data (initial conditions, and time varying boundary conditions) for the EBS post-closure performance assessment and the EBS Water Distribution and Removal Process Model. The objective of the analysis is to develop, describe, and apply calculation methods and models that can be used to predict thermal conditions within emplacement drifts under forced ventilation during the pre-closure period. The scope of this analysis includes: (1) Provide a general description of effects and heat transfer process of emplacement drift ventilation. (2) Develop a modeling approach to simulate the impacts of pre-closure ventilation on the thermal conditions in emplacement drifts. (3) Identify and document inputs to be used for modeling emplacement ventilation. (4) Perform calculations of temperatures and heat removal in the emplacement drift. (5) Address general considerations of the effect of water/moisture removal by ventilation on the repository thermal conditions. The numerical modeling in this document will be limited to heat-only modeling and calculations. Only a preliminary assessment of the heat/moisture ventilation effects and modeling method will be performed in this revision. Modeling of moisture effects on heat removal and emplacement drift temperature may be performed in the future

  17. Mathematical modelling

    DEFF Research Database (Denmark)

    Blomhøj, Morten

    2004-01-01

    Developing competences for setting up, analysing and criticising mathematical models are normally seen as relevant only from and above upper secondary level. The general belief among teachers is that modelling activities presuppose conceptual understanding of the mathematics involved. Mathematical...... roots for the construction of important mathematical concepts. In addition competences for setting up, analysing and criticising modelling processes and the possible use of models is a formative aim in this own right for mathematics teaching in general education. The paper presents a theoretical...... modelling, however, can be seen as a practice of teaching that place the relation between real life and mathematics into the centre of teaching and learning mathematics, and this is relevant at all levels. Modelling activities may motivate the learning process and help the learner to establish cognitive...

  18. Mathematical modelling

    CERN Document Server

    2016-01-01

    This book provides a thorough introduction to the challenge of applying mathematics in real-world scenarios. Modelling tasks rarely involve well-defined categories, and they often require multidisciplinary input from mathematics, physics, computer sciences, or engineering. In keeping with this spirit of modelling, the book includes a wealth of cross-references between the chapters and frequently points to the real-world context. The book combines classical approaches to modelling with novel areas such as soft computing methods, inverse problems, and model uncertainty. Attention is also paid to the interaction between models, data and the use of mathematical software. The reader will find a broad selection of theoretical tools for practicing industrial mathematics, including the analysis of continuum models, probabilistic and discrete phenomena, and asymptotic and sensitivity analysis.

  19. Model : making

    OpenAIRE

    Bottle, Neil

    2013-01-01

    The Model : making exhibition was curated by Brian Kennedy in collaboration with Allies & Morrison in September 2013. For the London Design Festival, the Model : making exhibition looked at the increased use of new technologies by both craft-makers and architectural model makers. In both practices traditional ways of making by hand are increasingly being combined with the latest technologies of digital imaging, laser cutting, CNC machining and 3D printing. This exhibition focussed on ...

  20. Model building

    International Nuclear Information System (INIS)

    Frampton, Paul H.

    1998-01-01

    In this talk I begin with some general discussion of model building in particle theory, emphasizing the need for motivation and testability. Three illustrative examples are then described. The first is the Left-Right model which provides an explanation for the chirality of quarks and leptons. The second is the 331-model which offers a first step to understanding the three generations of quarks and leptons. Third and last is the SU(15) model which can accommodate the light leptoquarks possibly seen at HERA

  1. Model building

    International Nuclear Information System (INIS)

    Frampton, P.H.

