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Sample records for genome-scale metabolic model

  1. Genome scale metabolic modeling of cancer

    DEFF Research Database (Denmark)

    Nilsson, Avlant; Nielsen, Jens

    2016-01-01

    been used as scaffolds for analysis of high throughput data to allow mechanistic interpretation of changes in expression. Finally, GEMs allow quantitative flux predictions using flux balance analysis (FBA). Here we critically review the requirements for successful FBA simulations of cancer cells......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...... 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...

  2. Modeling cancer metabolism on a genome scale

    Science.gov (United States)

    Yizhak, Keren; Chaneton, Barbara; Gottlieb, Eyal; Ruppin, Eytan

    2015-01-01

    Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field. PMID:26130389

  3. Genome-scale modeling for metabolic engineering

    Energy Technology Data Exchange (ETDEWEB)

    Simeonidis, E; Price, ND

    2015-01-13

    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.

  4. 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.

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

    DEFF Research Database (Denmark)

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

    2004-01-01

    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.......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 metabolic...

  6. 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...... optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed. -. encompassing many biological processes and simulation strategies. -. and next-generation models enable new types of predictions. Here, three key...... 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....

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

    Science.gov (United States)

    King, Zachary A; Lloyd, Colton J; Feist, Adam M; Palsson, Bernhard O

    2015-12-01

    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 optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed--encompassing many biological processes and simulation strategies-and next-generation models enable new types of predictions. Here, three key 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 Models

    DEFF Research Database (Denmark)

    Bergdahl, Basti; Sonnenschein, Nikolaus; Machado, Daniel

    2016-01-01

    An introduction to genome-scale models, how to build and use them, will be given in this chapter. Genome-scale models have become an important part of systems biology and metabolic engineering, and are increasingly used in research, both in academica and in industry, both for modeling chemical pr...

  9. Comparative genome-scale metabolic modeling of actinomycetes : The topology of essential core metabolism

    NARCIS (Netherlands)

    Alam, Mohammad Tauqeer; Medema, Marnix H.; Takano, Eriko; Breitling, Rainer; Gojobori, Takashi

    2011-01-01

    Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of act

  10. Comparative genome-scale metabolic modeling of actinomycetes: the topology of essential core metabolism.

    NARCIS (Netherlands)

    Alam, M.T.; Medema, M.H.; Takano, E.; Breitling, R.

    2011-01-01

    Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of act

  11. Comparative genome-scale metabolic modeling of actinomycetes: the topology of essential core metabolism.

    NARCIS (Netherlands)

    Alam, M.T.; Medema, M.H.; Takano, E.; Breitling, R.

    2011-01-01

    Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of

  12. Comparative genome-scale metabolic modeling of actinomycetes : The topology of essential core metabolism

    NARCIS (Netherlands)

    Alam, Mohammad Tauqeer; Medema, Marnix H.; Takano, Eriko; Breitling, Rainer; Gojobori, Takashi

    2011-01-01

    Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of

  13. 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......, translation initiation, translation elongation, translation termination, translation elongation, and mRNA decay. Considering these information from the mechanisms of transcription and translation, we will include this stoichiometric reactions into the genome scale model for S. Cerevisiae to obtain the first...

  14. 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.

  15. MultiMetEval : Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

    NARCIS (Netherlands)

    Zakrzewski, Piotr; Medema, Marnix H.; Gevorgyan, Albert; Kierzek, Andrzej M.; Breitling, Rainer; Takano, Eriko; Fong, Stephen S.

    2012-01-01

    Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the co

  16. Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling

    DEFF Research Database (Denmark)

    Österlund, Tobias; Nookaew, Intawat; Bordel, Sergio

    2013-01-01

    ABSTRACT: BACKGROUND: The genome-scale metabolic model of Saccharomyces cerevisiae, first presented in 2003, was the first genome-scale network reconstruction for a eukaryotic organism. Since then continuous efforts have been made in order to improve and expand the yeast metabolic network. RESULTS......: Here we present iTO977, a comprehensive genome-scale metabolic model that contains more reactions, metabolites and genes than previous models. The model was constructed based on two earlier reconstructions, namely iIN800 and the consensus network, and then improved and expanded using gap......-filling methods and by introducing new reactions and pathways based on studies of the literature and databases. The model was shown to perform well both for growth simulations in different media and gene essentiality analysis for single and double knock-outs. Further, the model was used as a scaffold...

  17. Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum

    Directory of Open Access Journals (Sweden)

    Hirasawa Takashi

    2009-08-01

    Full Text Available Abstract Background In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA, and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates. Results The reconstructed genome-scale metabolic model of C. glutamicum contains 502 reactions and 423 metabolites. We collected the reactions and biomass components from the database and literatures, and made the model available for the flux balance analysis by filling gaps in the reaction networks and removing inadequate loop reactions. Using the framework of FBA and our genome-scale metabolic model, we first simulated the changes in the metabolic flux profiles that occur on changing the oxygen uptake rate. The predicted production yields of carbon dioxide and organic acids agreed well with the experimental data. The metabolic profiles of amino acid production phases were also investigated. A comprehensive gene deletion study was performed in which the effects of gene deletions on metabolic fluxes were simulated; this helped in the identification of several genes whose deletion resulted in an improvement in organic acid production. Conclusion The genome-scale metabolic model provides useful information for the evaluation of the metabolic capabilities and prediction of the metabolic characteristics of C. glutamicum. This can form a basis for the in silico design of C. glutamicum metabolic networks for improved bioproduction of desirable metabolites.

  18. Genome-scale modeling of human metabolism - a systems biology approach.

    Science.gov (United States)

    Mardinoglu, Adil; Gatto, Francesco; Nielsen, Jens

    2013-09-01

    Altered metabolism is linked to the appearance of various human diseases and a better understanding of disease-associated metabolic changes may lead to the identification of novel prognostic biomarkers and the development of new therapies. Genome-scale metabolic models (GEMs) have been employed for studying human metabolism in a systematic manner, as well as for understanding complex human diseases. In the past decade, such metabolic models - one of the fundamental aspects of systems biology - have started contributing to the understanding of the mechanistic relationship between genotype and phenotype. In this review, we focus on the construction of the Human Metabolic Reaction database, the generation of healthy cell type- and cancer-specific GEMs using different procedures, and the potential applications of these developments in the study of human metabolism and in the identification of metabolic changes associated with various disorders. We further examine how in silico genome-scale reconstructions can be employed to simulate metabolic flux distributions and how high-throughput omics data can be analyzed in a context-dependent fashion. Insights yielded from this mechanistic modeling approach can be used for identifying new therapeutic agents and drug targets as well as for the discovery of novel biomarkers. Finally, recent advancements in genome-scale modeling and the future challenge of developing a model of whole-body metabolism are presented. The emergent contribution of GEMs to personalized and translational medicine is also discussed. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. 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

    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...

  20. Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function

    Science.gov (United States)

    2017-01-01

    Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism's biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria. PMID:28133437

  1. Challenges in experimental data integration within genome-scale metabolic models

    Directory of Open Access Journals (Sweden)

    Képès François

    2010-04-01

    Full Text Available Abstract A report of the meeting "Challenges in experimental data integration within genome-scale metabolic models", Institut Henri Poincaré, Paris, October 10-11 2009, organized by the CNRS-MPG joint program in Systems Biology.

  2. Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

    Directory of Open Access Journals (Sweden)

    Julián Triana

    2014-08-01

    Full Text Available The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942.

  3. Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

    Science.gov (United States)

    Triana, Julián; Montagud†, Arnau; Siurana, Maria; Fuente, David; Urchueguía, Arantxa; Gamermann, Daniel; Torres, Javier; Tena, Jose; de Córdoba, Pedro Fernández; Urchueguía, Javier F.

    2014-01-01

    The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942. PMID:25141288

  4. Genome-scale metabolic models: reconstruction and analysis

    NARCIS (Netherlands)

    Baart, G.J.; Martens, D.E.

    2012-01-01

    Metabolism can be defined as the complete set of chemical reactions that occur in living organisms in order to maintain life. Enzymes are the main players in this process as they are responsible for catalyzing the chemical reactions. The enzyme-reaction relationships can be used for the reconstructi

  5. Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE.

    Science.gov (United States)

    Wang, Yuliang; Eddy, James A; Price, Nathan D

    2012-12-13

    Human tissues perform diverse metabolic functions. Mapping out these tissue-specific functions in genome-scale models will advance our understanding of the metabolic basis of various physiological and pathological processes. The global knowledgebase of metabolic functions categorized for the human genome (Human Recon 1) coupled with abundant high-throughput data now makes possible the reconstruction of tissue-specific metabolic models. However, the number of available tissue-specific models remains incomplete compared with the large diversity of human tissues. We developed a method called metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE). mCADRE is able to infer a tissue-specific network based on gene expression data and metabolic network topology, along with evaluation of functional capabilities during model building. mCADRE produces models with similar or better functionality and achieves dramatic computational speed up over existing methods. Using our method, we reconstructed draft genome-scale metabolic models for 126 human tissue and cell types. Among these, there are models for 26 tumor tissues along with their normal counterparts, and 30 different brain tissues. We performed pathway-level analyses of this large collection of tissue-specific models and identified the eicosanoid metabolic pathway, especially reactions catalyzing the production of leukotrienes from arachidnoic acid, as potential drug targets that selectively affect tumor tissues. This large collection of 126 genome-scale draft metabolic models provides a useful resource for studying the metabolic basis for a variety of human diseases across many tissues. The functionality of the resulting models and the fast computational speed of the mCADRE algorithm make it a useful tool to build and update tissue-specific metabolic models.

  6. 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.

  7. 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

    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...

  8. Pantograph: A template-based method for genome-scale metabolic model reconstruction.

    Science.gov (United States)

    Loira, Nicolas; Zhukova, Anna; Sherman, David James

    2015-04-01

    Genome-scale metabolic models are a powerful tool to study the inner workings of biological systems and to guide applications. The advent of cheap sequencing has brought the opportunity to create metabolic maps of biotechnologically interesting organisms. While this drives the development of new methods and automatic tools, network reconstruction remains a time-consuming process where extensive manual curation is required. This curation introduces specific knowledge about the modeled organism, either explicitly in the form of molecular processes, or indirectly in the form of annotations of the model elements. Paradoxically, this knowledge is usually lost when reconstruction of a different organism is started. We introduce the Pantograph method for metabolic model reconstruction. This method combines a template reaction knowledge base, orthology mappings between two organisms, and experimental phenotypic evidence, to build a genome-scale metabolic model for a target organism. Our method infers implicit knowledge from annotations in the template, and rewrites these inferences to include them in the resulting model of the target organism. The generated model is well suited for manual curation. Scripts for evaluating the model with respect to experimental data are automatically generated, to aid curators in iterative improvement. We present an implementation of the Pantograph method, as a toolbox for genome-scale model reconstruction, curation and validation. This open source package can be obtained from: http://pathtastic.gforge.inria.fr.

  9. Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom.

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    Jennifer Levering

    Full Text Available Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.

  10. Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom.

    Science.gov (United States)

    Levering, Jennifer; Broddrick, Jared; Dupont, Christopher L; Peers, Graham; Beeri, Karen; Mayers, Joshua; Gallina, Alessandra A; Allen, Andrew E; Palsson, Bernhard O; Zengler, Karsten

    2016-01-01

    Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.

  11. A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica

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    Loira Nicolas

    2012-05-01

    Full Text Available Abstract Background Yarrowia lipolytica is an oleaginous yeast which has emerged as an important microorganism for several biotechnological processes, such as the production of organic acids, lipases and proteases. It is also considered a good candidate for single-cell oil production. Although some of its metabolic pathways are well studied, its metabolic engineering is hindered by the lack of a genome-scale model that integrates the current knowledge about its metabolism. Results Combining in silico tools and expert manual curation, we have produced an accurate genome-scale metabolic model for Y. lipolytica. Using a scaffold derived from a functional metabolic model of the well-studied but phylogenetically distant yeast S. cerevisiae, we mapped conserved reactions, rewrote gene associations, added species-specific reactions and inserted specialized copies of scaffold reactions to account for species-specific expansion of protein families. We used physiological measures obtained under lab conditions to validate our predictions. Conclusions Y. lipolytica iNL895 represents the first well-annotated metabolic model of an oleaginous yeast, providing a base for future metabolic improvement, and a starting point for the metabolic reconstruction of other species in the Yarrowia clade and other oleaginous yeasts.

  12. redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models.

    Science.gov (United States)

    Ataman, Meric; Hernandez Gardiol, Daniel F; Fengos, Georgios; Hatzimanikatis, Vassily

    2017-07-01

    Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these "consistently-reduced" models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.

  13. redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models.

    Directory of Open Access Journals (Sweden)

    Meric Ataman

    2017-07-01

    Full Text Available Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these "consistently-reduced" models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.

  14. Integration of gene expression data into genome-scale metabolic models

    DEFF Research Database (Denmark)

    Åkesson, M.; Förster, Jochen; Nielsen, Jens

    2004-01-01

    of gene expression from chemostat and batch cultures of Saccharomyces cerevisiae were combined with a recently developed genome-scale model, and the computed metabolic flux distributions were compared to experimental values from carbon labeling experiments and metabolic network analysis. The integration......A framework for integration of transcriptome data into stoichiometric metabolic models to obtain improved flux predictions is presented. The key idea is to exploit the regulatory information in the expression data to give additional constraints on the metabolic fluxes in the model. Measurements...... of expression data resulted in improved predictions of metabolic behavior in batch cultures, enabling quantitative predictions of exchange fluxes as well as qualitative estimations of changes in intracellular fluxes. A critical discussion of correlation between gene expression and metabolic fluxes is given....

  15. 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

    Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics......, 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...... 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...

  16. A metabolite-centric view on flux distributions in genome-scale metabolic models.

    Science.gov (United States)

    Riemer, S Alexander; Rex, René; Schomburg, Dietmar

    2013-04-12

    Genome-scale metabolic models are important tools in systems biology. They permit the in-silico prediction of cellular phenotypes via mathematical optimisation procedures, most importantly flux balance analysis. Current studies on metabolic models mostly consider reaction fluxes in isolation. Based on a recently proposed metabolite-centric approach, we here describe a set of methods that enable the analysis and interpretation of flux distributions in an integrated metabolite-centric view. We demonstrate how this framework can be used for the refinement of genome-scale metabolic models. We applied the metabolite-centric view developed here to the most recent metabolic reconstruction of Escherichia coli. By compiling the balance sheets of a small number of currency metabolites, we were able to fully characterise the energy metabolism as predicted by the model and to identify a possibility for model refinement in NADPH metabolism. Selected branch points were examined in detail in order to demonstrate how a metabolite-centric view allows identifying functional roles of metabolites. Fructose 6-phosphate aldolase and the sedoheptulose bisphosphate bypass were identified as enzymatic reactions that can carry high fluxes in the model but are unlikely to exhibit significant activity in vivo. Performing a metabolite essentiality analysis, unconstrained import and export of iron ions could be identified as potentially problematic for the quality of model predictions. The system-wide analysis of split ratios and branch points allows a much deeper insight into the metabolic network than reaction-centric analyses. Extending an earlier metabolite-centric approach, the methods introduced here establish an integrated metabolite-centric framework for the interpretation of flux distributions in genome-scale metabolic networks that can complement the classical reaction-centric framework. Analysing fluxes and their metabolic context simultaneously opens the door to systems biological

  17. 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.

  18. 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.

  19. Improved Evidence-Based Genome-scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

    Energy Technology Data Exchange (ETDEWEB)

    Seaver, Samuel M.D.; Frelin, Oceane; Bradbury, Louis M.T.; 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.

  20. Improved Evidence-Based Genome-scale Metabolic Models for Maize Leaf, Embryo, and Endosperm.

    Directory of Open Access Journals (Sweden)

    Samuel eSeaver

    2015-03-01

    Full Text Available 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.

  1. Genome-scale Metabolic Reaction Modeling: a New Approach to Geomicrobial Kinetics

    Science.gov (United States)

    McKernan, S. E.; Shapiro, B.; Jin, Q.

    2014-12-01

    Geomicrobial rates, rates of microbial metabolism in natural environments, are a key parameter of theoretical and practical problems in geobiology and biogeochemistry. Both laboratory- and field-based approaches have been applied to study rates of geomicrobial processes. Laboratory-based approaches analyze geomicrobial kinetics by incubating environmental samples under controlled laboratory conditions. Field methods quantify geomicrobial rates by observing the progress of geomicrobial processes. To take advantage of recent development in biogeochemical modeling and genome-scale metabolic modeling, we suggest that geomicrobial rates can also be predicted by simulating metabolic reaction networks of microbes. To predict geomicrobial rates, we developed a genome-scale metabolic model that describes enzyme reaction networks of microbial metabolism, and simulated the network model by accounting for the kinetics and thermodynamics of enzyme reactions. The model is simulated numerically to solve cellular enzyme abundance and hence metabolic rates under the constraints of cellular physiology. The new modeling approach differs from flux balance analysis of system biology in that it accounts for the thermodynamics and kinetics of enzymatic reactions. It builds on subcellular metabolic reaction networks, and hence also differs from classical biogeochemical reaction modeling. We applied the new approach to Methanosarcina acetivorans, an anaerobic, marine methanogen capable of disproportionating acetate to carbon dioxide and methane. The input of the new model includes (1) enzyme reaction network of acetoclastic methanogenesis, and (2) representative geochemical conditions of freshwater sedimentary environments. The output of the simulation includes the proteomics, metabolomics, and energy and matter fluxes of M. acetivorans. Our simulation results demonstrate the predictive power of the new modeling approach. Specifically, the results illustrate how methanogenesis rates vary

  2. iAK692: A genome-scale metabolic model of Spirulina platensis C1

    Science.gov (United States)

    2012-01-01

    Background Spirulina (Arthrospira) platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438) genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium. Results In this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP) analysis. Conclusions This study proposes the first genome-scale model of S. platensis C1, iAK692, which is a predictive metabolic platform

  3. iAK692: A genome-scale metabolic model of Spirulina platensis C1

    Directory of Open Access Journals (Sweden)

    Klanchui Amornpan

    2012-06-01

    Full Text Available Abstract Background Spirulina (Arthrospira platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438 genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium. Results In this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP analysis. Conclusions This study proposes the first genome-scale model of S. platensis C1, iAK692, which is a

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

    Science.gov (United States)

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

    2013-01-01

    Summary: 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. Availability: 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. Contact: johnmay@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23766418

  5. 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...... antimetabolites using two cell lines with different phenotypic origins, and found that it is effective in inhibiting the growth of these cell lines. Using immunohistochemistry, we also showed high or moderate expression levels of proteins targeted by the validated antimetabolite. Identified anti-growth factors...

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

    Directory of Open Access Journals (Sweden)

    McAnulty Michael J

    2012-05-01

    Full Text Available Abstract Background 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. Results 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. Conclusions 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.

  7. Advances in the integration of transcriptional regulatory information into genome-scale metabolic models.

    Science.gov (United States)

    Vivek-Ananth, R P; Samal, Areejit

    2016-09-01

    A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks.

  8. A Genome-Scale Model of Shewanella piezotolerans Simulates Mechanisms of Metabolic Diversity and Energy Conservation.

    Science.gov (United States)

    Dufault-Thompson, Keith; Jian, Huahua; Cheng, Ruixue; Li, Jiefu; Wang, Fengping; Zhang, Ying

    2017-01-01

    Shewanella piezotolerans strain WP3 belongs to the group 1 branch of the Shewanella genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 Shewanella species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other Shewanella species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, Shewanella oneidensis MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea. IMPORTANCE The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation

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

    Science.gov (United States)

    Vital-Lopez, Francisco G; Reifman, Jaques; Wallqvist, Anders

    2015-10-01

    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 cells. Developing treatments against biofilms requires an understanding of bacterial biofilm-specific physiological traits. Research efforts have started to elucidate the intricate mechanisms underlying biofilm development. However, many aspects of these mechanisms are still poorly understood. Here, we addressed questions regarding biofilm metabolism using a genome-scale kinetic model of the P. aeruginosa metabolic network and gene expression profiles. Specifically, we computed metabolite concentration differences between known mutants with altered biofilm formation and the wild-type strain to predict drug targets against P. aeruginosa biofilms. We also simulated the altered metabolism driven by gene expression changes between biofilm and stationary growth-phase planktonic cultures. Our analysis suggests that the synthesis of important biofilm-related molecules, such as the quorum-sensing molecule Pseudomonas quinolone signal and the exopolysaccharide Psl, is regulated not only through the expression of genes in their own synthesis pathway, but also through the biofilm-specific expression of genes in pathways competing for precursors to these molecules. Finally, we investigated why mutants defective in anthranilate degradation have an impaired ability to form biofilms. Alternative to a previous hypothesis that this biofilm reduction is caused by a decrease in energy production, we proposed that the dysregulation of the synthesis of secondary metabolites derived from anthranilate and chorismate is what impaired the biofilms of these mutants. Notably, these insights generated through our kinetic model-based approach are not accessible from previous constraint-based model analyses of P. aeruginosa biofilm

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

    Science.gov (United States)

    Ma, Ding; Yang, Laurence; Fleming, Ronan M. T.; Thiele, Ines; Palsson, Bernhard O.; Saunders, Michael A.

    2017-01-01

    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 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 models have 70,000 constraints and variables and will grow larger). We have developed a quadruple-precision 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 problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.

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

    Science.gov (United States)

    Yoshikawa, Katsunori; Aikawa, Shimpei; Kojima, Yuta; Toya, Yoshihiro; Furusawa, Chikara; Kondo, Akihiko; Shimizu, Hiroshi

    2015-01-01

    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(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.

  12. 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.

  13. 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.

  14. Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling

    DEFF Research Database (Denmark)

    McGarrity, Sarah; Halldórsson, Haraldur; Palsson, Sirus

    2016-01-01

    of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford...... mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus...... on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health...

  15. Revealing the mystery of metabolic adaptations using a genome scale model of Leishmania infantum.

    Science.gov (United States)

    Subramanian, Abhishek; Sarkar, Ram Rup

    2017-08-31

    Human macrophage phagolysosome and sandfly midgut provide antagonistic ecological niches for Leishmania parasites to survive and proliferate. Parasites optimize their metabolism to utilize the available inadequate resources by adapting to those environments. Lately, a number of metabolomics studies have revived the interest to understand metabolic strategies utilized by the Leishmania parasite for optimal survival within its hosts. For the first time, we propose a reconstructed genome-scale metabolic model for Leishmania infantum JPCM5, the analyses of which not only captures observations reported by metabolomics studies in other Leishmania species but also divulges novel features of the L. infantum metabolome. Our results indicate that Leishmania metabolism is organized in such a way that the parasite can select appropriate alternatives to compensate for limited external substrates. A dynamic non-essential amino acid motif exists within the network that promotes a restricted redistribution of resources to yield required essential metabolites. Further, subcellular compartments regulate this metabolic re-routing by reinforcing the physiological coupling of specific reactions. This unique metabolic organization is robust against accidental errors and provides a wide array of choices for the parasite to achieve optimal survival.

  16. 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

    2016-06-08

    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 LTM (logical transformation of model) 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.

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

    DEFF Research Database (Denmark)

    Vongsangnak, Wanwipa; Olsen, Peter; Hansen, Kim;

    2008-01-01

    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......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...... of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other...

  18. 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 interpretati

  19. MapMaker and PathTracer for tracking carbon in genome-scale metabolic models.

    Science.gov (United States)

    Tervo, Christopher J; Reed, Jennifer L

    2016-05-01

    Constraint-based reconstruction and analysis (COBRA) modeling results can be difficult to interpret given the large numbers of reactions in genome-scale models. While paths in metabolic networks can be found, existing methods are not easily combined with constraint-based approaches. To address this limitation, two tools (MapMaker and PathTracer) were developed to find paths (including cycles) between metabolites, where each step transfers carbon from reactant to product. MapMaker predicts carbon transfer maps (CTMs) between metabolites using only information on molecular formulae and reaction stoichiometry, effectively determining which reactants and products share carbon atoms. MapMaker correctly assigned CTMs for over 97% of the 2,251 reactions in an Escherichia coli metabolic model (iJO1366). Using CTMs as inputs, PathTracer finds paths between two metabolites. PathTracer was applied to iJO1366 to investigate the importance of using CTMs and COBRA constraints when enumerating paths, to find active and high flux paths in flux balance analysis (FBA) solutions, to identify paths for putrescine utilization, and to elucidate a potential CO2 fixation pathway in E. coli. These results illustrate how MapMaker and PathTracer can be used in combination with constraint-based models to identify feasible, active, and high flux paths between metabolites.

  20. Genome-Scale Metabolic Modeling in the Simulation of Field-Scale Uranium Bioremediation

    Science.gov (United States)

    Yabusaki, S.; Wilkins, M.; Fang, Y.; Williams, K. H.; Waichler, S.; Long, P. E.

    2015-12-01

    Coupled variably saturated flow and biogeochemical reactive transport modeling is used to improve understanding of the processes, properties, and conditions controlling uranium bio-immobilization in a field experiment where uranium-contaminated groundwater was amended with acetate and bicarbonate. The acetate stimulates indigenous microorganisms that catalyze metal reduction, including the conversion of aqueous U(VI) to solid-phase U(IV), which effectively removes uranium from solution. The initiation of the bicarbonate amendment prior to biostimulation was designed to promote U(VI) desorption that would increase the aqueous U(VI) available for bioreduction. The three-dimensional simulations were able to largely reproduce the timing and magnitude of the physical, chemical and biological responses to the acetate and bicarbonate amendment in the context of changing water table elevation and gradient. A time series of groundwater proteomic samples exhibited correlations between the most abundant Geobacter metallireducens proteins and the genome-scale metabolic model-predicted fluxes of intra-cellular reactions associated with each of those proteins. The desorption of U(VI) induced by the bicarbonate amendment led to initially higher rates of bioreduction compared to locations with minimal bicarbonate exposure. After bicarbonate amendment ceased, bioreduction continued at these locations whereas U(VI) sorption was the dominant removal mechanism at the bicarbonate-impacted sites.

  1. Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling

    NARCIS (Netherlands)

    Wodke, J.A.; Puchalka, J.; Lluch-Senar, M.; Marcos, J.; Yus, E.; Godinho, M.; Gutierrez-Gallego, R.; Martins Dos Santos, V.A.P.; Serrano, L.; Klipp, E.; Maier, T.

    2013-01-01

    Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the met

  2. Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation.

    Science.gov (United States)

    Flahaut, Nicolas A L; Wiersma, Anne; van de Bunt, Bert; Martens, Dirk E; Schaap, Peter J; Sijtsma, Lolke; Dos Santos, Vitor A Martins; de Vos, Willem M

    2013-10-01

    Lactococcus lactis subsp. cremoris MG1363 is a paradigm strain for lactococci used in industrial dairy fermentations. However, despite of its importance for process development, no genome-scale metabolic model has been reported thus far. Moreover, current models for other lactococci only focus on growth and sugar degradation. A metabolic model that includes nitrogen metabolism and flavor-forming pathways is instrumental for the understanding and designing new industrial applications of these lactic acid bacteria. A genome-scale, constraint-based model of the metabolism and transport in L. lactis MG1363, accounting for 518 genes, 754 reactions, and 650 metabolites, was developed and experimentally validated. Fifty-nine reactions are directly or indirectly involved in flavor formation. Flux Balance Analysis and Flux Variability Analysis were used to investigate flux distributions within the whole metabolic network. Anaerobic carbon-limited continuous cultures were used for estimating the energetic parameters. A thorough model-driven analysis showing a highly flexible nitrogen metabolism, e.g., branched-chain amino acid catabolism which coupled with the redox balance, is pivotal for the prediction of the formation of different flavor compounds. Furthermore, the model predicted the formation of volatile sulfur compounds as a result of the fermentation. These products were subsequently identified in the experimental fermentations carried out. Thus, the genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation. The model provided valuable insights into the metabolic networks underlying flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.

  3. Designing intracellular metabolism for production of target compounds by introducing a heterologous metabolic reaction based on a Synechosystis sp. 6803 genome-scale model.

    Science.gov (United States)

    Shirai, Tomokazu; Osanai, Takashi; Kondo, Akihiko

    2016-01-18

    Designing optimal intracellular metabolism is essential for using microorganisms to produce useful compounds. Computerized calculations for flux balance analysis utilizing a genome-scale model have been performed for such designs. Many genome-scale models have been developed for different microorganisms. However, optimal designs of intracellular metabolism aimed at producing a useful compound often utilize metabolic reactions of only the host microbial cells. In the present study, we added reactions other than the metabolic reactions with Synechosystis sp. 6803 as a host to its genome-scale model, and constructed a metabolic model of hybrid cells (SyHyMeP) using computerized analysis. Using this model provided a metabolic design that improves the theoretical yield of succinic acid, which is a useful compound. Constructing the SyHyMeP model enabled new metabolic designs for producing useful compounds. In the present study, we developed a metabolic design that allowed for improved theoretical yield in the production of succinic acid during glycogen metabolism by Synechosystis sp. 6803. The theoretical yield of succinic acid production using a genome-scale model of these cells was 1.00 mol/mol-glucose, but use of the SyHyMeP model enabled a metabolic design with which a 33 % increase in theoretical yield is expected due to the introduction of isocitrate lyase, adding activations of endogenous tree reactions via D-glycerate in Synechosystis sp. 6803. The SyHyMeP model developed in this study has provided a new metabolic design that is not restricted only to the metabolic reactions of individual microbial cells. The concept of construction of this model requires only replacement of the genome-scale model of the host microbial cells and can thus be applied to various useful microorganisms for metabolic design to produce compounds.

  4. Further developments towards a genome-scale metabolic model of yeast

    Directory of Open Access Journals (Sweden)

    Dunn Warwick B

    2010-10-01

    Full Text Available Abstract Background To date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity. Results We have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites - significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions. Conclusions We report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.

  5. 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.

  6. 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.

  7. 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.

  8. The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum.

    Directory of Open Access Journals (Sweden)

    Rasmus Agren

    Full Text Available 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.

  9. Integration of genome-scale modeling and transcript profiling reveals metabolic pathways underlying light and temperature acclimation in Arabidopsis.

    Science.gov (United States)

    Töpfer, Nadine; Caldana, Camila; Grimbs, Sergio; Willmitzer, Lothar; Fernie, Alisdair R; Nikoloski, Zoran

    2013-04-01

    Understanding metabolic acclimation of plants to challenging environmental conditions is essential for dissecting the role of metabolic pathways in growth and survival. As stresses involve simultaneous physiological alterations across all levels of cellular organization, a comprehensive characterization of the role of metabolic pathways in acclimation necessitates integration of genome-scale models with high-throughput data. Here, we present an integrative optimization-based approach, which, by coupling a plant metabolic network model and transcriptomics data, can predict the metabolic pathways affected in a single, carefully controlled experiment. Moreover, we propose three optimization-based indices that characterize different aspects of metabolic pathway behavior in the context of the entire metabolic network. We demonstrate that the proposed approach and indices facilitate quantitative comparisons and characterization of the plant metabolic response under eight different light and/or temperature conditions. The predictions of the metabolic functions involved in metabolic acclimation of Arabidopsis thaliana to the changing conditions are in line with experimental evidence and result in a hypothesis about the role of homocysteine-to-Cys interconversion and Asn biosynthesis. The approach can also be used to reveal the role of particular metabolic pathways in other scenarios, while taking into consideration the entirety of characterized plant metabolism.

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

    Science.gov (United States)

    2017-03-22

    Parasitology: Drugs and Drug Resistance journal homepage: www.elsevier .com/locate/ i jpddrUsing a genome-scale metabolic network model to elucidate...the mechanism of chloroquine action in Plasmodium falciparum Shivendra G. Tewari a , *, Sean T. Prigge b, Jaques Reifman a , Anders Wallqvist a , * a ...authors. E-mail addresses: stewari@bhsai.org (S.G. (S.T. Prigge), jaques.reifman.civ@mail.mil (J. Reifma mil ( A . Wallqvist). http://dx.doi.org/10.1016

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

    Directory of Open Access Journals (Sweden)

    João Gonçalo Rocha Cardoso

    2015-02-01

    Full Text Available 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 variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented 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, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.

  12. Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity.

    Science.gov (United States)

    Bosi, Emanuele; Monk, Jonathan M; Aziz, Ramy K; Fondi, Marco; Nizet, Victor; Palsson, Bernhard Ø

    2016-06-28

    Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world.

  13. Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity

    Science.gov (United States)

    Bosi, Emanuele; Monk, Jonathan M.; Aziz, Ramy K.; Fondi, Marco; Nizet, Victor; Palsson, Bernhard Ø.

    2016-01-01

    Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus. These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world. PMID:27286824

  14. 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.

  15. 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.

  16. Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism.

    Directory of Open Access Journals (Sweden)

    Ahmad A Mannan

    Full Text Available An understanding of the dynamics of the metabolic profile of a bacterial cell is sought from a dynamical systems analysis of kinetic models. This modelling formalism relies on a deterministic mathematical description of enzyme kinetics and their metabolite regulation. However, it is severely impeded by the lack of available kinetic information, limiting the size of the system that can be modelled. Furthermore, the subsystem of the metabolic network whose dynamics can be modelled is faced with three problems: how to parameterize the model with mostly incomplete steady state data, how to close what is now an inherently open system, and how to account for the impact on growth. In this study we address these challenges of kinetic modelling by capitalizing on multi-'omics' steady state data and a genome-scale metabolic network model. We use these to generate parameters that integrate knowledge embedded in the genome-scale metabolic network model, into the most comprehensive kinetic model of the central carbon metabolism of E. coli realized to date. As an application, we performed a dynamical systems analysis of the resulting enriched model. This revealed bistability of the central carbon metabolism and thus its potential to express two distinct metabolic states. Furthermore, since our model-informing technique ensures both stable states are constrained by the same thermodynamically feasible steady state growth rate, the ensuing bistability represents a temporal coexistence of the two states, and by extension, reveals the emergence of a phenotypically heterogeneous population.

  17. Flux balance analysis of genome-scale metabolic model of rice (Oryza sativa): Aiming to increase biomass

    Indian Academy of Sciences (India)

    Rahul Shaw; Sudip Kundu

    2015-10-01

    Due to socio-economic reasons, it is essential to design efficient stress-tolerant, more nutritious, high yielding rice varieties. A systematic understanding of the rice cellular metabolism is essential for this purpose. Here, we analyse a genome-scale metabolic model of rice leaf using Flux Balance Analysis to investigate whether it has potential metabolic flexibility to increase the biosynthesis of any of the biomass components. We initially simulate the metabolic responses under an objective to maximize the biomass components. Using the estimated maximum value of biomass synthesis as a constraint, we further simulate the metabolic responses optimizing the cellular economy. Depending on the physiological conditions of a cell, the transport capacities of intracellular transporters (ICTs) can vary. To mimic this physiological state, we randomly vary the ICTs’ transport capacities and investigate their effects. The results show that the rice leaf has the potential to increase glycine and starch in a wide range depending on the ICTs’ transport capacities. The predicted biosynthesis pathways vary slightly at the two different optimization conditions. With the constraint of biomass composition, the cell also has the metabolic plasticity to fix a wide range of carbon-nitrogen ratio.

  18. 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.

  19. Reconstruction and analysis of genome-scale metabolic model of a photosynthetic bacterium

    Directory of Open Access Journals (Sweden)

    Urchueguía Javier F

    2010-11-01

    Full Text Available Abstract Background Synechocystis sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform - an attractive cell factory capable of using CO2 and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of Synechocystis sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the Synechocystis metabolic network. Results We report the most comprehensive metabolic model of Synechocystis sp. PCC6803 available, iSyn669, which includes 882 reactions, associated with 669 genes, and 790 metabolites. The model includes a detailed biomass equation which encompasses elementary building blocks that are needed for cell growth, as well as a detailed stoichiometric representation of photosynthesis. We demonstrate applicability of iSyn669 for stoichiometric analysis by simulating three physiologically relevant growth conditions of Synechocystis sp. PCC6803, and through in silico metabolic engineering simulations that allowed identification of a set of gene knock-out candidates towards enhanced succinate production. Gene essentiality and hydrogen production potential have also been assessed. Furthermore, iSyn669 was used as a transcriptomic data integration scaffold and thereby we found metabolic hot-spots around which gene regulation is dominant during light-shifting growth regimes. Conclusions iSyn669 provides a platform for facilitating the development of cyanobacteria as microbial cell factories.

  20. MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models

    OpenAIRE

    2016-01-01

    Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes...

  1. MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models

    Directory of Open Access Journals (Sweden)

    Maike Kathrin Aurich

    2016-08-01

    Full Text Available Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools , we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.

  2. MetaboTools: A Comprehensive Toolbox for Analysis of Genome-Scale Metabolic Models.

    Science.gov (United States)

    Aurich, Maike K; Fleming, Ronan M T; Thiele, Ines

    2016-01-01

    Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.

  3. 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.

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

    Directory of Open Access Journals (Sweden)

    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

  5. Integration of Genome-Scale Metabolic Nodels of Iron-Reducing Bacteria With Subsurface Flow and Geochemical Reactive Transport Models

    Science.gov (United States)

    Scheibe, T. D.; Mahadevan, R.; Fang, Y.; Garg, S.; Long, P. E.; Lovley, D. M.

    2008-12-01

    Several field and laboratory experiments have demonstrated that the growth and activity of iron-reducing bacteria can be stimulated in many subsurface environments by amendment of groundwater with a soluble electron donor. Under strong iron-reducing conditions, these organisms mediate reactions that can impact a wide range of subsurface contaminants including chlorinated hydrocarbons, metals, and radionuclides. Therefore there is strong interest in in-situ bioremediation as a potential technology for cleanup of contaminated aquifers. To evaluate and design bioremediation systems, as well as to evaluate the viability of monitored natural attenuation as an alternative, quantitative models of biogeochemically reactive transport are needed. To date, most such models represent microbial activity in terms of kinetic rate (e.g., Monod- type) formulations. Such models do not account for fundamental changes in microbial functionality (such as utilization of alternative respiratory pathways) that occur as the result of spatial and temporal variations in the geochemical environment experienced by microorganisms. Constraint-based genome-scale in silico models of microbial metabolism present an alternative to simplified rate formulations that provide flexibility to account for changes in microbial function in response to local geochemical conditions. We have developed and applied a methodology for coupling a constraint-based in silico model of Geobacter sulfurreducens with a conventional model of groundwater flow, transport, and geochemical reaction. Two uses of the in silico model are tested: 1) incorporation of modified microbial growth yield coefficients based on the in silico model, and 2) variation of reaction rates in a reactive transport model based on in silico modeling of a range of local geochemical conditions. Preliminary results from this integrated model will be presented.

  6. Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

    Science.gov (United States)

    Tartakovsky, G. D.; Tartakovsky, A. M.; Scheibe, T. D.; Fang, Y.; Mahadevan, R.; Lovley, D. R.

    2013-09-01

    Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated with microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparison to prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model

  7. Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

    Energy Technology Data Exchange (ETDEWEB)

    Tartakovsky, Guzel D.; Tartakovsky, Alexandre M.; Scheibe, Timothy D.; Fang, Yilin; Mahadevan, Radhakrishnan; Lovley, Derek R.

    2013-09-07

    Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated with microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparisonto prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model under

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

    DEFF Research Database (Denmark)

    Olivares Hernandez, Roberto

    by a rapidly growing cell. To extend the model including protein synthesis, from the survey of the available literature was possible to identify a few enzymatic reactions and gene functions in the early steps of gene expression for proteins: mRNA transcription, mRNA processing, mRNA export out of the nucleus...

  9. 13C metabolic flux analysis at a genome-scale.

    Science.gov (United States)

    Gopalakrishnan, Saratram; Maranas, Costas D

    2015-11-01

    Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism. They typically omit degradation pathways, complete cofactor balances, and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up mapping models to a genome-scale. The core mapping model employed in this study accounts for (75 reactions and 65 metabolites) primarily from central metabolism. The genome-scale metabolic mapping model (GSMM) (697 reaction and 595 metabolites) is constructed using as a basis the iAF1260 model upon eliminating reactions guaranteed not to carry flux based on growth and fermentation data for a minimal glucose growth medium. Labeling data for 17 amino acid fragments obtained from cells fed with glucose labeled at the second carbon was used to obtain fluxes and ranges. Metabolic fluxes and confidence intervals are estimated, for both core and genome-scale mapping models, by minimizing the sum of square of differences between predicted and experimentally measured labeling patterns using the EMU decomposition algorithm. Overall, we find that both topology and estimated values of the metabolic fluxes remain largely consistent between core and GSM model. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non

  10. Integration and Validation of the Genome-Scale Metabolic Models of Pichia pastoris: A Comprehensive Update of Protein Glycosylation Pathways, Lipid and Energy Metabolism.