    1998-01-01

    In this talk I begin with some general discussion of model building in particle theory, emphasizing the need for motivation and testability. Three illustrative examples are then described. The first is the Left-Right model which provides an explanation for the chirality of quarks and leptons. The second is the 331-model which offers a first step to understanding the three generations of quarks and leptons. Third and last is the SU(15) model which can accommodate the light leptoquarks possibly seen at HERA. copyright 1998 American Institute of Physics

  2. Modeling Documents with Event Model

    Directory of Open Access Journals (Sweden)

    Longhui Wang

    2015-08-01

    Full Text Available Currently deep learning has made great breakthroughs in visual and speech processing, mainly because it draws lessons from the hierarchical mode that brain deals with images and speech. In the field of NLP, a topic model is one of the important ways for modeling documents. Topic models are built on a generative model that clearly does not match the way humans write. In this paper, we propose Event Model, which is unsupervised and based on the language processing mechanism of neurolinguistics, to model documents. In Event Model, documents are descriptions of concrete or abstract events seen, heard, or sensed by people and words are objects in the events. Event Model has two stages: word learning and dimensionality reduction. Word learning is to learn semantics of words based on deep learning. Dimensionality reduction is the process that representing a document as a low dimensional vector by a linear mode that is completely different from topic models. Event Model achieves state-of-the-art results on document retrieval tasks.

  3. Animal models

    DEFF Research Database (Denmark)

    Gøtze, Jens Peter; Krentz, Andrew

    2014-01-01

    In this issue of Cardiovascular Endocrinology, we are proud to present a broad and dedicated spectrum of reviews on animal models in cardiovascular disease. The reviews cover most aspects of animal models in science from basic differences and similarities between small animals and the human...

  4. Battery Modeling

    NARCIS (Netherlands)

    Jongerden, M.R.; Haverkort, Boudewijn R.H.M.

    2008-01-01

    The use of mobile devices is often limited by the capacity of the employed batteries. The battery lifetime determines how long one can use a device. Battery modeling can help to predict, and possibly extend this lifetime. Many different battery models have been developed over the years. However,

  5. Didactical modelling

    DEFF Research Database (Denmark)

    Højgaard, Tomas; Hansen, Rune

    The purpose of this paper is to introduce Didactical Modelling as a research methodology in mathematics education. We compare the methodology with other approaches and argue that Didactical Modelling has its own specificity. We discuss the methodological “why” and explain why we find it useful...

  6. Design modelling

    NARCIS (Netherlands)

    Kempen, van A.; Kok, H.; Wagter, H.

    1992-01-01

    In Computer Aided Drafting three groups of three-dimensional geometric modelling can be recognized: wire frame, surface and solid modelling. One of the methods to describe a solid is by using a boundary based representation. The topology of the surface of a solid is the adjacency information between

  7. Education models

    NARCIS (Netherlands)

    Poortman, Sybilla; Sloep, Peter

    2006-01-01

    Educational models describes a case study on a complex learning object. Possibilities are investigated for using this learning object, which is based on a particular educational model, outside of its original context. Furthermore, this study provides advice that might lead to an increase in

  8. VENTILATION MODEL

    International Nuclear Information System (INIS)

    V. Chipman

    2002-01-01

    The purpose of the Ventilation Model is to simulate the heat transfer processes in and around waste emplacement drifts during periods of forced ventilation. The model evaluates the effects of emplacement drift ventilation on the thermal conditions in the emplacement drifts and surrounding rock mass, and calculates the heat removal by ventilation as a measure of the viability of ventilation to delay the onset of peak repository temperature and reduce its magnitude. The heat removal by ventilation is temporally and spatially dependent, and is expressed as the fraction of heat carried away by the ventilation air compared to the fraction of heat produced by radionuclide decay. One minus the heat removal is called the wall heat fraction, or the remaining amount of heat that is transferred via conduction to the surrounding rock mass. Downstream models, such as the ''Multiscale Thermohydrologic Model'' (BSC 2001), use the wall heat fractions as outputted from the Ventilation Model to initialize their postclosure analyses

  9. Modelling Constructs

    DEFF Research Database (Denmark)

    Kindler, Ekkart

    2009-01-01

    , these notations have been extended in order to increase expressiveness and to be more competitive. This resulted in an increasing number of notations and formalisms for modelling business processes and in an increase of the different modelling constructs provided by modelling notations, which makes it difficult......There are many different notations and formalisms for modelling business processes and workflows. These notations and formalisms have been introduced with different purposes and objectives. Later, influenced by other notations, comparisons with other tools, or by standardization efforts...... to compare modelling notations and to make transformations between them. One of the reasons is that, in each notation, the new concepts are introduced in a different way by extending the already existing constructs. In this chapter, we go the opposite direction: We show that it is possible to add most...