    Science.gov (United States)

    Tomàs-Gamisans, Màrius; Ferrer, Pau; Albiol, Joan

    2016-01-01

    Genome-scale metabolic models (GEMs) are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is one of the most widely used organisms for heterologous protein expression. There are different GEMs for this methylotrophic yeast of which the most relevant and complete in the published literature are iPP668, PpaMBEL1254 and iLC915. However, these three models differ regarding certain pathways, terminology for metabolites and reactions and annotations. Moreover, GEMs for some species are typically built based on the reconstructed models of related model organisms. In these cases, some organism-specific pathways could be missing or misrepresented. In order to provide an updated and more comprehensive GEM for P. pastoris, we have reconstructed and validated a consensus model integrating and merging all three existing models. In this step a comprehensive review and integration of the metabolic pathways included in each one of these three versions was performed. In addition, the resulting iMT1026 model includes a new description of some metabolic processes. Particularly new information described in recently published literature is included, mainly related to fatty acid and sphingolipid metabolism, glycosylation and cell energetics. Finally the reconstructed model was tested and validated, by comparing the results of the simulations with available empirical physiological datasets results obtained from a wide range of experimental conditions, such as different carbon sources, distinct oxygen availability conditions, as well as producing of two different recombinant proteins. In these simulations, the

  11. Integration and Validation of the Genome-Scale Metabolic Models of Pichia pastoris: A Comprehensive Update of Protein Glycosylation Pathways, Lipid and Energy Metabolism.

    Directory of Open Access Journals (Sweden)

    Màrius Tomàs-Gamisans

    Full Text Available Genome-scale metabolic models (GEMs are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is one of the most widely used organisms for heterologous protein expression. There are different GEMs for this methylotrophic yeast of which the most relevant and complete in the published literature are iPP668, PpaMBEL1254 and iLC915. However, these three models differ regarding certain pathways, terminology for metabolites and reactions and annotations. Moreover, GEMs for some species are typically built based on the reconstructed models of related model organisms. In these cases, some organism-specific pathways could be missing or misrepresented.In order to provide an updated and more comprehensive GEM for P. pastoris, we have reconstructed and validated a consensus model integrating and merging all three existing models. In this step a comprehensive review and integration of the metabolic pathways included in each one of these three versions was performed. In addition, the resulting iMT1026 model includes a new description of some metabolic processes. Particularly new information described in recently published literature is included, mainly related to fatty acid and sphingolipid metabolism, glycosylation and cell energetics. Finally the reconstructed model was tested and validated, by comparing the results of the simulations with available empirical physiological datasets results obtained from a wide range of experimental conditions, such as different carbon sources, distinct oxygen availability conditions, as well as producing of two different recombinant proteins. In

  12. 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; Wilkins, Michael J; Yabusaki, Steven B; Lipton, Mary S; Long, Philip E

    2012-12-01

    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.

  13. 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

  14. 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 M49

  15. Construction of robust dynamic genome-scale metabolic model structures of Saccharomyces cerevisiae through iterative re-parameterization.

    Science.gov (United States)

    Sánchez, Benjamín J; Pérez-Correa, José R; Agosin, Eduardo

    2014-09-01

    Dynamic flux balance analysis (dFBA) has been widely employed in metabolic engineering to predict the effect of genetic modifications and environmental conditions in the cell׳s metabolism during dynamic cultures. However, the importance of the model parameters used in these methodologies has not been properly addressed. Here, we present a novel and simple procedure to identify dFBA parameters that are relevant for model calibration. The procedure uses metaheuristic optimization and pre/post-regression diagnostics, fixing iteratively the model parameters that do not have a significant role. We evaluated this protocol in a Saccharomyces cerevisiae dFBA framework calibrated for aerobic fed-batch and anaerobic batch cultivations. The model structures achieved have only significant, sensitive and uncorrelated parameters and are able to calibrate different experimental data. We show that consumption, suboptimal growth and production rates are more useful for calibrating dynamic S. cerevisiae metabolic models than Boolean gene expression rules, biomass requirements and ATP maintenance.

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

    DEFF Research Database (Denmark)

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

    2003-01-01

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

  17. Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?

    Directory of Open Access Journals (Sweden)

    Parizad Babaei

    2014-01-01

    Full Text Available To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

  18. 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.

  19. New approach for phylogenetic tree recovery based on genome-scale metabolic networks.

    Science.gov (United States)

    Gamermann, Daniel; Montagud, Arnaud; Conejero, J Alberto; Urchueguía, Javier F; de Córdoba, Pedro Fernández

    2014-07-01

    A wide range of applications and research has been done with genome-scale metabolic models. In this work, we describe an innovative methodology for comparing metabolic networks constructed from genome-scale metabolic models and how to apply this comparison in order to infer evolutionary distances between different organisms. Our methodology allows a quantification of the metabolic differences between different species from a broad range of families and even kingdoms. This quantification is then applied in order to reconstruct phylogenetic trees for sets of various organisms.

  20. Genome-scale constraint-based modeling of Geobacter metallireducens

    Directory of Open Access Journals (Sweden)

    Famili Iman

    2009-01-01

    Full Text Available Abstract Background Geobacter metallireducens was the first organism that can be grown in pure culture to completely oxidize organic compounds with Fe(III oxide serving as electron acceptor. Geobacter species, including G. sulfurreducens and G. metallireducens, are used for bioremediation and electricity generation from waste organic matter and renewable biomass. The constraint-based modeling approach enables the development of genome-scale in silico models that can predict the behavior of complex biological systems and their responses to the environments. Such a modeling approach was applied to provide physiological and ecological insights on the metabolism of G. metallireducens. Results The genome-scale metabolic model of G. metallireducens was constructed to include 747 genes and 697 reactions. Compared to the G. sulfurreducens model, the G. metallireducens metabolic model contains 118 unique reactions that reflect many of G. metallireducens' specific metabolic capabilities. Detailed examination of the G. metallireducens model suggests that its central metabolism contains several energy-inefficient reactions that are not present in the G. sulfurreducens model. Experimental biomass yield of G. metallireducens growing on pyruvate was lower than the predicted optimal biomass yield. Microarray data of G. metallireducens growing with benzoate and acetate indicated that genes encoding these energy-inefficient reactions were up-regulated by benzoate. These results suggested that the energy-inefficient reactions were likely turned off during G. metallireducens growth with acetate for optimal biomass yield, but were up-regulated during growth with complex electron donors such as benzoate for rapid energy generation. Furthermore, several computational modeling approaches were applied to accelerate G. metallireducens research. For example, growth of G. metallireducens with different electron donors and electron acceptors were studied using the genome-scale

  1. Current state of genome-scale modeling in filamentous fungi.

    Science.gov (United States)

    Brandl, Julian; Andersen, Mikael R

    2015-06-01

    The group of filamentous fungi contains important species used in industrial biotechnology for acid, antibiotics and enzyme production. Their unique lifestyle turns these organisms into a valuable genetic reservoir of new natural products and biomass degrading enzymes that has not been used to full capacity. One of the major bottlenecks in the development of new strains into viable industrial hosts is the alteration of the metabolism towards optimal production. Genome-scale models promise a reduction in the time needed for metabolic engineering by predicting the most potent targets in silico before testing them in vivo. The increasing availability of high quality models and molecular biological tools for manipulating filamentous fungi renders the model-guided engineering of these fungal factories possible with comprehensive metabolic networks. A typical fungal model contains on average 1138 unique metabolic reactions and 1050 ORFs, making them a vast knowledge-base of fungal metabolism. In the present review we focus on the current state as well as potential future applications of genome-scale models in filamentous fungi.

  2. Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of Clostridium ljungdahlii

    Energy Technology Data Exchange (ETDEWEB)

    Nagarajan, H; Sahin, M; Nogales, J; Latif, H; Lovley, DR; Ebrahim, A; Zengler, K

    2013-11-25

    Background: The metabolic capabilities of acetogens to ferment a wide range of sugars, to grow autotrophically on H-2/CO2, and more importantly on synthesis gas (H-2/CO/CO2) make them very attractive candidates as production hosts for biofuels and biocommodities. Acetogenic metabolism is considered one of the earliest modes of bacterial metabolism. A thorough understanding of various factors governing the metabolism, in particular energy conservation mechanisms, is critical for metabolic engineering of acetogens for targeted production of desired chemicals. Results: Here, we present the genome-scale metabolic network of Clostridium ljungdahlii, the first such model for an acetogen. This genome-scale model (iHN637) consisting of 637 genes, 785 reactions, and 698 metabolites captures all the major central metabolic and biosynthetic pathways, in particular pathways involved in carbon fixation and energy conservation. A combination of metabolic modeling, with physiological and transcriptomic data provided insights into autotrophic metabolism as well as aided the characterization of a nitrate reduction pathway in C. ljungdahlii. Analysis of the iHN637 metabolic model revealed that flavin based electron bifurcation played a key role in energy conservation during autotrophic growth and helped identify genes for some of the critical steps in this mechanism. Conclusions: iHN637 represents a predictive model that recapitulates experimental data, and provides valuable insights into the metabolic response of C. ljungdahlii to genetic perturbations under various growth conditions. Thus, the model will be instrumental in guiding metabolic engineering of C. ljungdahlii for the industrial production of biocommodities and biofuels.

  3. Current state of genome-scale modeling in filamentous fungi

    DEFF Research Database (Denmark)

    Brandl, Julian; Andersen, Mikael Rørdam

    2015-01-01

    The group of filamentous fungi contains important species used in industrial biotechnology for acid, antibiotics and enzyme production. Their unique lifestyle turns these organisms into a valuable genetic reservoir of new natural products and biomass degrading enzymes that has not been used to full...... testing them in vivo. The increasing availability of high quality models and molecular biological tools for manipulating filamentous fungi renders the model-guided engineering of these fungal factories possible with comprehensive metabolic networks. A typical fungal model contains on average 1138 unique...... metabolic reactions and 1050 ORFs, making them a vast knowledge-base of fungal metabolism. In the present review we focus on the current state as well as potential future applications of genome-scale models in filamentous fungi....

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

    DEFF Research Database (Denmark)

    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...

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

    DEFF Research Database (Denmark)

    McCloskey, Douglas; Palsson, Bernhard; Feist, Adam

    2013-01-01

    The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction...... of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective...... on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype-phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges...

  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...... and lactate. Comparable flux values between in silico model and experimental values were seen, although some differences in the phenotypic behavior between the model and the experimental data were observed,...

  7. 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.

  8. Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism.

    Directory of Open Access Journals (Sweden)

    Rajib Saha

    Full Text Available 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.

  9. 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.

  10. Interplay between Constraints, Objectives, and Optimality for Genome-Scale Stoichiometric Models

    NARCIS (Netherlands)

    Maarleveld, T.R.; Wortel, M.; Olivier, B.G.; Teusink, B.; Bruggeman, F.J.

    2015-01-01

    High-throughput data generation and genome-scale stoichiometric models have greatly facilitated the comprehensive study of metabolic networks. The computation of all feasible metabolic routes with these models, given stoichiometric, thermodynamic, and steady-state constraints, provides important ins

  11. 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 cellular...... growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started....

  12. 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.

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

    Science.gov (United States)

    King, Zachary A; Lu, Justin; Dräger, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A; Ebrahim, Ali; Palsson, Bernhard O; Lewis, Nathan E

    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 repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.

  14. 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

    Metabolic flux analysis (MFA) is considered to be the gold standard for determining the intracellular flux distribution of biological systems. The majority of work using MFA has been limited to core models of metabolism due to challenges in implementing genome-scale MFA and the undesirable trade...... distributions (MIDs),(1) it was found that a total of 232 net fluxes of central and peripheral metabolism could be resolved in the E. coli network. The increase in scope was shown to cover the full biosynthetic route to an expanded set of bioproduction pathways, which should facilitate applications......-off between increased scope and decreased precision in flux estimations. This work presents a tunable workflow for expanding the scope of MFA to the genome-scale without trade-offs in flux precision. The genome-scale MFA model presented here, iDM2014, accounts for 537 net reactions, which includes the core...

  15. 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

    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......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...... 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....

  16. An experimentally-supported genome-scale metabolic network reconstruction for Yersinia pestis CO92

    Directory of Open Access Journals (Sweden)

    Motin Vladimir L

    2011-10-01

    Full Text Available Abstract Background Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas. Results Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain. Conclusions Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, provides an in silico platform with which to investigate the metabolism of this important human pathogen.

  17. An Experimentally-Supported Genome-Scale Metabolic Network Reconstruction for Yersinia pestis CO92

    Energy Technology Data Exchange (ETDEWEB)

    Charusanti, Pep; Chauhan, Sadhana; Mcateer, Kathleen; Lerman, Joshua A.; Hyduke, Daniel R.; Motin, Vladimir L.; Ansong, Charles; Adkins, Joshua N.; Palsson, Bernhard O.

    2011-10-13

    Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas. Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain. Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, thus provides an in silico platform with which to investigate the metabolism of this important human pathogen.

  18. 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...... transcriptional regulatory interactions to genome-scale metabolic models in a quantitative manner....

  19. Genome-scale metabolic representation of Amycolatopsis balhimycina

    DEFF Research Database (Denmark)

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

    2012-01-01

    EC numbers, 647 metabolites and 1,363 metabolic reactions. During the analysis of the metabolic model, linear, quadratic and evolutionary programming algorithms using flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), and OptGene, respectively were applied as well as phenotypic...... biosynthesis in Amycolatopsis balhimycina. The balhimycin yield obtained by A. balhimycina is, however, low and there is therefore a need to improve balhimycin production. In this study, we performed genome sequencing, assembly and annotation analysis of A. balhimycina and further used these annotated data...... 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...

  20. 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.

  1. Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species

    Science.gov (United States)

    Pitkänen, Esa; Jouhten, Paula; Hou, Jian; Syed, Muhammad Fahad; Blomberg, Peter; Kludas, Jana; Oja, Merja; Holm, Liisa; Penttilä, Merja; Rousu, Juho; Arvas, Mikko

    2014-01-01

    We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. PMID:24516375

  2. Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species.

    Directory of Open Access Journals (Sweden)

    Esa Pitkänen

    2014-02-01

    Full Text Available We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.

  3. 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.

  4. Genome-scale metabolic flux analysis of Streptomyces lividans growing on a complex medium.

    Science.gov (United States)

    D'Huys, Pieter-Jan; Lule, Ivan; Vercammen, Dominique; Anné, Jozef; Van Impe, Jan F; Bernaerts, Kristel

    2012-09-15

    Constraint-based metabolic modeling comprises various excellent tools to assess experimentally observed phenotypic behavior of micro-organisms in terms of intracellular metabolic fluxes. In combination with genome-scale metabolic networks, micro-organisms can be investigated in much more detail and under more complex environmental conditions. Although complex media are ubiquitously applied in industrial fermentations and are often a prerequisite for high protein secretion yields, such multi-component conditions are seldom investigated using genome-scale flux analysis. In this paper, a systematic and integrative approach is presented to determine metabolic fluxes in Streptomyces lividans TK24 grown on a nutritious and complex medium. Genome-scale flux balance analysis and randomized sampling of the solution space are combined to extract maximum information from exometabolome profiles. It is shown that biomass maximization cannot predict the observed metabolite production pattern as such. Although this cellular objective commonly applies to batch fermentation data, both input and output constraints are required to reproduce the measured biomass production rate. Rich media hence not necessarily lead to maximum biomass growth. To eventually identify a unique intracellular flux vector, a hierarchical optimization of cellular objectives is adopted. Out of various tested secondary objectives, maximization of the ATP yield per flux unit returns the closest agreement with the maximum frequency in flux histograms. This unique flux estimation is hence considered as a reasonable approximation for the biological fluxes. Flux maps for different growth phases show no active oxidative part of the pentose phosphate pathway, but NADPH generation in the TCA cycle and NADPH transdehydrogenase activity are most important in fulfilling the NADPH balance. Amino acids contribute to biomass growth by augmenting the pool of available amino acids and by boosting the TCA cycle, particularly

  5. 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

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

    OpenAIRE

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

    2011-01-01

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

  7. 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.

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

    Science.gov (United States)

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

    2016-01-01

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

  9. Diagnostics for stochastic genome-scale modeling via model slicing and debugging.

    Directory of Open Access Journals (Sweden)

    Kevin J Tsai

    Full Text Available Modeling of biological behavior has evolved from simple gene expression plots represented by mathematical equations to genome-scale systems biology networks. However, due to obstacles in complexity and scalability of creating genome-scale models, several biological modelers have turned to programming or scripting languages and away from modeling fundamentals. In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis. We have developed a model diagnostic methodology inspired by program slicing and debugging and demonstrate the effectiveness of the methodology on a genome-scale metabolic network model published in the BioModels database. The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production. We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction. A software application that implements the methodology is available at http://gel.ym.edu.tw/gcs/.

  10. Diagnostics for stochastic genome-scale modeling via model slicing and debugging.

    Science.gov (United States)

    Tsai, Kevin J; Chang, Chuan-Hsiung

    2014-01-01

    Modeling of biological behavior has evolved from simple gene expression plots represented by mathematical equations to genome-scale systems biology networks. However, due to obstacles in complexity and scalability of creating genome-scale models, several biological modelers have turned to programming or scripting languages and away from modeling fundamentals. In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis. We have developed a model diagnostic methodology inspired by program slicing and debugging and demonstrate the effectiveness of the methodology on a genome-scale metabolic network model published in the BioModels database. The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production. We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction. A software application that implements the methodology is available at http://gel.ym.edu.tw/gcs/.

  11. Construction of an E. Coli genome-scale atom mapping model for MFA calculations.

    Science.gov (United States)

    Ravikirthi, Prabhasa; Suthers, Patrick F; Maranas, Costas D

    2011-06-01

    Metabolic flux analysis (MFA) has so far been restricted to lumped networks lacking many important pathways, partly due to the difficulty in automatically generating isotope mapping matrices for genome-scale metabolic networks. Here we introduce a procedure that uses a compound matching algorithm based on the graph theoretical concept of pattern recognition along with relevant reaction information to automatically generate genome-scale atom mappings which trace the path of atoms from reactants to products for every reaction. The procedure is applied to the iAF1260 metabolic reconstruction of Escherichia coli yielding the genome-scale isotope mapping model imPR90068. This model maps 90,068 non-hydrogen atoms that span all 2,077 reactions present in iAF1260 (previous largest mapping model included 238 reactions). The expanded scope of the isotope mapping model allows the complete tracking of labeled atoms through pathways such as cofactor and prosthetic group biosynthesis and histidine metabolism. An EMU representation of imPR90068 is also constructed and made available.

  12. 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.

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

    Directory of Open Access Journals (Sweden)

    Oner Ebru

    2011-01-01

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

  14. Symbolic flux analysis for genome-scale metabolic networks

    Directory of Open Access Journals (Sweden)

    Peterson Pearu

    2011-05-01

    Full Text Available Abstract Background With the advent of genomic technology, the size of metabolic networks that are subject to analysis is growing. A common task when analyzing metabolic networks is to find all possible steady state regimes. There are several technical issues that have to be addressed when analyzing large metabolic networks including accumulation of numerical errors and presentation of the solution to the researcher. One way to resolve those technical issues is to analyze the network using symbolic methods. The aim of this paper is to develop a routine that symbolically finds the steady state solutions of large metabolic networks. Results A symbolic Gauss-Jordan elimination routine was developed for analyzing large metabolic networks. This routine was tested by finding the steady state solutions for a number of curated stoichiometric matrices with the largest having about 4000 reactions. The routine was able to find the solution with a computational time similar to the time used by a numerical singular value decomposition routine. As an advantage of symbolic solution, a set of independent fluxes can be suggested by the researcher leading to the formation of a desired flux basis describing the steady state solution of the network. These independent fluxes can be constrained using experimental data. We demonstrate the application of constraints by calculating a flux distribution for the central metabolic and amino acid biosynthesis pathways of yeast. Conclusions We were able to find symbolic solutions for the steady state flux distribution of large metabolic networks. The ability to choose a flux basis was found to be useful in the constraint process and provides a strong argument for using symbolic Gauss-Jordan elimination in place of singular value decomposition.

  15. Symbolic flux analysis for genome-scale metabolic networks.

    Science.gov (United States)

    Schryer, David W; Vendelin, Marko; Peterson, Pearu

    2011-05-23

    With the advent of genomic technology, the size of metabolic networks that are subject to analysis is growing. A common task when analyzing metabolic networks is to find all possible steady state regimes. There are several technical issues that have to be addressed when analyzing large metabolic networks including accumulation of numerical errors and presentation of the solution to the researcher. One way to resolve those technical issues is to analyze the network using symbolic methods. The aim of this paper is to develop a routine that symbolically finds the steady state solutions of large metabolic networks. A symbolic Gauss-Jordan elimination routine was developed for analyzing large metabolic networks. This routine was tested by finding the steady state solutions for a number of curated stoichiometric matrices with the largest having about 4000 reactions. The routine was able to find the solution with a computational time similar to the time used by a numerical singular value decomposition routine. As an advantage of symbolic solution, a set of independent fluxes can be suggested by the researcher leading to the formation of a desired flux basis describing the steady state solution of the network. These independent fluxes can be constrained using experimental data. We demonstrate the application of constraints by calculating a flux distribution for the central metabolic and amino acid biosynthesis pathways of yeast. We were able to find symbolic solutions for the steady state flux distribution of large metabolic networks. The ability to choose a flux basis was found to be useful in the constraint process and provides a strong argument for using symbolic Gauss-Jordan elimination in place of singular value decomposition.

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

    Science.gov (United States)

    McCloskey, Douglas; Young, Jamey D; Xu, Sibei; Palsson, Bernhard O; Feist, Adam M

    2016-04-05

    Metabolic flux analysis (MFA) is considered to be the gold standard for determining the intracellular flux distribution of biological systems. The majority of work using MFA has been limited to core models of metabolism due to challenges in implementing genome-scale MFA and the undesirable trade-off between increased scope and decreased precision in flux estimations. This work presents a tunable workflow for expanding the scope of MFA to the genome-scale without trade-offs in flux precision. The genome-scale MFA model presented here, iDM2014, accounts for 537 net reactions, which includes the core 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 distributions (MIDs),1 it was found that a total of 232 net fluxes of central and peripheral metabolism could be resolved in the E. coli network. The increase in scope was shown to cover the full biosynthetic route to an expanded set of bioproduction pathways, which should facilitate applications such as the design of more complex bioprocessing strains and aid in identifying new antimicrobials. Importantly, it was found that there was no loss in precision of core fluxes when compared to a traditional core model, and additionally there was an overall increase in precision when considering all observable reactions.

  17. 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

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

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    Panda Gurudutta

    2011-05-01

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

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

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    Thomas Ulas

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

  20. A genome-scale metabolic reconstruction of Mycoplasma genitalium, iPS189.

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    Patrick F Suthers

    2009-02-01

    Full Text Available With a genome size of approximately 580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites, is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms.

  1. A Method to Constrain Genome-Scale Models with 13C Labeling Data.

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    Héctor García Martín

    2015-09-01

    Full Text Available Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA. This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.

  2. Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling

    NARCIS (Netherlands)

    Chaudhary, N.; Tøndel, K.; Bhatnagar, R.; Martins dos Santos, V.A.P.; Puchalka, J.

    2016-01-01

    Genome-Scale Metabolic Reconstructions (GSMRs), along with optimization-based methods, predominantly Flux Balance Analysis (FBA) and its derivatives, are widely applied for assessing and predicting the behavior of metabolic networks upon perturbation, thereby enabling identification of potential nov

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

    DEFF Research Database (Denmark)

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

    2008-01-01

    Background: Aspergillus nidulans is a member of a diverse group of filamentous fungi, sharing many of the properties of its close relatives with significance in the fields of medicine, agriculture and industry. Furthermore, A. nidulans has been a classical model organism for studies of development...... biology and gene regulation, and thus it has become one of the best-characterized filamentous fungi. It was the first Aspergillus species to have its genome sequenced, and automated gene prediction tools predicted 9,451 open reading frames (ORFs) in the genome, of which less than 10% were assigned...

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

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    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.

  5. The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy.

    Science.gov (United States)

    Papin, Jason A; Price, Nathan D; Edwards, Jeremy S; Palsson B, Bernhard Ø

    2002-03-07

    Genome-scale metabolic networks can be characterized by a set of systemically independent and unique extreme pathways. These extreme pathways span a convex, high-dimensional space that circumscribes all potential steady-state flux distributions achievable by the defined metabolic network. Genome-scale extreme pathways associated with the production of non-essential amino acids in Haemophilus influenzae were computed. They offer valuable insight into the functioning of its metabolic network. Three key results were obtained. First, there were multiple internal flux maps corresponding to externally indistinguishable states. It was shown that there was an average of 37 internal states per unique exchange flux vector in H. influenzae when the network was used to produce a single amino acid while allowing carbon dioxide and acetate as carbon sinks. With the inclusion of succinate as an additional output, this ratio increased to 52, a 40% increase. Second, an analysis of the carbon fates illustrated that the extreme pathways were non-uniformly distributed across the carbon fate spectrum. In the detailed case study, 45% of the distinct carbon fate values associated with lysine production represented 85% of the extreme pathways. Third, this distribution fell between distinct systemic constraints. For lysine production, the carbon fate values that represented 85% of the pathways described above corresponded to only 2 distinct ratios of 1:1 and 4:1 between carbon dioxide and acetate. The present study analysed single outputs from one organism, and provides a start to genome-scale extreme pathways studies. These emergent system-level characterizations show the significance of metabolic extreme pathway analysis at the genome-scale.

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

    Science.gov (United States)

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

    2017-08-30

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

  7. 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.

  8. Metabolic stasis in an ancient symbiosis: genome-scale metabolic networks from two Blattabacterium cuenoti strains, primary endosymbionts of cockroaches.

    Science.gov (United States)

    González-Domenech, Carmen Maria; Belda, Eugeni; Patiño-Navarrete, Rafael; Moya, Andrés; Peretó, Juli; Latorre, Amparo

    2012-01-18

    Cockroaches are terrestrial insects that strikingly eliminate waste nitrogen as ammonia instead of uric acid. Blattabacterium cuenoti (Mercier 1906) strains Bge and Pam are the obligate primary endosymbionts of the cockroaches Blattella germanica and Periplaneta americana, respectively. The genomes of both bacterial endosymbionts have recently been sequenced, making possible a genome-scale constraint-based reconstruction of their metabolic networks. The mathematical expression of a metabolic network and the subsequent quantitative studies of phenotypic features by Flux Balance Analysis (FBA) represent an efficient functional approach to these uncultivable bacteria. We report the metabolic models of Blattabacterium strains Bge (iCG238) and Pam (iCG230), comprising 296 and 289 biochemical reactions, associated with 238 and 230 genes, and 364 and 358 metabolites, respectively. Both models reflect both the striking similarities and the singularities of these microorganisms. FBA was used to analyze the properties, potential and limits of the models, assuming some environmental constraints such as aerobic conditions and the net production of ammonia from these bacterial systems, as has been experimentally observed. In addition, in silico simulations with the iCG238 model have enabled a set of carbon and nitrogen sources to be defined, which would also support a viable phenotype in terms of biomass production in the strain Pam, which lacks the first three steps of the tricarboxylic acid cycle. FBA reveals a metabolic condition that renders these enzymatic steps dispensable, thus offering a possible evolutionary explanation for their elimination. We also confirm, by computational simulations, the fragility of the metabolic networks and their host dependence. The minimized Blattabacterium metabolic networks are surprisingly similar in strains Bge and Pam, after 140 million years of evolution of these endosymbionts in separate cockroach lineages. FBA performed on the

  9. A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory

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    Thiele Ines

    2008-09-01

    Full Text Available Abstract Background Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions. Results We present a genome-scale reconstruction of P. putida KT2440's metabolism, iJN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. iJN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds (e.g., toluene, benzoate, phenylacetate, nicotinate, not described in other metabolic reconstructions or biochemical databases. The predictive potential of iJN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected. Conclusion Here we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of iJN746 as i a knowledge-base, ii a discovery tool, and iii an engineering platform to explore P

  10. 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

    , prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and "hedging" against uncertain environments and stresses, as indicated by significant enrichment...... 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......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...

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

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    Hong Yang

    2014-09-01

    Full Text Available Transposon mutagenesis, in combination with parallel sequencing, is becoming a powerful tool for en-masse mutant analysis. A probability generating function was used to explain observed miniHimar transposon insertion patterns, and gene essentiality calls were made by transposon insertion frequency analysis (TIFA. TIFA incorporated the observed genome and sequence motif bias of the miniHimar transposon. The gene essentiality calls were compared to: 1 previous genome-wide direct gene-essentiality assignments; and, 2 flux balance analysis (FBA predictions from an existing genome-scale metabolic model of Shewanella oneidensis MR-1. A three-way comparison between FBA, TIFA, and the direct essentiality calls was made to validate the TIFA approach. The refinement in the interpretation of observed transposon insertions demonstrated that genes without insertions are not necessarily essential, and that genes that contain insertions are not always nonessential. The TIFA calls were in reasonable agreement with direct essentiality calls for S. oneidensis, but agreed more closely with E. coli essentiality calls for orthologs. The TIFA gene essentiality calls were in good agreement with the MR-1 FBA essentiality predictions, and the agreement between TIFA and FBA predictions was substantially better than between the FBA and the direct gene essentiality predictions.

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

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    Feist Adam M

    2011-10-01

    Full Text Available Abstract Background Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown utility in the study of systems medicine. However, no integrated analysis between human tissues has been done. Results To describe tissue-specific functions, Recon 1 was tailored to describe metabolism in three human cells: adipocytes, hepatocytes, and myocytes. These cell-specific networks were manually curated and validated based on known cellular metabolic functions. To study intercellular interactions, a novel multi-tissue type modeling approach was developed to integrate the metabolic functions for the three cell types, and subsequently used to simulate known integrated metabolic cycles. In addition, the multi-tissue model was used to study diabetes: a pathology with systemic properties. High-throughput data was integrated with the network to determine differential metabolic activity between obese and type II obese gastric bypass patients in a whole-body context. Conclusion The multi-tissue type modeling approach presented provides a platform to study integrated metabolic states. As more cell and tissue-specific models are released, it is critical to develop a framework in which to study their interdependencies.

  13. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments.

    Science.gov (United States)

    Zhuang, Kai; Izallalen, Mounir; Mouser, Paula; Richter, Hanno; Risso, Carla; Mahadevan, Radhakrishnan; Lovley, Derek R

    2011-02-01

    The advent of rapid complete genome sequencing, and the potential to capture this information in genome-scale metabolic models, provide the possibility of comprehensively modeling microbial community interactions. For example, Rhodoferax and Geobacter species are acetate-oxidizing Fe(III)-reducers that compete in anoxic subsurface environments and this competition may have an influence on the in situ bioremediation of uranium-contaminated groundwater. Therefore, genome-scale models of Geobacter sulfurreducens and Rhodoferax ferrireducens were used to evaluate how Geobacter and Rhodoferax species might compete under diverse conditions found in a uranium-contaminated aquifer in Rifle, CO. The model predicted that at the low rates of acetate flux expected under natural conditions at the site, Rhodoferax will outcompete Geobacter as long as sufficient ammonium is available. The model also predicted that when high concentrations of acetate are added during in situ bioremediation, Geobacter species would predominate, consistent with field-scale observations. This can be attributed to the higher expected growth yields of Rhodoferax and the ability of Geobacter to fix nitrogen. The modeling predicted relative proportions of Geobacter and Rhodoferax in geochemically distinct zones of the Rifle site that were comparable to those that were previously documented with molecular techniques. The model also predicted that under nitrogen fixation, higher carbon and electron fluxes would be diverted toward respiration rather than biomass formation in Geobacter, providing a potential explanation for enhanced in situ U(VI) reduction in low-ammonium zones. These results show that genome-scale modeling can be a useful tool for predicting microbial interactions in subsurface environments and shows promise for designing bioremediation strategies.

  14. 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 currently used for metabolic engineering and elucidating biological interactions. Here we review the history of yeast's GEMs, focusing on recent developments. We study how these models are typically evaluated, using both descriptive and predictive metrics. Additionally, we analyze the different ways...... 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....

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

    Science.gov (United States)

    Feizi, Amir; Österlund, Tobias; Petranovic, Dina; Bordel, Sergio; Nielsen, Jens

    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 was developed which mimics secretory machinery and assigns each secretory protein to a particular secretory class that determines the set of PTMs and transport steps specific to each protein. Protein abundances were integrated with the model in order to gain system level estimation of the metabolic demands associated with the processing of each specific protein as well as a quantitative estimation of the activity of each component of the secretory machinery.

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

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    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.

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

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    Jacek Puchałka

    2008-10-01

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

  18. Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology.

    Science.gov (United States)

    Milne, Caroline B; Kim, Pan-Jun; Eddy, James A; Price, Nathan D

    2009-12-01

    Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a trans-formative tool in biotechnology.

  19. Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement

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    Chung Bevan KS

    2010-07-01

    Full Text Available Abstract Background Pichia pastoris has been recognized as an effective host for recombinant protein production. A number of studies have been reported for improving this expression system. However, its physiology and cellular metabolism still remained largely uncharacterized. Thus, it is highly desirable to establish a systems biotechnological framework, in which a comprehensive in silico model of P. pastoris can be employed together with high throughput experimental data analysis, for better understanding of the methylotrophic yeast's metabolism. Results A fully compartmentalized metabolic model of P. pastoris (iPP668, composed of 1,361 reactions and 1,177 metabolites, was reconstructed based on its genome annotation and biochemical information. The constraints-based flux analysis was then used to predict achievable growth rate which is consistent with the cellular phenotype of P. pastoris observed during chemostat experiments. Subsequent in silico analysis further explored the effect of various carbon sources on cell growth, revealing sorbitol as a promising candidate for culturing recombinant P. pastoris strains producing heterologous proteins. Interestingly, methanol consumption yields a high regeneration rate of reducing equivalents which is substantial for the synthesis of valuable pharmaceutical precursors. Hence, as a case study, we examined the applicability of P. pastoris system to whole-cell biotransformation and also identified relevant metabolic engineering targets that have been experimentally verified. Conclusion The genome-scale metabolic model characterizes the cellular physiology of P. pastoris, thus allowing us to gain valuable insights into the metabolism of methylotrophic yeast and devise possible strategies for strain improvement through in silico simulations. This computational approach, combined with synthetic biology techniques, potentially forms a basis for rational analysis and design of P. pastoris metabolic network

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

    Directory of Open Access Journals (Sweden)

    Agosin Eduardo

    2011-05-01

    Full Text Available Abstract 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.

  1. A Genome-Scale Modeling Approach to Quantify Biofilm Component Growth of Salmonella Typhimurium.

    Science.gov (United States)

    Ribaudo, Nicholas; Li, Xianhua; Davis, Brett; Wood, Thomas K; Huang, Zuyi Jacky

    2017-01-01

    Salmonella typhimurium (S. typhimurium) is an extremely dangerous foodborne bacterium that infects both animal and human subjects, causing fatal diseases around the world. Salmonella's robust virulence, antibiotic-resistant nature, and capacity to survive under harsh conditions are largely due to its ability to form resilient biofilms. Multiple genome-scale metabolic models have been developed to study the complex and diverse nature of this organism's metabolism; however, none of these models fully integrated the reactions and mechanisms required to study the influence of biofilm formation. This work developed a systems-level approach to study the adjustment of intracellular metabolism of S. typhimurium during biofilm formation. The most advanced metabolic reconstruction currently available, STM_v1.0, was 1st extended to include the formation of the extracellular biofilm matrix. Flux balance analysis was then employed to study the influence of biofilm formation on cellular growth rate and the production rates of biofilm components. With biofilm formation present, biomass growth was examined under nutrient rich and nutrient deficient conditions, resulting in overall growth rates of 0.8675 and 0.6238 h(-1) respectively. Investigation of intracellular flux variation during biofilm formation resulted in the elucidation of 32 crucial reactions, and associated genes, whose fluxes most significantly adapt during the physiological response. Experimental data were found in the literature to validate the importance of these genes for the biofilm formation of S. typhimurium. This preliminary investigation on the adjustment of intracellular metabolism of S. typhimurium during biofilm formation will serve as a platform to generate hypotheses for further experimental study on the biofilm formation of this virulent bacterium.

  2. Genome-scale NAD(H/(+ availability patterns as a differentiating feature between Saccharomyces cerevisiae and Scheffersomyces stipitis in relation to fermentative metabolism.

    Directory of Open Access Journals (Sweden)

    Alejandro Acevedo

    Full Text Available Scheffersomyces stipitis is a yeast able to ferment pentoses to ethanol, unlike Saccharomyces cerevisiae, it does not present the so-called overflow phenomenon. Metabolic features characterizing the presence or not of this phenomenon have not been fully elucidated. This work proposes that genome-scale metabolic response to variations in NAD(H/(+ availability characterizes fermentative behavior in both yeasts. Thus, differentiating features in S. stipitis and S. cerevisiae were determined analyzing growth sensitivity response to changes in available reducing capacity in relation to ethanol production capacity and overall metabolic flux span. Using genome-scale constraint-based metabolic models, phenotypic phase planes and shadow price analyses, an excess of available reducing capacity for growth was found in S. cerevisiae at every metabolic phenotype where growth is limited by oxygen uptake, while in S. stipitis this was observed only for a subset of those phenotypes. Moreover, by using flux variability analysis, an increased metabolic flux span was found in S. cerevisiae at growth limited by oxygen uptake, while in S. stipitis flux span was invariant. Therefore, each yeast can be characterized by a significantly different metabolic response and flux span when growth is limited by oxygen uptake, both features suggesting a higher metabolic flexibility in S. cerevisiae. By applying an optimization-based approach on the genome-scale models, three single reaction deletions were found to generate in S. stipitis the reducing capacity availability pattern found in S. cerevisiae, two of them correspond to reactions involved in the overflow phenomenon. These results show a close relationship between the growth sensitivity response given by the metabolic network and fermentative behavior.

  3. Genome-Scale NAD(H/+) Availability Patterns as a Differentiating Feature between Saccharomyces cerevisiae and Scheffersomyces stipitis in Relation to Fermentative Metabolism

    Science.gov (United States)

    Acevedo, Alejandro; Aroca, German; Conejeros, Raul

    2014-01-01

    Scheffersomyces stipitis is a yeast able to ferment pentoses to ethanol, unlike Saccharomyces cerevisiae, it does not present the so-called overflow phenomenon. Metabolic features characterizing the presence or not of this phenomenon have not been fully elucidated. This work proposes that genome-scale metabolic response to variations in NAD(H/+) availability characterizes fermentative behavior in both yeasts. Thus, differentiating features in S. stipitis and S. cerevisiae were determined analyzing growth sensitivity response to changes in available reducing capacity in relation to ethanol production capacity and overall metabolic flux span. Using genome-scale constraint-based metabolic models, phenotypic phase planes and shadow price analyses, an excess of available reducing capacity for growth was found in S. cerevisiae at every metabolic phenotype where growth is limited by oxygen uptake, while in S. stipitis this was observed only for a subset of those phenotypes. Moreover, by using flux variability analysis, an increased metabolic flux span was found in S. cerevisiae at growth limited by oxygen uptake, while in S. stipitis flux span was invariant. Therefore, each yeast can be characterized by a significantly different metabolic response and flux span when growth is limited by oxygen uptake, both features suggesting a higher metabolic flexibility in S. cerevisiae. By applying an optimization-based approach on the genome-scale models, three single reaction deletions were found to generate in S. stipitis the reducing capacity availability pattern found in S. cerevisiae, two of them correspond to reactions involved in the overflow phenomenon. These results show a close relationship between the growth sensitivity response given by the metabolic network and fermentative behavior. PMID:24489927

  4. 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.

  5. 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.

  6. Candidate states of Helicobacter pylori's genome-scale metabolic network upon application of "loop law" thermodynamic constraints.

    Science.gov (United States)

    Price, Nathan D; Thiele, Ines; Palsson, Bernhard Ø

    2006-06-01

    Constraint-based modeling has proven to be a useful tool in the analysis of biochemical networks. To date, most studies in this field have focused on the use of linear constraints, resulting from mass balance and capacity constraints, which lead to the definition of convex solution spaces. One additional constraint arising out of thermodynamics is known as the "loop law" for reaction fluxes, which states that the net flux around a closed biochemical loop must be zero because no net thermodynamic driving force exists. The imposition of the loop-law can lead to nonconvex solution spaces making the analysis of the consequences of its imposition challenging. A four-step approach is developed here to apply the loop-law to study metabolic network properties: 1), determine linear equality constraints that are necessary (but not necessarily sufficient) for thermodynamic feasibility; 2), tighten V(max) and V(min) constraints to enclose the remaining nonconvex space; 3), uniformly sample the convex space that encloses the nonconvex space using standard Monte Carlo techniques; and 4), eliminate from the resulting set all solutions that violate the loop-law, leaving a subset of steady-state solutions. This subset of solutions represents a uniform random sample of the space that is defined by the additional imposition of the loop-law. This approach is used to evaluate the effect of imposing the loop-law on predicted candidate states of the genome-scale metabolic network of Helicobacter pylori.