  10. STEREOMETRIC MODELLING

    Directory of Open Access Journals (Sweden)

    P. Grimaldi

    2012-07-01

    Full Text Available These mandatory guidelines are provided for preparation of papers accepted for publication in the series of Volumes of The The stereometric modelling means modelling achieved with : – the use of a pair of virtual cameras, with parallel axes and positioned at a mutual distance average of 1/10 of the distance camera-object (in practice the realization and use of a stereometric camera in the modeling program; – the shot visualization in two distinct windows – the stereoscopic viewing of the shot while modelling. Since the definition of "3D vision" is inaccurately referred to as the simple perspective of an object, it is required to add the word stereo so that "3D stereo vision " shall stand for "three-dimensional view" and ,therefore, measure the width, height and depth of the surveyed image. Thanks to the development of a stereo metric model , either real or virtual, through the "materialization", either real or virtual, of the optical-stereo metric model made visible with a stereoscope. It is feasible a continuous on line updating of the cultural heritage with the help of photogrammetry and stereometric modelling. The catalogue of the Architectonic Photogrammetry Laboratory of Politecnico di Bari is available on line at: http://rappresentazione.stereofot.it:591/StereoFot/FMPro?-db=StereoFot.fp5&-lay=Scheda&-format=cerca.htm&-view

  11. Modeling complexes of modeled proteins.

    Science.gov (United States)

    Anishchenko, Ivan; Kundrotas, Petras J; Vakser, Ilya A

    2017-03-01

    Structural characterization of proteins is essential for understanding life processes at the molecular level. However, only a fraction of known proteins have experimentally determined structures. This fraction is even smaller for protein-protein complexes. Thus, structural modeling of protein-protein interactions (docking) primarily has to rely on modeled structures of the individual proteins, which typically are less accurate than the experimentally determined ones. Such "double" modeling is the Grand Challenge of structural reconstruction of the interactome. Yet it remains so far largely untested in a systematic way. We present a comprehensive validation of template-based and free docking on a set of 165 complexes, where each protein model has six levels of structural accuracy, from 1 to 6 Å C α RMSD. Many template-based docking predictions fall into acceptable quality category, according to the CAPRI criteria, even for highly inaccurate proteins (5-6 Å RMSD), although the number of such models (and, consequently, the docking success rate) drops significantly for models with RMSD > 4 Å. The results show that the existing docking methodologies can be successfully applied to protein models with a broad range of structural accuracy, and the template-based docking is much less sensitive to inaccuracies of protein models than the free docking. Proteins 2017; 85:470-478. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  12. Graphical Rasch models

    DEFF Research Database (Denmark)

    Kreiner, Svend; Christensen, Karl Bang

    Rasch models; Partial Credit models; Rating Scale models; Item bias; Differential item functioning; Local independence; Graphical models......Rasch models; Partial Credit models; Rating Scale models; Item bias; Differential item functioning; Local independence; Graphical models...

  13. Supernova models

    International Nuclear Information System (INIS)

    Woosley, S.E.; California, University, Livermore, CA); Weaver, T.A.