  7. Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials

    Directory of Open Access Journals (Sweden)

    Caspeta Luis

    2012-04-01

    Full Text Available Abstract Background Pichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research. Results In this work, genome-scale metabolic models (GEMs of P. stipitis (iSS884 and P. pastoris (iLC915 were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed. Conclusions In conclusion the first GEM of P. stipitis (iSS884 was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials.

  8. Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways.

    Science.gov (United States)

    Pey, Jon; Valgepea, Kaspar; Rubio, Angel; Beasley, John E; Planes, Francisco J

    2013-12-08

    The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.

  9. 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

  10. 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.

  11. Counting and Correcting Thermodynamically Infeasible Flux Cycles in Genome-Scale Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Andrea De Martino

    2013-10-01

    Full Text Available Thermodynamics constrains the flow of matter in a reaction network to occur through routes along which the Gibbs energy decreases, implying that viable steady-state flux patterns should be void of closed reaction cycles. Identifying and removing cycles in large reaction networks can unfortunately be a highly challenging task from a computational viewpoint. We propose here a method that accomplishes it by combining a relaxation algorithm and a Monte Carlo procedure to detect loops, with ad hoc rules (discussed in detail to eliminate them. As test cases, we tackle (a the problem of identifying infeasible cycles in the E. coli metabolic network and (b the problem of correcting thermodynamic infeasibilities in the Flux-Balance-Analysis solutions for 15 human cell-type-specific metabolic networks. Results for (a are compared with previous analyses of the same issue, while results for (b are weighed against alternative methods to retrieve thermodynamically viable flux patterns based on minimizing specific global quantities. Our method, on the one hand, outperforms previous techniques and, on the other, corrects loopy solutions to Flux Balance Analysis. As a byproduct, it also turns out to be able to reveal possible inconsistencies in model reconstructions.

  12. Genome-scale modeling of light-driven reductant partitioning and carbon fluxes in diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142.

    Directory of Open Access Journals (Sweden)

    Trang T Vu

    Full Text Available Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When growth is limited by the flux through photosystem I, terminal respiratory oxidases are predicted to be an important mechanism for removing excess reductant. Similarly, under photosystem II flux limitation, excess electron carriers must be removed via cyclic electron transport. Furthermore, in silico calculations were in good quantitative agreement with the measured growth rates whereas predictions of reaction usage were qualitatively consistent with protein and mRNA expression data, which we used to further improve the resolution of intracellular flux values.

  13. Genome-scale modeling of light-driven reductant partitioning and carbon fluxes in diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142.

    Science.gov (United States)

    Vu, Trang T; Stolyar, Sergey M; Pinchuk, Grigoriy E; Hill, Eric A; Kucek, Leo A; Brown, Roslyn N; Lipton, Mary S; Osterman, Andrei; Fredrickson, Jim K; Konopka, Allan E; Beliaev, Alexander S; Reed, Jennifer L

    2012-01-01

    Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When growth is limited by the flux through photosystem I, terminal respiratory oxidases are predicted to be an important mechanism for removing excess reductant. Similarly, under photosystem II flux limitation, excess electron carriers must be removed via cyclic electron transport. Furthermore, in silico calculations were in good quantitative agreement with the measured growth rates whereas predictions of reaction usage were qualitatively consistent with protein and mRNA expression data, which we used to further improve the resolution of intracellular flux values.

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

    DEFF Research Database (Denmark)

    Federowicz, Stephen; Kim, Donghyuk; Ebrahim, Ali

    2014-01-01

    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 r......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...

  15. ToMI-FBA: A genome-scale metabolic flux based algorithm to select optimum hosts and media formulations for expressing pathways of interest

    Directory of Open Access Journals (Sweden)

    Hadi Nazem-Bokaee

    2015-09-01

    Full Text Available The Total Membrane Influx constrained Flux Balance Analysis (ToMI-FBA algorithm was developed in this research as a new tool to help researchers decide which microbial host and medium formulation are optimal for expressing a new metabolic pathway. ToMI-FBA relies on genome-scale metabolic flux modeling and a novel in silico cell membrane influx constraint that specifies the flux of atoms (not molecules into the cell through all possible membrane transporters. The ToMI constraint is constructed through the addition of an extra row and column to the stoichiometric matrix of a genome-scale metabolic flux model. In this research, the mathematical formulation of the ToMI constraint is given along with four case studies that demonstrate its usefulness. In Case Study 1, ToMI-FBA returned an optimal culture medium formulation for the production of isobutanol from Bacillus subtilis. Significant levels of L-valine were recommended to optimize production, and this result has been observed experimentally. In Case Study 2, it is demonstrated how the carbon to nitrogen uptake ratio can be specified as an additional ToMI-FBA constraint. This was investigated for maximizing medium chain length polyhydroxyalkanoates (mcl-PHA production from Pseudomonas putida KT2440. In Case Study 3, ToMI-FBA revealed a strategy of adding cellobiose as a means to increase ethanol selectivity during the stationary growth phase of Clostridium acetobutylicum ATCC 824. This strategy was also validated experimentally. Finally, in Case Study 4, B. subtilis was identified as a superior host to Escherichia coli, Saccharomyces cerevisiae, and Synechocystis PCC6803 for the production of artemisinate.

  16. 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.

  17. Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling.

    Science.gov (United States)

    Chaudhary, Neha; Tøndel, Kristin; Bhatnagar, Rakesh; dos Santos, Vítor A P Martins; Puchałka, Jacek

    2016-03-01

    Genome-Scale Metabolic Reconstructions (GSMRs), along with optimization-based methods, predominantly Flux Balance Analysis (FBA) and its derivatives, are widely applied for assessing and predicting the behavior of metabolic networks upon perturbation, thereby enabling identification of potential novel drug targets and biotechnologically relevant pathways. The abundance of alternate flux profiles has led to the evolution of methods to explore the complete solution space aiming to increase the accuracy of predictions. Herein we present a novel, generic algorithm to characterize the entire flux space of GSMR upon application of FBA, leading to the optimal value of the objective (the optimal flux space). Our method employs Modified Latin-Hypercube Sampling (LHS) to effectively border the optimal space, followed by Principal Component Analysis (PCA) to identify and explain the major sources of variability within it. The approach was validated with the elementary mode analysis of a smaller network of Saccharomyces cerevisiae and applied to the GSMR of Pseudomonas aeruginosa PAO1 (iMO1086). It is shown to surpass the commonly used Monte Carlo Sampling (MCS) in providing a more uniform coverage for a much larger network in less number of samples. Results show that although many fluxes are identified as variable upon fixing the objective value, majority of the variability can be reduced to several main patterns arising from a few alternative pathways. In iMO1086, initial variability of 211 reactions could almost entirely be explained by 7 alternative pathway groups. These findings imply that the possibilities to reroute greater portions of flux may be limited within metabolic networks of bacteria. Furthermore, the optimal flux space is subject to change with environmental conditions. Our method may be a useful device to validate the predictions made by FBA-based tools, by describing the optimal flux space associated with these predictions, thus to improve them.

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

    Science.gov (United States)

    Racle, Julien; Stefaniuk, Adam Jan; Hatzimanikatis, Vassily

    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.

  19. 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.

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

    National Research Council Canada - National Science Library

    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

    2015-01-01

    .... 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...

  1. 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

  2. Integrated genome-scale analysis of the transcriptional regulatory landscape in a blood stem/progenitor cell model.

    Science.gov (United States)

    Wilson, Nicola K; Schoenfelder, Stefan; Hannah, Rebecca; Sánchez Castillo, Manuel; Schütte, Judith; Ladopoulos, Vasileios; Mitchelmore, Joanna; Goode, Debbie K; Calero-Nieto, Fernando J; Moignard, Victoria; Wilkinson, Adam C; Jimenez-Madrid, Isabel; Kinston, Sarah; Spivakov, Mikhail; Fraser, Peter; Göttgens, Berthold

    2016-03-31

    Comprehensive study of transcriptional control processes will be required to enhance our understanding of both normal and malignant hematopoiesis. Modern sequencing technologies have revolutionized our ability to generate genome-scale expression and histone modification profiles, transcription factor (TF)-binding maps, and also comprehensive chromatin-looping information. Many of these technologies, however, require large numbers of cells, and therefore cannot be applied to rare hematopoietic stem/progenitor cell (HSPC) populations. The stem cell factor-dependent multipotent progenitor cell line HPC-7 represents a well-recognized cell line model for HSPCs. Here we report genome-wide maps for 17 TFs, 3 histone modifications, DNase I hypersensitive sites, and high-resolution promoter-enhancer interactomes in HPC-7 cells. Integrated analysis of these complementary data sets revealed TF occupancy patterns of genomic regions involved in promoter-anchored loops. Moreover, preferential associations between pairs of TFs bound at either ends of chromatin loops led to the identification of 4 previously unrecognized protein-protein interactions between key blood stem cell regulators. All HPC-7 data sets are freely available both through standard repositories and a user-friendly Web interface. Together with previously generated genome-wide data sets, this study integrates HPC-7 data into a genomic resource on par with ENCODE tier 1 cell lines and, importantly, is the only current model with comprehensive genome-scale data that is relevant to HSPC biology. © 2016 by The American Society of Hematology.

  3. TRFBA: an algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data.

    Science.gov (United States)

    Motamedian, Ehsan; Mohammadi, Maryam; Shojaosadati, Seyed Abbas; Heydari, Mona

    2017-04-01

    Integration of different biological networks and data-types has been a major challenge in systems biology. The present study introduces the transcriptional regulated flux balance analysis (TRFBA) algorithm that integrates transcriptional regulatory and metabolic models using a set of expression data for various perturbations. TRFBA considers the expression levels of genes as a new continuous variable and introduces two new linear constraints. The first constraint limits the rate of reaction(s) supported by a metabolic gene using a constant parameter (C) that converts the expression levels to the upper bounds of the reactions. Considering the concept of constraint-based modeling, the second set of constraints correlates the expression level of each target gene with that of its regulating genes. A set of constraints and binary variables was also added to prevent the second set of constraints from overlapping. TRFBA was implemented on Escherichia coli and Saccharomyces cerevisiae models to estimate growth rates under various environmental and genetic perturbations. The error sensitivity to the algorithm parameter was evaluated to find the best value of C. The results indicate a significant improvement in the quantitative prediction of growth in comparison with previously presented algorithms. The robustness of the algorithm to change in the expression data and the regulatory network was tested to evaluate the effect of noisy and incomplete data. Furthermore, the use of added constraints for perturbations without their gene expression profile demonstrates that these constraints can be applied to improve the growth prediction of FBA. TRFBA is implemented in Matlab software and requires COBRA toolbox. Source code is freely available at http://sbme.modares.ac.ir . : motamedian@modares.ac.ir. Supplementary data are available at Bioinformatics online.

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

    Science.gov (United States)

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

  5. 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

  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. A Comprehensively Curated Genome-Scale Two-Cell Model for the Heterocystous Cyanobacterium Anabaena sp. PCC 7120.

    Science.gov (United States)

    Malatinszky, David; Steuer, Ralf; Jones, Patrik R

    2017-01-01

    Anabaena sp. PCC 7120 is a nitrogen-fixing filamentous cyanobacterium. Under nitrogen-limiting conditions, a fraction of the vegetative cells in each filament terminally differentiate to nongrowing heterocysts. Heterocysts are metabolically and structurally specialized to enable O2-sensitive nitrogen fixation. The functionality of the filament, as an association of vegetative cells and heterocysts, is postulated to depend on metabolic exchange of electrons, carbon, and fixed nitrogen. In this study, we compile and evaluate a comprehensive curated stoichiometric model of this two-cell system, with the objective function based on the growth of the filament under diazotrophic conditions. The predicted growth rate under nitrogen-replete and -deplete conditions, as well as the effect of external carbon and nitrogen sources, was thereafter verified. Furthermore, the model was utilized to comprehensively evaluate the optimality of putative metabolic exchange reactions between heterocysts and vegetative cells. The model suggested that optimal growth requires at least four exchange metabolites. Several combinations of exchange metabolites resulted in predicted growth rates that are higher than growth rates achieved by only considering exchange of metabolites previously suggested in the literature. The curated model of the metabolic network of Anabaena sp. PCC 7120 enhances our ability to understand the metabolic organization of multicellular cyanobacteria and provides a platform for further study and engineering of their metabolism.

  8. 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.

  9. MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks.

    Science.gov (United States)

    Vitkin, Edward; Shlomi, Tomer

    2012-11-29

    Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for constraint-based modeling analysis and specifically for metabolic engineering. The reconstructed network models are supplied via standard SBML files.

  10. Systematic assignment of thermodynamic constraints in metabolic network models

    NARCIS (Netherlands)

    Kümmel, Anne; Panke, Sven; Heinemann, Matthias

    2006-01-01

    Background: The availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that par

  11. 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

    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...

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

    Science.gov (United States)

    2016-03-15

    one antimicrobial or have human homologs (see Materials and Methods). This resulted in a set of 56 putative target reactions against P. aer- uginosa...diphosphate; amp, adenylate. See S1 Supporting Information for the definition of the remaining of the abbreviations. doi:10.1371/journal.pcbi.1004452.g001...reducing or biofilm-increasing reactions, respectively). Then, for the inhibition of each reaction not used in the definition of any one of the metabo

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

    DEFF Research Database (Denmark)

    Brochado, Ana Rita

    . 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....

  14. ScrumPy: metabolic modelling with Python.

    Science.gov (United States)

    Poolman, M G

    2006-09-01

    ScrumPy is a software package used for the definition and analysis of metabolic models. It is written using the Python programming language that is also used as a user interface. ScrumPy has features for both kinetic and structural modelling, but the emphasis is on structural modelling and those features of most relevance to analysis of large (genome-scale) models. The aim is at describing ScrumPy's functionality to readers with some knowledge of metabolic modelling, but implementation, programming and other computational details are omitted. ScrumPy is released under the Gnu Public Licence, and available for download from http://mudshark.brookes.ac.uk/ ScrumPy.

  15. 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.

  16. Analysis of Sensitive CO2 Pathways and Genes Related to Carbon Uptake and Accumulation in Chlamydomonas reinhardtii through Genomic Scale Modeling and Experimental Validation

    Science.gov (United States)

    Winck, Flavia V.; Melo, David O. Páez; Riaño-Pachón, Diego M.; Martins, Marina C. M.; Caldana, Camila; Barrios, Andrés F. González

    2016-01-01

    The development of microalgae sustainable applications needs better understanding of microalgae biology. Moreover, how cells coordinate their metabolism toward biomass accumulation is not fully understood. In this present study, flux balance analysis (FBA) was performed to identify sensitive metabolic pathways of Chlamydomonas reinhardtii under varied CO2 inputs. The metabolic network model of Chlamydomonas was updated based on the genome annotation data and sensitivity analysis revealed CO2 sensitive reactions. Biological experiments were performed with cells cultivated at 0.04% (air), 2.5, 5, 8, and 10% CO2 concentration under controlled conditions and cell growth profiles and biomass content were measured. Pigments, lipids, proteins, and starch were further quantified for the reference low (0.04%) and high (10%) CO2 conditions. The expression level of candidate genes of sensitive reactions was measured and validated by quantitative real time PCR. The sensitive analysis revealed mitochondrial compartment as the major affected by changes on the CO2 concentrations and glycolysis/gluconeogenesis, glyoxylate, and dicarboxylate metabolism among the affected metabolic pathways. Genes coding for glycerate kinase (GLYK), glycine cleavage system, H-protein (GCSH), NAD-dependent malate dehydrogenase (MDH3), low-CO2 inducible protein A (LCIA), carbonic anhydrase 5 (CAH5), E1 component, alpha subunit (PDC3), dual function alcohol dehydrogenase/acetaldehyde dehydrogenase (ADH1), and phosphoglucomutase (GPM2), were defined, among other genes, as sensitive nodes in the metabolic network simulations. These genes were experimentally responsive to the changes in the carbon fluxes in the system. We performed metabolomics analysis using mass spectrometry validating the modulation of carbon dioxide responsive pathways and metabolites. The changes on CO2 levels mostly affected the metabolism of amino acids found in the photorespiration pathway. Our updated metabolic network was

  17. Analysis of sensitive CO2 pathways and genes related to carbon uptake and accumulation in Chlamydomonas reinhardtii through genomic scale modeling and experimental validation

    Directory of Open Access Journals (Sweden)

    Flavia Vischi Winck

    2016-02-01

    Full Text Available The development of microalgae sustainable applications needs better understanding of microalgae biology. Moreover, how cells coordinate their metabolism towards biomass accumulation is not fully understood. In this present study, flux balance analysis (FBA was performed to identify sensitive metabolic pathways of Chlamydomonas reinhardtii under varied CO2 inputs. The metabolic network model of Chlamydomonas was updated based on the genome annotation data and sensitivity analysis revealed CO2 sensitive reactions. Biological experiments were performed with cells cultivated at 0.04% (air, 2.5%, 5%, 8% and 10% CO2 concentration under controlled conditions and cell growth profiles and biomass content were measured. Pigments, lipids, proteins and starch were further quantified for the reference low (0.04% and high (10% CO2 conditions. The expression level of candidate genes of sensitive reactions was measured and validated by quantitative real time qPCR. The sensitive analysis revealed mitochondrial compartment as the major affected by high CO2 levels and glycolysis/gluconeogenesis, glyoxylate and dicarboxylate metabolism among the affected metabolic pathways. Genes coding for glycerate kinase (GLYK, glycine cleavage system, H-protein (GCSH, NAD-dependent malate dehydrogenase (MDH3, low-CO2 inducible protein A (LCIA, carbonic anhydrase 5 (CAH5, E1 component, alpha subunit (PDC3, dual function alcohol dehydrogenase/acetaldehyde dehydrogenase (ADH1 and phosphoglucomutase (GPM2, were defined, among other genes, as sensitive nodes in the metabolic network simulations. These genes were experimentally responsive to the changes in the carbon fluxes in the system. We performed metabolomics analysis using mass spectrometry validating the modulation of carbon dioxide responsive pathways and metabolites. The changes on CO2 levels mostly affected the metabolism of amino acids found in the photorespiration pathway. Our updated metabolic network was compared to

  18. Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation

    DEFF Research Database (Denmark)

    Hartman, Hassan B.; Fell, David A.; Rossell, Sergio;

    2014-01-01

    . Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal...

  19. Systematic assignment of thermodynamic constraints in metabolic network models

    Directory of Open Access Journals (Sweden)

    Heinemann Matthias

    2006-11-01

    Full Text Available Abstract Background The availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models. Results We present an algorithm that – based on thermodynamics, network topology and heuristic rules – automatically assigns reaction directions in metabolic models such that the reaction network is thermodynamically feasible with respect to the production of energy equivalents. It first exploits all available experimentally derived Gibbs energies of formation to identify irreversible reactions. As these thermodynamic data are not available for all metabolites, in a next step, further reaction directions are assigned on the basis of network topology considerations and thermodynamics-based heuristic rules. Briefly, the algorithm identifies reaction subsets from the metabolic network that are able to convert low-energy co-substrates into their high-energy counterparts and thus net produce energy. Our algorithm aims at disabling such thermodynamically infeasible cyclic operation of reaction subnetworks by assigning reaction directions based on a set of thermodynamics-derived heuristic rules. We demonstrate our algorithm on a genome-scale metabolic model of E. coli. The introduced systematic direction assignment yielded 130 irreversible reactions (out of 920 total reactions, which corresponds to about 70% of all irreversible

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

    Science.gov (United States)

    Birkel, Garrett W; Ghosh, Amit; Kumar, Vinay S; Weaver, Daniel; Ando, David; Backman, Tyler W H; Arkin, Adam P; Keasling, Jay D; Martín, Héctor García

    2017-04-05

    Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux 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. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and (13)C Metabolic Flux Analysis. Moreover, it introduces the capability to use (13)C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale (13)C Metabolic Flux Analysis (2S-(13)C 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. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it

  1. 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.

  2. New paradigms for metabolic modeling of human cells

    DEFF Research Database (Denmark)

    Mardinoglu, Adil; Nielsen, Jens

    2015-01-01

    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......, 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....... 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...

  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. Mathematical modelling of metabolism

    DEFF Research Database (Denmark)

    Gombert, Andreas Karoly; Nielsen, Jens

    2000-01-01

    Mathematical models of the cellular metabolism have a special interest within biotechnology. Many different kinds of commercially important products are derived from the cell factory, and metabolic engineering can be applied to improve existing production processes, as well as to make new processes...... available. Both stoichiometric and kinetic models have been used to investigate the metabolism, which has resulted in defining the optimal fermentation conditions, as well as in directing the genetic changes to be introduced in order to obtain a good producer strain or cell line. With the increasing...... availability of genomic information and powerful analytical techniques, mathematical models also serve as a tool for understanding the cellular metabolism and physiology....

  5. Applications of computational modeling in metabolic engineering of yeast

    DEFF Research Database (Denmark)

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

    2015-01-01

    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...

  6. 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.

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

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    Amphun eChaiboonchoe

    2014-12-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  9. Characterizing the metabolism of Dehalococcoides with a constraint-based model.

    Directory of Open Access Journals (Sweden)

    M Ahsanul Islam

    Full Text Available Dehalococcoides strains respire a wide variety of chloro-organic compounds and are important for the bioremediation of toxic, persistent, carcinogenic, and ubiquitous ground water pollutants. In order to better understand metabolism and optimize their application, we have developed a pan-genome-scale metabolic network and constraint-based metabolic model of Dehalococcoides. The pan-genome was constructed from publicly available complete genome sequences of Dehalococcoides sp. strain CBDB1, strain 195, strain BAV1, and strain VS. We found that Dehalococcoides pan-genome consisted of 1118 core genes (shared by all, 457 dispensable genes (shared by some, and 486 unique genes (found in only one genome. The model included 549 metabolic genes that encoded 356 proteins catalyzing 497 gene-associated model reactions. Of these 497 reactions, 477 were associated with core metabolic genes, 18 with dispensable genes, and 2 with unique genes. This study, in addition to analyzing the metabolism of an environmentally important phylogenetic group on a pan-genome scale, provides valuable insights into Dehalococcoides metabolic limitations, low growth yields, and energy conservation. The model also provides a framework to anchor and compare disparate experimental data, as well as to give insights on the physiological impact of "incomplete" pathways, such as the TCA-cycle, CO(2 fixation, and cobalamin biosynthesis pathways. The model, referred to as iAI549, highlights the specialized and highly conserved nature of Dehalococcoides metabolism, and suggests that evolution of Dehalococcoides species is driven by the electron acceptor availability.

  10. Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation

    Energy Technology Data Exchange (ETDEWEB)

    Bordbar, Aarash; Mo, Monica L.; Nakayasu, Ernesto S.; Rutledge, Alexandra C.; Kim, Young-Mo; Metz, Thomas O.; Jones, Marcus B.; Frank, Bryan C.; Smith, Richard D.; Peterson, Scott N.; Hyduke, Daniel R.; Adkins, Joshua N.; Palsson, Bernhard O.

    2012-06-26

    Macrophages are central players in the immune response, manifesting divergent phenotypes to control inflammation and innate immunity through the release of cytokines and other regulatory factor-dependent signaling pathways. In recent years, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features critical for macrophage functions. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of macrophage activation. Metabolites well-known to be associated with immunoactivation (e.g., glucose and arginine) and immunosuppression (e.g., tryptophan and vitamin D3) were amongst the most critical effectors. Intracellular metabolic mechanisms linked to critical suppressive effectors were then assessed, identifying a suppressive role for de novo nucleotide synthesis. Finally, the underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Our study demonstrates metabolism's role in regulating activation may be greater than previously anticipated and elucidates underlying metabolic connections between activation and metabolic effectors.

  11. Constraint-based modeling analysis of the metabolism of two Pelobacter species

    Directory of Open Access Journals (Sweden)

    Bui Olivia

    2010-12-01

    Full Text Available Abstract Background Pelobacter species are commonly found in a number of subsurface environments, and are unique members of the Geobacteraceae family. They are phylogenetically intertwined with both Geobacter and Desulfuromonas species. Pelobacter species likely play important roles in the fermentative degradation of unusual organic matters and syntrophic metabolism in the natural environments, and are of interest for applications in bioremediation and microbial fuel cells. Results In order to better understand the physiology of Pelobacter species, genome-scale metabolic models for Pelobacter carbinolicus and Pelobacter propionicus were developed. Model development was greatly aided by the availability of models of the closely related Geobacter sulfurreducens and G. metallireducens. The reconstructed P. carbinolicus model contains 741 genes and 708 reactions, whereas the reconstructed P. propionicus model contains 661 genes and 650 reactions. A total of 470 reactions are shared among the two Pelobacter models and the two Geobacter models. The different reactions between the Pelobacter and Geobacter models reflect some unique metabolic capabilities such as fermentative growth for both Pelobacter species. The reconstructed Pelobacter models were validated by simulating published growth conditions including fermentations, hydrogen production in syntrophic co-culture conditions, hydrogen utilization, and Fe(III reduction. Simulation results matched well with experimental data and indicated the accuracy of the models. Conclusions We have developed genome-scale metabolic models of P. carbinolicus and P. propionicus. These models of Pelobacter metabolism can now be incorporated into the growing repertoire of genome scale models of the Geobacteraceae family to aid in describing the growth and activity of these organisms in anoxic environments and in the study of their roles and interactions in the subsurface microbial community.

  12. GIM3E: Condition-specific Models of Cellular Metabolism Developed from Metabolomics and Expression Data

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, Brian; Ebrahim, Ali; Metz, Thomas O.; Adkins, Joshua N.; Palsson, Bernard O.; Hyduke, Daniel R.

    2013-11-15

    Motivation: Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been reported. Results: GIMMME (Gene Inactivation Moderated by Metabolism, Metabolomics, and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics, and intracellular metabolomics data. GIMMME establishes metabolite utilization requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions, and also provides calculations of the turnover (production / consumption) flux of metabolites. GIMMME was employed to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. Availability: GIMMME has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/

  13. von Bertalanffy 1.0: a COBRA toolbox extension to thermodynamically constrain metabolic models.

    Science.gov (United States)

    Fleming, Ronan M T; Thiele, Ines

    2011-01-01

    In flux balance analysis of genome scale stoichiometric models of metabolism, the principal constraints are uptake or secretion rates, the steady state mass conservation assumption and reaction directionality. Here, we introduce an algorithmic pipeline for quantitative assignment of reaction directionality in multi-compartmental genome scale models based on an application of the second law of thermodynamics to each reaction. Given experimental or computationally estimated standard metabolite species Gibbs energy and metabolite concentrations, the algorithms bounds reaction Gibbs energy, which is transformed to in vivo pH, temperature, ionic strength and electrical potential. This cross-platform MATLAB extension to the COnstraint-Based Reconstruction and Analysis (COBRA) toolbox is computationally efficient, extensively documented and open source. http://opencobra.sourceforge.net.

  14. Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data

    Directory of Open Access Journals (Sweden)

    Maranas Costas D

    2010-12-01

    Full Text Available Abstract Background Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., iMM904 remains significantly less predictive than the latest E. coli model (i.e., iAF1260. This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model. Results In this paper we make use of the automated GrowMatch procedure for restoring consistency with single gene deletion experiments in yeast and extend the procedure to make use of synthetic lethality data using the genome-scale model iMM904 as a basis. We identified and vetted using literature sources 120 distinct model modifications including various regulatory constraints for minimal and YP media. The incorporation of the suggested modifications led to a substantial increase in the fraction of correctly predicted lethal knockouts (i.e., specificity from 38.84% (87 out of 224 to 53.57% (120 out of 224 for the minimal medium and from 24.73% (45 out of 182 to 40.11% (73 out of 182 for the YP medium. Synthetic lethality predictions improved from 12.03% (16 out of 133 to 23.31% (31 out of 133 for the minimal medium and from 6.96% (8 out of 115 to 13.04% (15 out of 115 for the YP medium. Conclusions Overall, this study provides a roadmap for the computationally driven correction of multi-compartment genome-scale

  15. PSAMM: A Portable System for the Analysis of Metabolic Models.

    Directory of Open Access Journals (Sweden)

    Jon Lund Steffensen

    2016-02-01

    Full Text Available The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM, a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies.

  16. Genome Scale Transcriptomics of Baculovirus-Insect Interactions

    Directory of Open Access Journals (Sweden)

    Steven Reid

    2013-11-01

    Full Text Available Baculovirus-insect cell technologies are applied in the production of complex proteins, veterinary and human vaccines, gene delivery vectors‚ and biopesticides. Better understanding of how baculoviruses and insect cells interact would facilitate baculovirus-based production. While complete genomic sequences are available for over 58 baculovirus species, little insect genomic information is known. The release of the Bombyx mori and Plutella xylostella genomes, the accumulation of EST sequences for several Lepidopteran species, and especially the availability of two genome-scale analysis tools, namely oligonucleotide microarrays and next generation sequencing (NGS, have facilitated expression studies to generate a rich picture of insect gene responses to baculovirus infections. This review presents current knowledge on the interaction dynamics of the baculovirus-insect system‚ which is relatively well studied in relation to nucleocapsid transportation, apoptosis, and heat shock responses, but is still poorly understood regarding responses involved in pro-survival pathways, DNA damage pathways, protein degradation, translation, signaling pathways, RNAi pathways, and importantly metabolic pathways for energy, nucleotide and amino acid production. We discuss how the two genome-scale transcriptomic tools can be applied for studying such pathways and suggest that proteomics and metabolomics can produce complementary findings to transcriptomic studies.

  17. Genome scale transcriptomics of baculovirus-insect interactions.

    Science.gov (United States)

    Nguyen, Quan; Nielsen, Lars K; Reid, Steven

    2013-11-12

    Baculovirus-insect cell technologies are applied in the production of complex proteins, veterinary and human vaccines, gene delivery vectors' and biopesticides. Better understanding of how baculoviruses and insect cells interact would facilitate baculovirus-based production. While complete genomic sequences are available for over 58 baculovirus species, little insect genomic information is known. The release of the Bombyx mori and Plutella xylostella genomes, the accumulation of EST sequences for several Lepidopteran species, and especially the availability of two genome-scale analysis tools, namely oligonucleotide microarrays and next generation sequencing (NGS), have facilitated expression studies to generate a rich picture of insect gene responses to baculovirus infections. This review presents current knowledge on the interaction dynamics of the baculovirus-insect system' which is relatively well studied in relation to nucleocapsid transportation, apoptosis, and heat shock responses, but is still poorly understood regarding responses involved in pro-survival pathways, DNA damage pathways, protein degradation, translation, signaling pathways, RNAi pathways, and importantly metabolic pathways for energy, nucleotide and amino acid production. We discuss how the two genome-scale transcriptomic tools can be applied for studying such pathways and suggest that proteomics and metabolomics can produce complementary findings to transcriptomic studies.

  18. 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.

  19. SHARP: genome-scale identification of gene-protein-reaction associations in cyanobacteria.

    Science.gov (United States)

    Krishnakumar, S; Durai, Dilip A; Wangikar, Pramod P; Viswanathan, Ganesh A

    2013-11-01

    Genome scale metabolic model provides an overview of an organism's metabolic capability. These genome-specific metabolic reconstructions are based on identification of gene to protein to reaction (GPR) associations and, in turn, on homology with annotated genes from other organisms. Cyanobacteria are photosynthetic prokaryotes which have diverged appreciably from their nonphotosynthetic counterparts. They also show significant evolutionary divergence from plants, which are well studied for their photosynthetic apparatus. We argue that context-specific sequence and domain similarity can add to the repertoire of the GPR associations and significantly expand our view of the metabolic capability of cyanobacteria. We took an approach that combines the results of context-specific sequence-to-sequence similarity search with those of sequence-to-profile searches. We employ PSI-BLAST for the former, and CDD, Pfam, and COG for the latter. An optimization algorithm was devised to arrive at a weighting scheme to combine the different evidences with KEGG-annotated GPRs as training data. We present the algorithm in the form of software "Systematic, Homology-based Automated Re-annotation for Prokaryotes (SHARP)." We predicted 3,781 new GPR associations for the 10 prokaryotes considered of which eight are cyanobacteria species. These new GPR associations fall in several metabolic pathways and were used to annotate 7,718 gaps in the metabolic network. These new annotations led to discovery of several pathways that may be active and thereby providing new directions for metabolic engineering of these species for production of useful products. Metabolic model developed on such a reconstructed network is likely to give better phenotypic predictions.

  20. An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models.

    Science.gov (United States)

    Chindelevitch, Leonid; Trigg, Jason; Regev, Aviv; Berger, Bonnie

    2014-10-07

    Constraint-based models are currently the only methodology that allows the study of metabolism at the whole-genome scale. Flux balance analysis is commonly used to analyse constraint-based models. Curiously, the results of this analysis vary with the software being run, a situation that we show can be remedied by using exact rather than floating-point arithmetic. Here we introduce MONGOOSE, a toolbox for analysing the structure of constraint-based metabolic models in exact arithmetic. We apply MONGOOSE to the analysis of 98 existing metabolic network models and find that the biomass reaction is surprisingly blocked (unable to sustain non-zero flux) in nearly half of them. We propose a principled approach for unblocking these reactions and extend it to the problems of identifying essential and synthetic lethal reactions and minimal media. Our structural insights enable a systematic study of constraint-based metabolic models, yielding a deeper understanding of their possibilities and limitations.

  1. 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.

  2. 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.

  3. Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity

    Directory of Open Access Journals (Sweden)

    Ed Reznik

    2017-09-01

    Full Text Available Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g., allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical limitations have hindered the production of a genome-scale atlas of small molecule-enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets. Using metabolic control analysis, we prove a fundamental trade-off between regulation and enzymatic activity, and we combine it with metabolomic measurements and the SMRN to make inferences on the sensitivity of enzymes to their regulators. Generalizing the analysis to other organisms, we identify highly conserved regulatory interactions across evolutionarily divergent species, further emphasizing a critical role for small molecule interactions in the maintenance of metabolic homeostasis.

  4. Systems Biology of Metabolism.

    Science.gov (United States)

    Nielsen, Jens

    2017-06-20

    Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

  5. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

    OpenAIRE

    2011-01-01

    Over the past decade, a growing community of researchers has emerged around the use of COnstraint-Based Reconstruction and Analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a significant update of this in silico ToolBox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis m...

  6. 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.

  7. Model-driven discovery of underground metabolic functions in Escherichia coli

    DEFF Research Database (Denmark)

    Guzmán, Gabriela I.; Utrilla, José; Nurk, Sergey

    2015-01-01

    of unknown underground pathways stemming from enzymatic cross-reactivity. We demonstrate a workflow that couples constraint-based modeling and bioinformatic tools with KO strain analysis and adaptive laboratory evolution for the purpose of predicting promiscuity at the genome scale. Three cases of genes......E, and gltA and prpC. This study demonstrates how a targeted model-driven approach to discovery can systematically fill knowledge gaps, characterize underground metabolism, and elucidate regulatory mechanisms of adaptation in response to gene KO perturbations....

  8. Systems biology as a foundation for genome-scale synthetic biology.

    Science.gov (United States)

    Barrett, Christian L; Kim, Tae Yong; Kim, Hyun Uk; Palsson, Bernhard Ø; Lee, Sang Yup

    2006-10-01

    As the ambitions of synthetic biology approach genome-scale engineering, comprehensive characterization of cellular systems is required, as well as a means to accurately model cell-scale molecular interactions. These requirements are coincident with the goals of systems biology and, thus, systems biology will become the foundation for genome-scale synthetic biology. Systems biology will form this foundation through its efforts to reconstruct and integrate cellular systems, develop the mathematics, theory and software tools for the accurate modeling of these integrated systems, and through evolutionary mechanisms. As genome-scale synthetic biology is so enabled, it will prove to be a positive feedback driver of systems biology by exposing and forcing researchers to confront those aspects of systems biology which are inadequately understood.

  9. Tapping into Salmonella typhimurium LT2 genome in a quest to explore its therapeutic arsenal: A metabolic network modeling approach.

    Science.gov (United States)

    Mehla, Kusum; Ramana, Jayashree

    2017-02-01

    S. typhimurium, the classical broad-host-range serovar is a widely distributed cause of food-borne illness. Escalating antibiotic resistance and potential of conjugal transmission to other pathogens attributable to its broad spectrum host specificities have aided S. typhimurium to emerge as a global health threat. To keep pace with ever evolving bacterial defenses, there is dire need to restock the antibiotic pipeline. Genome scale metabolic reconstructions present immense possibilities to decipher physiological properties of an organism using constraint-based methods The systems-level approaches of genome scale metabolic networks interrogation open up new avenues of drug target identification against deadly infectious diseases. We performed flux balance analysis and minimization of metabolic adjustment studies of genome scale reconstruction model of S. typhimurium targeted at identifying large number of metabolites with a potential to be utilized as therapeutic drug targets. These constraint based approaches initially predict a set of genes indispensable to bacterial survival by performing gene knockout studies which are then prioritized through a multistep process. Metabolites involved in l-rhamnose biosynthesis, peptidoglycan biosynthesis, fatty acid biosynthesis, and folate biosynthesis pathways were prioritized as candidate drug targets. This study provides a general therapeutic approach which can be effectively applied to other pathogens as well.

  10. 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.

  11. Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm

    NARCIS (Netherlands)

    Megchelenbrink, W.; Rossell, S.; Huynen, M.A.; Notebaart, R.A.; Marchiori, E.

    2015-01-01

    MOTIVATION: Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologicall

  12. 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.

  13. 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

    Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks. The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing. So far the predictive capability...... 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...... 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...

  14. Mathematical Modeling of Cellular Metabolism.

    Science.gov (United States)

    Berndt, Nikolaus; Holzhütter, Hermann-Georg

    2016-01-01

    Cellular metabolism basically consists of the conversion of chemical compounds taken up from the extracellular environment into energy (conserved in energy-rich bonds of organic phosphates) and a wide array of organic molecules serving as catalysts (enzymes), information carriers (nucleic acids), and building blocks for cellular structures such as membranes or ribosomes. Metabolic modeling aims at the construction of mathematical representations of the cellular metabolism that can be used to calculate the concentration of cellular molecules and the rates of their mutual chemical interconversion in response to varying external conditions as, for example, hormonal stimuli or supply of essential nutrients. Based on such calculations, it is possible to quantify complex cellular functions as cellular growth, detoxification of drugs and xenobiotic compounds or synthesis of exported molecules. Depending on the specific questions to metabolism addressed, the methodological expertise of the researcher, and available experimental information, different conceptual frameworks have been established, allowing the usage of computational methods to condense experimental information from various layers of organization into (self-) consistent models. Here, we briefly outline the main conceptual frameworks that are currently exploited in metabolism research.

  15. Modeling rice metabolism: from elucidating environmental effects on cellular phenotype to guiding crop improvement

    Directory of Open Access Journals (Sweden)

    Meiyappan Lakshmanan

    2016-11-01

    Full Text Available Crop productivity is severely limited by various biotic and abiotic stresses. Thus, it is highly needed to understand the underlying mechanisms of environmental stress response and tolerance in plants, which could be addressed by systems biology approach. To this end, high-throughput omics profiling and in silico modeling can be considered to explore the environmental effects on phenotypic states and metabolic behaviors of rice crops at the systems level. Especially, the advent of constraint-based metabolic reconstruction and analysis paves a way to characterize the plant cellular physiology under various stresses by combining the mathematical network models with multi-omics data. Rice metabolic networks have been reconstructed since 2013 and currently 6 such networks are available, where 5 are at genome-scale. Since their publication, these models have been utilized to systematically elucidate the rice abiotic stress responses and identify agronomic traits for crop improvement. In this review, we summarize the current status of the existing rice metabolic networks and models with their applications. Furthermore, we also highlight future directions of rice modeling studies, particularly stressing how these models can be used to contextualize the affluent multi-omics data that are readily available in the public domain. Overall, we envisage a number of studies in the future, exploiting the available metabolic models to enhance the yield and quality of rice and other food crops.

  16. Integration of a constraint-based metabolic model of Brassica napus developing seeds with (13)C-metabolic flux analysis.

    Science.gov (United States)

    Hay, Jordan O; Shi, Hai; Heinzel, Nicolas; Hebbelmann, Inga; Rolletschek, Hardy; Schwender, Jorg

    2014-01-01

    The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for (13)C-Metabolic Flux Analysis ((13)C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from (13)C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content.