    1981-01-01

    Recent progress in understanding the observed properties of type I supernovae as a consequence of the thermonuclear detonation of white dwarf stars and the ensuing decay of the Ni-56 produced therein is reviewed. The expected nucleosynthesis and gamma-line spectra for this model of type I explosions and a model for type II explosions are presented. Finally, a qualitatively new approach to the problem of massive star death and type II supernovae based upon a combination of rotation and thermonuclear burning is discussed. While the theoretical results of existing models are predicated upon the assumption of a successful core bounce calculation and the neglect of such two-dimensional effects as rotation and magnetic fields the new model suggests an entirely different scenario in which a considerable portion of the energy carried by an equatorially ejected blob is deposited in the red giant envelope overlying the mantle of the star

  14. Model theory

    CERN Document Server

    Hodges, Wilfrid

    1993-01-01

    An up-to-date and integrated introduction to model theory, designed to be used for graduate courses (for students who are familiar with first-order logic), and as a reference for more experienced logicians and mathematicians.

  15. Markov model

    Indian Academy of Sciences (India)

    2School of Water Resources, Indian Institute of Technology,. Kharagpur ... the most accepted method for modelling LULCC using current .... We used UTM coordinate system with zone 45 .... need to develop criteria for making decision about.

  16. Paleoclimate Modeling

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Computer simulations of past climate. Variables provided as model output are described by parameter keyword. In some cases the parameter keywords are a subset of all...

  17. Energy Models

    Science.gov (United States)

    Energy models characterize the energy system, its evolution, and its interactions with the broader economy. The energy system consists of primary resources, including both fossil fuels and renewables; power plants, refineries, and other technologies to process and convert these r...

  18. Linear Models

    CERN Document Server

    Searle, Shayle R

    2012-01-01

    This 1971 classic on linear models is once again available--as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.

  19. Ventilation models

    Science.gov (United States)

    Skaaret, Eimund

    Calculation procedures, used in the design of ventilating systems, which are especially suited for displacement ventilation in addition to linking it to mixing ventilation, are addressed. The two zone flow model is considered and the steady state and transient solutions are addressed. Different methods of supplying air are discussed, and different types of air flow are considered: piston flow, plane flow and radial flow. An evaluation model for ventilation systems is presented.

  20. Intraplate volcanism in the Danube Basin of NW Hungary: 3D geophysical modelling of the Late Miocene Pásztori volcano

    Science.gov (United States)

    Pánisová, Jaroslava; Balázs, Attila; Zalai, Zsófia; Bielik, Miroslav; Horváth, Ferenc; Harangi, Szabolcs; Schmidt, Sabine; Götze, Hans-Jürgen

    2017-12-01

    Three-dimensional geophysical modelling of the early Late Miocene Pásztori volcano (ca. 11-10 Ma) and adjacent area in the Little Hungarian Plain Volcanic Field of the Danube Basin was carried out to get an insight into the most prominent intra-crustal structures here. We have used gridded gravity and magnetic data, interpreted seismic reflection sections and borehole data combined with re-evaluated geological constraints. Based on petrological analysis of core samples from available six exploration boreholes, the volcanic rocks consist of a series of alkaline trachytic and trachyandesitic volcanoclastic and effusive rocks. The measured magnetic susceptibilities of these samples are generally very low suggesting a deeper magnetic source. The age of the modelled Pásztori volcano, buried beneath a 2 km-thick Late Miocene-to-Quaternary sedimentary sequence, is 10.4 +/- 0.3 Ma belonging to the dominantly normal C5 chron. Our model includes crustal domains with different effective induced magnetizations and densities: uppermost 0.3-1.8 km thick layer of volcanoclastics underlain by a trachytic-trachyandesitic coherent and volcanoclastic rock units of a maximum 2 km thickness, with a top situated at minimal depth of 2.3 km, and a deeper magmatic pluton in a depth range of 5-15 km. The 3D model of the Danube Basin is consistent with observed high ΔZ magnetic anomalies above the volcano, while the observed Bouguer gravity anomalies correlate better with the crystalline basement depth. Our analysis contributes to deeper understanding of the crustal architecture and the evolution of the basin accompanied by alkaline intraplate volcanism.