  17. Integration of a constraint-based metabolic model of Brassica napus developing seeds with 13C-Metabolic Flux Analysis

    Directory of Open Access Journals (Sweden)

    Jordan eHay

    2014-12-01

    Full Text Available The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using Brassica napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for 13C-Metabolic Flux Analysis (13C-MFA with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from 13C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA. Using this combined approach we characterize the difference in metabolic flux of developing seeds of two Brassica napus genotypes contrasting in starch and

  18. 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.

  19. 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.

  20. Metabolic Adaptation after Whole Genome Duplication

    NARCIS (Netherlands)

    Hoek, M.J.A. van; Hogeweg, P.

    2009-01-01

    Whole genome duplications (WGDs) have been hypothesized to be responsible for major transitions in evolution. However, the effects of WGD and subsequent gene loss on cellular behavior and metabolism are still poorly understood. Here we develop a genome scale evolutionary model to study the dynamics

  1. Unraveling the Light-Specific Metabolic and Regulatory Signatures of Rice through Combined in Silico Modeling and Multiomics Analysis.

    Science.gov (United States)

    Lakshmanan, Meiyappan; Lim, Sun-Hyung; Mohanty, Bijayalaxmi; Kim, Jae Kwang; Ha, Sun-Hwa; Lee, Dong-Yup

    2015-12-01

    Light quality is an important signaling component upon which plants orchestrate various morphological processes, including seed germination and seedling photomorphogenesis. However, it is still unclear how plants, especially food crops, sense various light qualities and modulate their cellular growth and other developmental processes. Therefore, in this work, we initially profiled the transcripts of a model crop, rice (Oryza sativa), under four different light treatments (blue, green, red, and white) as well as in the dark. Concurrently, we reconstructed a fully compartmentalized genome-scale metabolic model of rice cells, iOS2164, containing 2,164 unique genes, 2,283 reactions, and 1,999 metabolites. We then combined the model with transcriptome profiles to elucidate the light-specific transcriptional signatures of rice metabolism. Clearly, light signals mediated rice gene expressions, differentially regulating numerous metabolic pathways: photosynthesis and secondary metabolism were up-regulated in blue light, whereas reserve carbohydrates degradation was pronounced in the dark. The topological analysis of gene expression data with the rice genome-scale metabolic model further uncovered that phytohormones, such as abscisate, ethylene, gibberellin, and jasmonate, are the key biomarkers of light-mediated regulation, and subsequent analysis of the associated genes' promoter regions identified several light-specific transcription factors. Finally, the transcriptional control of rice metabolism by red and blue light signals was assessed by integrating the transcriptome and metabolome data with constraint-based modeling. The biological insights gained from this integrative systems biology approach offer several potential applications, such as improving the agronomic traits of food crops and designing light-specific synthetic gene circuits in microbial and mammalian systems.

  2. 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

    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......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...... promoters, we do not yet have a genome-scale assessment of their function. Results: Using multiple genome-scale measurements, we elucidated the network of s-factor and promoter interactions in Escherichia coli. The reconstructed network includes 4,724 sigma-factor-specific promoters corresponding...

  3. Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters

    NARCIS (Netherlands)

    Adadi, Roi; Volkmer, Benjamin; Milo, Ron; Heinemann, Matthias; Shlomi, Tomer

    2012-01-01

    Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal bio

  4. Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters

    NARCIS (Netherlands)

    Adadi, Roi; Volkmer, Benjamin; Milo, Ron; Heinemann, Matthias; Shlomi, Tomer

    Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal

  5. 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

  6. A Prochlorococcus proving ground for constraint-based metabolic modeling and multi-`omics data integration

    Science.gov (United States)

    Casey, J.; Ji, B.; Shaoie, S.; Mardinoglu, A.; Sarathi Sen, P.; Jahn, O.; Reda, K.; Leigh, J.; Follows, M. J.; Nielsen, J.; Karl, D. M.

    2016-02-01

    Representatives of the oligotrophic marine cyanobacterium Prochlorococcus marinus are the smallest free-living photosynthetic organisms, both in terms of physical size and genome size, yet are the most abundant photoautotrophic microbes in the oceans and profoundly influence global biogeochemical cycles. Physiological and regulatory control of nutrient and light stress has been observed in MED4 in culture and in its closely related `ecotype' eMED4 in the field, however its metabolism has not been investigated in detail. We present a genome-scale metabolic network reconstruction of the high-light adapted axenic strain MED4ax ("iJCMED4") for the quantitative analysis of a range of its metabolic phenotypes. The resulting structure is a proving ground for the incorporation of enzyme kinetics, biochemical and elemental compositional data, transcriptomic, proteomic, metabolomic, and fluxomic datasets which can be implemented within a constraint-based metabolic modeling environment. The iJCMED4 stoichiometric model consists of 523 metabolic genes encoding 787 reactions with 673 unique metabolites distributed in 5 sub-cellular compartments and is mass, charge, and thermodynamically balanced. Several variants of flux balance analysis were used to simulate growth and metabolic fluxes over the diel cycle, under various stress conditions (e.g., nitrogen, phosphorus, light), and within the framework of a global biogeochemical model (DARWIN). Model simulations accurately predicted growth rates in culture under a variety of defined medium compositions and there was close agreement of photosynthetic performance, biomass and energy yields and efficiencies, and transporter fluxes for iJCMED4 and culture experiments. In addition to a nearly optimal photosynthetic quotient and central carbon metabolism efficiency, MED4 has made dramatic alterations to redox and phosphorus metabolism across biosynthetic and intermediate pathways. We propose that reductions in phosphate reaction

  7. Elucidating the interactions between the human gut microbiota and its host through metabolic modeling

    Directory of Open Access Journals (Sweden)

    Saeed eShoaie

    2014-04-01

    Full Text Available Increased understanding of the interactions between the gut microbiota, diet and environmental effects may allow us to design efficient treatment strategies for addressing global health problems. Existence of symbiotic microorganisms in the human gut provides different functions for the host such as conversion of nutrients, training of the immune system and resistance to pathogens. The gut microbiome also plays an influential role in maintaining human health, and it is a potential target for prevention and treatment of common disorders including obesity, type 2 diabetes and atherosclerosis. Due to the extreme complexity of such disorders, it is necessary to develop mathematical models for deciphering the role of its individual elements as well as the entire system and such models may assist in better understanding of the interactions between the bacteria in the human gut and the host by use of genome-scale metabolic models (GEMs. Recently, GEMs have been employed to explore the interactions between predominant bacteria in the gut ecosystems. Additionally, these models enabled analysis of the contribution of each species to the overall metabolism of the microbiota through the integration of omics data. The outcome of these studies can be used for proposing optimal conditions for desired microbiome phenotypes. Here, we review the recent progress and challenges for elucidating the interactions between the human gut microbiota and host through metabolic modeling. We discuss how these models may provide scaffolds for analyzing high throughput data, developing probiotics and prebiotics, evaluating the effects of probiotics and prebiotics and eventually designing clinical interventions.

  8. Determination of sample size in genome-scale RNAi screens.

    Science.gov (United States)

    Zhang, Xiaohua Douglas; Heyse, Joseph F

    2009-04-01

    For genome-scale RNAi research, it is critical to investigate sample size required for the achievement of reasonably low false negative rate (FNR) and false positive rate. The analysis in this article reveals that current design of sample size contributes to the occurrence of low signal-to-noise ratio in genome-scale RNAi projects. The analysis suggests that (i) an arrangement of 16 wells per plate is acceptable and an arrangement of 20-24 wells per plate is preferable for a negative control to be used for hit selection in a primary screen without replicates; (ii) in a confirmatory screen or a primary screen with replicates, a sample size of 3 is not large enough, and there is a large reduction in FNRs when sample size increases from 3 to 4. To search a tradeoff between benefit and cost, any sample size between 4 and 11 is a reasonable choice. If the main focus is the selection of siRNAs with strong effects, a sample size of 4 or 5 is a good choice. If we want to have enough power to detect siRNAs with moderate effects, sample size needs to be 8, 9, 10 or 11. These discoveries about sample size bring insight to the design of a genome-scale RNAi screen experiment.

  9. Toolbox model of evolution of prokaryotic metabolic networks and their regulation.

    Science.gov (United States)

    Maslov, Sergei; Krishna, Sandeep; Pang, Tin Yau; Sneppen, Kim

    2009-06-16

    It has been reported that the number of transcription factors encoded in prokaryotic genomes scales approximately quadratically with their total number of genes. We propose a conceptual explanation of this finding and illustrate it using a simple model in which metabolic and regulatory networks of prokaryotes are shaped by horizontal gene transfer of coregulated metabolic pathways. Adapting to a new environmental condition monitored by a new transcription factor (e.g., learning to use another nutrient) involves both acquiring new enzymes and reusing some of the enzymes already encoded in the genome. As the repertoire of enzymes of an organism (its toolbox) grows larger, it can reuse its enzyme tools more often and thus needs to get fewer new ones to master each new task. From this observation, it logically follows that the number of functional tasks and their regulators increases faster than linearly with the total number of genes encoding enzymes. Genomes can also shrink, e.g., because of a loss of a nutrient from the environment, followed by deletion of its regulator and all enzymes that become redundant. We propose several simple models of network evolution elaborating on this toolbox argument and reproducing the empirically observed quadratic scaling. The distribution of lengths of pathway branches in our model agrees with that of the real-life metabolic network of Escherichia coli. Thus, our model provides a qualitative explanation for broad distributions of regulon sizes in prokaryotes.

  10. Hybrid metabolic flux analysis: combining stoichiometric and statistical constraints to model the formation of complex recombinant products

    Directory of Open Access Journals (Sweden)

    Alves Paula M

    2011-02-01

    Full Text Available Abstract Background Stoichiometric models constitute the basic framework for fluxome quantification in the realm of metabolic engineering. A recurrent bottleneck, however, is the establishment of consistent stoichiometric models for the synthesis of recombinant proteins or viruses. Although optimization algorithms for in silico metabolic redesign have been developed in the context of genome-scale stoichiometric models for small molecule production, still rudimentary knowledge of how different cellular levels are regulated and phenotypically expressed prevents their full applicability for complex product optimization. Results A hybrid framework is presented combining classical metabolic flux analysis with projection to latent structures to further link estimated metabolic fluxes with measured productivities. We first explore the functional metabolic decomposition of a baculovirus-producing insect cell line from experimental data, highlighting the TCA cycle and mitochondrial respiration as pathways strongly associated with viral replication. To reduce uncertainty in metabolic target identification, a Monte Carlo sampling method was used to select meaningful associations with the target, from which 66% of the estimated fluxome had to be screened out due to weak correlations and/or high estimation errors. The proposed hybrid model was then validated using a subset of preliminary experiments to pinpoint the same determinant pathways, while predicting the productivity of independent cultures. Conclusions Overall, the results indicate our hybrid metabolic flux analysis framework is an advantageous tool for metabolic identification and quantification in incomplete or ill-defined metabolic networks. As experimental and computational solutions for constructing comprehensive global cellular models are in development, the contribution of hybrid metabolic flux analysis should constitute a valuable complement to current computational platforms in bridging the

  11. Systematic planning of genome-scale experiments in poorly studied species.

    Science.gov (United States)

    Guan, Yuanfang; Dunham, Maitreya; Caudy, Amy; Troyanskaya, Olga

    2010-03-05

    Genome-scale datasets have been used extensively in model organisms to screen for specific candidates or to predict functions for uncharacterized genes. However, despite the availability of extensive knowledge in model organisms, the planning of genome-scale experiments in poorly studied species is still based on the intuition of experts or heuristic trials. We propose that computational and systematic approaches can be applied to drive the experiment planning process in poorly studied species based on available data and knowledge in closely related model organisms. In this paper, we suggest a computational strategy for recommending genome-scale experiments based on their capability to interrogate diverse biological processes to enable protein function assignment. To this end, we use the data-rich functional genomics compendium of the model organism to quantify the accuracy of each dataset in predicting each specific biological process and the overlap in such coverage between different datasets. Our approach uses an optimized combination of these quantifications to recommend an ordered list of experiments for accurately annotating most proteins in the poorly studied related organisms to most biological processes, as well as a set of experiments that target each specific biological process. The effectiveness of this experiment- planning system is demonstrated for two related yeast species: the model organism Saccharomyces cerevisiae and the comparatively poorly studied Saccharomyces bayanus. Our system recommended a set of S. bayanus experiments based on an S. cerevisiae microarray data compendium. In silico evaluations estimate that less than 10% of the experiments could achieve similar functional coverage to the whole microarray compendium. This estimation was confirmed by performing the recommended experiments in S. bayanus, therefore significantly reducing the labor devoted to characterize the poorly studied genome. This experiment-planning framework could

  12. Analysis of metabolic flux using dynamic labelling and metabolic modelling.

    Science.gov (United States)

    Fernie, A R; Morgan, J A

    2013-09-01

    Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms that control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches have been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences, is reviewed, and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives, and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed.

  13. 13C Metabolic Flux Analysis for Systematic Metabolic Engineering of S. cerevisiae for Overproduction of Fatty Acids

    DEFF Research Database (Denmark)

    Ghosh, Amit; Ando, David; Gin, Jennifer

    2016-01-01

    Efficient redirection of microbial metabolism into the abundant production of desired bioproducts remains non-trivial. Here, we used flux-based modeling approaches to improve yields of fatty acids in Saccharomyces cerevisiae. We combined 13C labeling data with comprehensive genome-scale models to...

  14. Metabolic model of Synechococcus sp. PCC 7002: Prediction of flux distribution and network modification for enhanced biofuel production.

    Science.gov (United States)

    Hendry, John I; Prasannan, Charulata B; Joshi, Aditi; Dasgupta, Santanu; Wangikar, Pramod P

    2016-08-01

    Flux Balance Analysis was performed with the Genome Scale Metabolic Model of a fast growing cyanobacterium Synechococcus sp. PCC 7002 to gain insights that would help in engineering the organism as a production host. Gene essentiality and synthetic lethality analysis revealed a reduced metabolic robustness under genetic perturbation compared to the heterotrophic bacteria Escherichia coli. Under glycerol heterotrophy the reducing equivalents were generated from tricarboxylic acid cycle rather than the oxidative pentose phosphate pathway. During mixotrophic growth in glycerol the photosynthetic electron transport chain was predominantly used for ATP synthesis with a photosystem I/photosystem II flux ratio higher than that observed under autotrophy. An exhaustive analysis of all possible double reaction knock outs was performed to reroute fixed carbon towards ethanol and butanol production. It was predicted that only ∼10% of fixed carbon could be diverted for ethanol and butanol production.

  15. Computational model of cellular metabolic dynamics

    DEFF Research Database (Denmark)

    Li, Yanjun; Solomon, Thomas; Haus, Jacob M

    2010-01-01

    : intracellular metabolite concentrations and patterns of glucose disposal. Model variations were simulated to investigate three alternative mechanisms to explain insulin enhancements: Model 1 (M.1), simple mass action; M.2, insulin-mediated activation of key metabolic enzymes (i.e., hexokinase, glycogen synthase......Identifying the mechanisms by which insulin regulates glucose metabolism in skeletal muscle is critical to understanding the etiology of insulin resistance and type 2 diabetes. Our knowledge of these mechanisms is limited by the difficulty of obtaining in vivo intracellular data. To quantitatively...... distinguish significant transport and metabolic mechanisms from limited experimental data, we developed a physiologically based, multiscale mathematical model of cellular metabolic dynamics in skeletal muscle. The model describes mass transport and metabolic processes including distinctive processes...

  16. Metabolic-hydaulic model Data

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The data contained in this workbook were compiled to investigate the relationship between hydrology of the Colorado River and ecosystem metabolism parameters (i.e.,...

  17. Metabolic model reconstruction and analysis of an artificial microbial ecosystem for vitamin C production.

    Science.gov (United States)

    Ye, Chao; Zou, Wei; Xu, Nan; Liu, Liming

    2014-07-20

    An artificial microbial ecosystem (AME) consisting of Ketogulonicigenium vulgare and Bacillus megaterium is currently used in a two-step fermentation process for vitamin C production. In order to obtain a comprehensive understanding of the metabolic interactions between the two bacteria, a two-species stoichiometric metabolic model (iWZ-KV-663-BM-1055) consisting of 1718 genes, 1573 metabolites, and 1891 reactions (excluding exchange reactions) was constructed based on separate genome-scale metabolic models (GSMMs) of K. vulgare and B. megaterium. These two compartments (k and b) of iWZ-KV-663-BM-1055 shared 453 reactions and 548 metabolites. Compartment b was richer in subsystems than compartment k. In minimal media with glucose (MG), metabolite exchange between compartments was assessed by constraint-based analysis. Compartment b secreted essential amino acids, nucleic acids, vitamins and cofactors important for K. vulgare growth and biosynthesis of 2-keto-l-gulonic acid (2-KLG). Further research showed that when co-cultured with B. megaterium in l-sorbose-CSLP medium, the growth rate of K. vulgare and 2-KLG production were increased by 111.9% and 29.42%, respectively, under the constraints employed. Our study demonstrated that GSMMs and constraint-based methods can be used to decode the physiological features and inter-species interactions of AMEs used in industrial biotechnology, which will be of benefit for improving regulation and refinement in future industrial processes.

  18. Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering

    Science.gov (United States)

    Baghalian, Kambiz; Hajirezaei, Mohammad-Reza; Schreiber, Falk

    2014-01-01

    Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology. PMID:25344492

  19. Evaluating computational models of cholesterol metabolism.

    Science.gov (United States)

    Paalvast, Yared; Kuivenhoven, Jan Albert; Groen, Albert K

    2015-10-01

    Regulation of cholesterol homeostasis has been studied extensively during the last decades. Many of the metabolic pathways involved have been discovered. Yet important gaps in our knowledge remain. For example, knowledge on intracellular cholesterol traffic and its relation to the regulation of cholesterol synthesis and plasma cholesterol levels is incomplete. One way of addressing the remaining questions is by making use of computational models. Here, we critically evaluate existing computational models of cholesterol metabolism making use of ordinary differential equations and addressed whether they used assumptions and make predictions in line with current knowledge on cholesterol homeostasis. Having studied the results described by the authors, we have also tested their models. This was done primarily by testing the effect of statin treatment in each model. Ten out of eleven models tested have made assumptions in line with current knowledge of cholesterol metabolism. Three out of the ten remaining models made correct predictions, i.e. predicting a decrease in plasma total and LDL cholesterol or increased uptake of LDL upon treatment upon the use of statins. In conclusion, few models on cholesterol metabolism are able to pass a functional test. Apparently most models have not undergone the critical iterative systems biology cycle of validation. We expect modeling of cholesterol metabolism to go through many more model topologies and iterative cycles and welcome the increased understanding of cholesterol metabolism these are likely to bring.

  20. Evolutionary models of metabolism, behaviour and personality.

    Science.gov (United States)

    Houston, Alasdair I

    2010-12-27

    I explore the relationship between metabolism and personality by establishing how selection acts on metabolic rate and risk-taking in the context of a trade-off between energy and predation. Using a simple time budget model, I show that a high resting metabolic rate is not necessarily associated with a high daily energy expenditure. The metabolic rate that minimizes the time spent foraging does not maximize the net gain rate while foraging, and it is not always advantageous for animals to have a higher metabolic rate when food availability is high. A model based on minimizing the ratio of mortality rate to net gain rate is used to determine how a willingness to take risks should be correlated with metabolic rate. My results establish that it is not always advantageous for animals to take greater risks when metabolic rate is high. When foraging intensity and metabolic rate coevolve, I show that in a particular case different combinations of foraging intensity and metabolic rate can have equal fitness.

  1. Cancer Metabolism: A Modeling Perspective

    DEFF Research Database (Denmark)

    Ghaffari, Pouyan; Mardinoglu, Adil; Nielsen, Jens

    2015-01-01

    requires both the advancement of experimental technologies for more comprehensive measurement of omics as well as the advancement of robust computational methods for accurate analysis of the generated data. Here, we review cancer-associated reprogramming of metabolism and highlight the capability of genome...... suggest that utilization of amino acids and lipids contributes significantly to cancer cell metabolism. Also recent progresses in our understanding of carcinogenesis have revealed that cancer is a complex disease and cannot be understood through simple investigation of genetic mutations of cancerous cells....... Cancer cells present in complex tumor tissues communicate with the surrounding microenvironment and develop traits which promote their growth, survival, and metastasis. Decoding the full scope and targeting dysregulated metabolic pathways that support neoplastic transformations and their preservation...

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

    Directory of Open Access Journals (Sweden)

    Gengjie Jia

    2012-11-01

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

  3. Flux Analysis Uncovers Key Role of Functional Redundancy in Formaldehyde Metabolism

    OpenAIRE

    Christopher J Marx; Van Dien, Stephen J.; Mary E Lidstrom

    2005-01-01

    Genome-scale analysis of predicted metabolic pathways has revealed the common occurrence of apparent redundancy for specific functional units, or metabolic modules. In many cases, mutation analysis does not resolve function, and instead, direct experimental analysis of metabolic flux under changing conditions is necessary. In order to use genome sequences to build models of cellular function, it is important to define function for such apparently redundant systems. Here we describe direct flu...

  4. Genome-scale engineering for systems and synthetic biology

    Science.gov (United States)

    Esvelt, Kevin M; Wang, Harris H

    2013-01-01

    Genome-modification technologies enable the rational engineering and perturbation of biological systems. Historically, these methods have been limited to gene insertions or mutations at random or at a few pre-defined locations across the genome. The handful of methods capable of targeted gene editing suffered from low efficiencies, significant labor costs, or both. Recent advances have dramatically expanded our ability to engineer cells in a directed and combinatorial manner. Here, we review current technologies and methodologies for genome-scale engineering, discuss the prospects for extending efficient genome modification to new hosts, and explore the implications of continued advances toward the development of flexibly programmable chasses, novel biochemistries, and safer organismal and ecological engineering. PMID:23340847

  5. Genome-scale engineering for systems and synthetic biology.

    Science.gov (United States)

    Esvelt, Kevin M; Wang, Harris H

    2013-01-01

    Genome-modification technologies enable the rational engineering and perturbation of biological systems. Historically, these methods have been limited to gene insertions or mutations at random or at a few pre-defined locations across the genome. The handful of methods capable of targeted gene editing suffered from low efficiencies, significant labor costs, or both. Recent advances have dramatically expanded our ability to engineer cells in a directed and combinatorial manner. Here, we review current technologies and methodologies for genome-scale engineering, discuss the prospects for extending efficient genome modification to new hosts, and explore the implications of continued advances toward the development of flexibly programmable chasses, novel biochemistries, and safer organismal and ecological engineering.

  6. Genome-scale validation of deep-sequencing libraries.

    Directory of Open Access Journals (Sweden)

    Dominic Schmidt

    Full Text Available Chromatin immunoprecipitation followed by high-throughput (HTP sequencing (ChIP-seq is a powerful tool to establish protein-DNA interactions genome-wide. The primary limitation of its broad application at present is the often-limited access to sequencers. Here we report a protocol, Mab-seq, that generates genome-scale quality evaluations for nucleic acid libraries intended for deep-sequencing. We show how commercially available genomic microarrays can be used to maximize the efficiency of library creation and quickly generate reliable preliminary data on a chromosomal scale in advance of deep sequencing. We also exploit this technique to compare enriched regions identified using microarrays with those identified by sequencing, demonstrating that they agree on a core set of clearly identified enriched regions, while characterizing the additional enriched regions identifiable using HTP sequencing.

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

    Science.gov (United States)

    Tabe-Bordbar, Shayan; Marashi, Sayed-Amir

    2013-12-01

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

  8. Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization.

    Directory of Open Access Journals (Sweden)

    Ines Thiele

    2009-03-01

    Full Text Available Metabolic network reconstructions represent valuable scaffolds for '-omics' data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA. Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron-sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1 quantitatively integrate gene expression data as reaction constraints and (2 compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of '-omics' datasets and thus the study of the mechanistic principles underlying the genotype-phenotype relationship.

  9. Toward metabolic engineering in the context of system biology and synthetic biology: advances and prospects.

    Science.gov (United States)

    Liu, Yanfeng; Shin, Hyun-dong; Li, Jianghua; Liu, Long

    2015-02-01

    Metabolic engineering facilitates the rational development of recombinant bacterial strains for metabolite overproduction. Building on enormous advances in system biology and synthetic biology, novel strategies have been established for multivariate optimization of metabolic networks in ensemble, spatial, and dynamic manners such as modular pathway engineering, compartmentalization metabolic engineering, and metabolic engineering guided by genome-scale metabolic models, in vitro reconstitution, and systems and synthetic biology. Herein, we summarize recent advances in novel metabolic engineering strategies. Combined with advancing kinetic models and synthetic biology tools, more efficient new strategies for improving cellular properties can be established and applied for industrially important biochemical production.

  10. Computational Modeling of Lipid Metabolism in Yeast

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    Vera Schützhold

    2016-09-01

    Full Text Available Lipid metabolism is essential for all major cell functions and has recently gained increasing attention in research and health studies. However, mathematical modeling by means of classical approaches such as stoichiometric networks and ordinary differential equation systems has not yet provided satisfactory insights, due to the complexity of lipid metabolism characterized by many different species with only slight differences and by promiscuous multifunctional enzymes.Here, we present a object-oriented stochastic model approach as a way to cope with the complex lipid metabolic network. While all lipid species are treated objects in the model, they can be modified by the respective converting reactions based on reaction rules, a hybrid method that integrates benefits of agent-based and classical stochastic simulation. This approach allows to follow the dynamics of all lipid species with different fatty acids, different degrees of saturation and different headgroups over time and to analyze the effect of parameter changes, potential mutations in the catalyzing enzymes or provision of different precursors. Applied to yeast metabolism during one cell cycle period, we could analyze the distribution of all lipids to the various membranes in time-dependent manner.The presented approach allows to efficiently treat the complexity of cellular lipid metabolism and to derive conclusions on the time- and location-dependent distributions of lipid species and their properties such as saturation. It is widely applicable, easily extendable and will provide further insights in healthy and diseased states of cell metabolism.

  11. Rodent Models for Metabolic Syndrome Research

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    Sunil K. Panchal

    2011-01-01

    Full Text Available Rodents are widely used to mimic human diseases to improve understanding of the causes and progression of disease symptoms and to test potential therapeutic interventions. Chronic diseases such as obesity, diabetes and hypertension, together known as the metabolic syndrome, are causing increasing morbidity and mortality. To control these diseases, research in rodent models that closely mimic the changes in humans is essential. This review will examine the adequacy of the many rodent models of metabolic syndrome to mimic the causes and progression of the disease in humans. The primary criterion will be whether a rodent model initiates all of the signs, especially obesity, diabetes, hypertension and dysfunction of the heart, blood vessels, liver and kidney, primarily by diet since these are the diet-induced signs in humans with metabolic syndrome. We conclude that the model that comes closest to fulfilling this criterion is the high carbohydrate, high fat-fed male rodent.

  12. Computational modeling of lipoprotein metabolism

    NARCIS (Netherlands)

    Schalkwijk, Daniël Bernardus van

    2013-01-01

    This PhD thesis contains the following chapters. The first part, containing chapter 2 and 3 mainly concerns model development. Chapter 2 describes the development of a mathematical modeling framework within which different diagnostic models based on lipoprotein profiles can be developed, and a first

  13. Kinetic models of conjugated metabolic cycles

    Science.gov (United States)

    Ershov, Yu. A.

    2016-01-01

    A general method is developed for the quantitative kinetic analysis of conjugated metabolic cycles in the human organism. This method is used as a basis for constructing a kinetic graph and model of the conjugated citric acid and ureapoiesis cycles. The results from a kinetic analysis of the model for these cycles are given.

  14. A mathematical model of glutathione metabolism

    Directory of Open Access Journals (Sweden)

    James S Jill

    2008-04-01

    Full Text Available Abstract Background Glutathione (GSH plays an important role in anti-oxidant defense and detoxification reactions. It is primarily synthesized in the liver by the transsulfuration pathway and exported to provide precursors for in situ GSH synthesis by other tissues. Deficits in glutathione have been implicated in aging and a host of diseases including Alzheimer's disease, Parkinson's disease, cardiovascular disease, cancer, Down syndrome and autism. Approach We explore the properties of glutathione metabolism in the liver by experimenting with a mathematical model of one-carbon metabolism, the transsulfuration pathway, and glutathione synthesis, transport, and breakdown. The model is based on known properties of the enzymes and the regulation of those enzymes by oxidative stress. We explore the half-life of glutathione, the regulation of glutathione synthesis, and its sensitivity to fluctuations in amino acid input. We use the model to simulate the metabolic profiles previously observed in Down syndrome and autism and compare the model results to clinical data. Conclusion We show that the glutathione pools in hepatic cells and in the blood are quite insensitive to fluctuations in amino acid input and offer an explanation based on model predictions. In contrast, we show that hepatic glutathione pools are highly sensitive to the level of oxidative stress. The model shows that overexpression of genes on chromosome 21 and an increase in oxidative stress can explain the metabolic profile of Down syndrome. The model also correctly simulates the metabolic profile of autism when oxidative stress is substantially increased and the adenosine concentration is raised. Finally, we discuss how individual variation arises and its consequences for one-carbon and glutathione metabolism.

  15. Systems Biology of cancer: Moving toward the Integrative Study of the metabolic alterations in cancer cells.

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    Claudia Erika Hernández Patiño

    2013-01-01

    Full Text Available One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: 1 the integration of data from high-throughput technologies, 2 the assessment of how metabolic activity is related to phenotype in cancer cell lines and 3 the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues.

  16. Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells.

    Science.gov (United States)

    Hernández Patiño, Claudia E; Jaime-Muñoz, Gustavo; Resendis-Antonio, Osbaldo

    2012-01-01

    One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues.

  17. HEMETβ: improvement of hepatocyte metabolism mathematical model.

    Science.gov (United States)

    Orsi, G; De Maria, C; Guzzardi, M; Vozzi, F; Vozzi, G

    2011-10-01

    This article describes hepatocyte metabolism mathematical model (HEMETβ), which is an improved version of HEMET, an effective and versatile virtual cell model based on hepatic cell metabolism. HEMET is based on a set of non-linear differential equations, implemented in Simulink®, which describes the biochemical reactions and energetic cell state, and completely mimics the principal metabolic pathways in hepatic cells. The cell energy function and modular structure are the core of this model. HEMETβ as HEMET model describes hepatic cellular metabolism in standard conditions (cell culture in a plastic multi-well placed in an incubator at 37° C with 5% of CO2) and with excess substrates concentration. The main improvements in HEMETβ are the introductions of Michaelis-Menten models for reversible reactions and enzymatic inhibition. In addition, we eliminated hard non-linearities and modelled cell proliferation and every single aminoacid degradation pathway. All these innovations, combined with a user-friendly aspect, allow researchers to create new cell types and validate new experimental protocols just varying 'peripheral' pathways or model inputs.

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

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    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.

  19. A genomic scale map of genetic diversity in Trypanosoma cruzi

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    Ackermann Alejandro A

    2012-12-01

    Full Text Available Abstract Background Trypanosoma cruzi, the causal agent of Chagas Disease, affects more than 16 million people in Latin America. The clinical outcome of the disease results from a complex interplay between environmental factors and the genetic background of both the human host and the parasite. However, knowledge of the genetic diversity of the parasite, is currently limited to a number of highly studied loci. The availability of a number of genomes from different evolutionary lineages of T. cruzi provides an unprecedented opportunity to look at the genetic diversity of the parasite at a genomic scale. Results Using a bioinformatic strategy, we have clustered T. cruzi sequence data available in the public domain and obtained multiple sequence alignments in which one or two alleles from the reference CL-Brener were included. These data covers 4 major evolutionary lineages (DTUs: TcI, TcII, TcIII, and the hybrid TcVI. Using these set of alignments we have identified 288,957 high quality single nucleotide polymorphisms and 1,480 indels. In a reduced re-sequencing study we were able to validate ~ 97% of high-quality SNPs identified in 47 loci. Analysis of how these changes affect encoded protein products showed a 0.77 ratio of synonymous to non-synonymous changes in the T. cruzi genome. We observed 113 changes that introduce or remove a stop codon, some causing significant functional changes, and a number of tri-allelic and tetra-allelic SNPs that could be exploited in strain typing assays. Based on an analysis of the observed nucleotide diversity we show that the T. cruzi genome contains a core set of genes that are under apparent purifying selection. Interestingly, orthologs of known druggable targets show statistically significant lower nucleotide diversity values. Conclusions This study provides the first look at the genetic diversity of T. cruzi at a genomic scale. The analysis covers an estimated ~ 60% of the genetic diversity present in the

  20. Metabolic syndrome components in murine models.

    Science.gov (United States)

    Lawson, Heather A; Cheverud, James M

    2010-03-01

    Animal models have enriched understanding of the physiological basis of metabolic disorders and advanced identification of genetic risk factors underlying the metabolic syndrome (MetS). Murine models are especially appropriate for this type of research, and are an excellent resource not only for identifying candidate genomic regions, but also for illuminating the possible molecular mechanisms or pathways affected in individual components of MetS. In this review, we briefly discuss findings from mouse models of metabolic disorders, particularly in light of issues raised by the recent flood of human genome-wide association studies (GWAS) results. We describe how mouse models are revealing that genotype interacts with environment in important ways, indicating that the underlying genetics of MetS is highly context dependant. Further we show that epistasis, imprinting and maternal effects each contribute to the genetic architecture underlying variation in metabolic traits, and mouse models provide an opportunity to dissect these aspects of the genetic architecture that are difficult if not impossible to ascertain in humans. Finally we discuss how knowledge gained from mouse models can be used in conjunction with comparative genomic methods and bioinformatic resources to inform human MetS research.

  1. Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks.

    Science.gov (United States)

    Chiappino-Pepe, Anush; Tymoshenko, Stepan; Ataman, Meriç; Soldati-Favre, Dominique; Hatzimanikatis, Vassily

    2017-03-01

    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.

  2. 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.

  3. 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

  4. Functional states of the genome-scale Escherichia coli transcriptional regulatory system.

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    Erwin P Gianchandani

    2009-06-01

    Full Text Available A transcriptional regulatory network (TRN constitutes the collection of regulatory rules that link environmental cues to the transcription state of a cell's genome. We recently proposed a matrix formalism that quantitatively represents a system of such rules (a transcriptional regulatory system [TRS] and allows systemic characterization of TRS properties. The matrix formalism not only allows the computation of the transcription state of the genome but also the fundamental characterization of the input-output mapping that it represents. Furthermore, a key advantage of this "pseudo-stoichiometric" matrix formalism is its ability to easily integrate with existing stoichiometric matrix representations of signaling and metabolic networks. Here we demonstrate for the first time how this matrix formalism is extendable to large-scale systems by applying it to the genome-scale Escherichia coli TRS. We analyze the fundamental subspaces of the regulatory network matrix (R to describe intrinsic properties of the TRS. We further use Monte Carlo sampling to evaluate the E. coli transcription state across a subset of all possible environments, comparing our results to published gene expression data as validation. Finally, we present novel in silico findings for the E. coli TRS, including (1 a gene expression correlation matrix delineating functional motifs; (2 sets of gene ontologies for which regulatory rules governing gene transcription are poorly understood and which may direct further experimental characterization; and (3 the appearance of a distributed TRN structure, which is in stark contrast to the more hierarchical organization of metabolic networks.

  5. Functional states of the genome-scale Escherichia coli transcriptional regulatory system.

    Science.gov (United States)

    Gianchandani, Erwin P; Joyce, Andrew R; Palsson, Bernhard Ø; Papin, Jason A

    2009-06-01

    A transcriptional regulatory network (TRN) constitutes the collection of regulatory rules that link environmental cues to the transcription state of a cell's genome. We recently proposed a matrix formalism that quantitatively represents a system of such rules (a transcriptional regulatory system [TRS]) and allows systemic characterization of TRS properties. The matrix formalism not only allows the computation of the transcription state of the genome but also the fundamental characterization of the input-output mapping that it represents. Furthermore, a key advantage of this "pseudo-stoichiometric" matrix formalism is its ability to easily integrate with existing stoichiometric matrix representations of signaling and metabolic networks. Here we demonstrate for the first time how this matrix formalism is extendable to large-scale systems by applying it to the genome-scale Escherichia coli TRS. We analyze the fundamental subspaces of the regulatory network matrix (R) to describe intrinsic properties of the TRS. We further use Monte Carlo sampling to evaluate the E. coli transcription state across a subset of all possible environments, comparing our results to published gene expression data as validation. Finally, we present novel in silico findings for the E. coli TRS, including (1) a gene expression correlation matrix delineating functional motifs; (2) sets of gene ontologies for which regulatory rules governing gene transcription are poorly understood and which may direct further experimental characterization; and (3) the appearance of a distributed TRN structure, which is in stark contrast to the more hierarchical organization of metabolic networks.

  6. Analysis of complex metabolic behavior through pathway decomposition

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    Ip Kuhn

    2011-06-01

    Full Text Available Abstract Background Understanding complex systems through decomposition into simple interacting components is a pervasive paradigm throughout modern science and engineering. For cellular metabolism, complexity can be reduced by decomposition into pathways with particular biochemical functions, and the concept of elementary flux modes provides a systematic way for organizing metabolic networks into such pathways. While decomposition using elementary flux modes has proven to be a powerful tool for understanding and manipulating cellular metabolism, its utility, however, is severely limited since the number of modes in a network increases exponentially with its size. Results Here, we present a new method for decomposition of metabolic flux distributions into elementary flux modes. Our method can easily operate on large, genome-scale networks since it does not require all relevant modes of the metabolic network to be generated. We illustrate the utility of our method for metabolic engineering of Escherichia coli and for understanding the survival of Mycobacterium tuberculosis (MTB during infection. Conclusions Our method can achieve computational time improvements exceeding 2000-fold and requires only several seconds to generate elementary mode decompositions on genome-scale networks. These improvements arise from not having to generate all relevant elementary modes prior to initiating the decomposition. The decompositions from our method are useful for understanding complex flux distributions and debugging genome-scale models.

  7. 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

    Background: Salmonella Typhimurium is an important pathogen of human and animals. It shows a broad growth range and survives in harsh conditions. The aim of this study was to analyze transcriptional responses to a number of growth and stress conditions as well as the relationship of metabolic...... pathways and/or cell functions at the genome-scale-level by network analysis, and further to explore whether highly connected genes ( hubs) in these networks were essential for growth, stress adaptation and virulence. Results: De novo generated as well as published transcriptional data for 425 selected...... genes under a number of growth and stress conditions were used to construct a bipartite network connecting culture conditions and significantly regulated genes (transcriptional network). Also, a genome scale network was constructed for strain LT2. The latter connected genes with metabolic pathways...

  8. Human Metabolic Network: Reconstruction, Simulation, and Applications in Systems Biology

    Science.gov (United States)

    Wu, Ming; Chan, Christina

    2012-01-01

    Metabolism is crucial to cell growth and proliferation. Deficiency or alterations in metabolic functions are known to be involved in many human diseases. Therefore, understanding the human metabolic system is important for the study and treatment of complex diseases. Current reconstructions of the global human metabolic network provide a computational platform to integrate genome-scale information on metabolism. The platform enables a systematic study of the regulation and is applicable to a wide variety of cases, wherein one could rely on in silico perturbations to predict novel targets, interpret systemic effects, and identify alterations in the metabolic states to better understand the genotype-phenotype relationships. In this review, we describe the reconstruction of the human metabolic network, introduce the constraint based modeling approach to analyze metabolic networks, and discuss systems biology applications to study human physiology and pathology. We highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system. PMID:24957377

  9. A markov classification model for metabolic pathways

    Directory of Open Access Journals (Sweden)

    Mamitsuka Hiroshi

    2010-01-01

    Full Text Available Abstract Background This paper considers the problem of identifying pathways through metabolic networks that relate to a specific biological response. Our proposed model, HME3M, first identifies frequently traversed network paths using a Markov mixture model. Then by employing a hierarchical mixture of experts, separate classifiers are built using information specific to each path and combined into an ensemble prediction for the response. Results We compared the performance of HME3M with logistic regression and support vector machines (SVM for both simulated pathways and on two metabolic networks, glycolysis and the pentose phosphate pathway for Arabidopsis thaliana. We use AltGenExpress microarray data and focus on the pathway differences in the developmental stages and stress responses of Arabidopsis. The results clearly show that HME3M outperformed the comparison methods in the presence of increasing network complexity and pathway noise. Furthermore an analysis of the paths identified by HME3M for each metabolic network confirmed known biological responses of Arabidopsis. Conclusions This paper clearly shows HME3M to be an accurate and robust method for classifying metabolic pathways. HME3M is shown to outperform all comparison methods and further is capable of identifying known biologically active pathways within microarray data.

  10. Mouse models of the metabolic syndrome.

    Science.gov (United States)

    Kennedy, Arion J; Ellacott, Kate L J; King, Victoria L; Hasty, Alyssa H

    2010-01-01

    The metabolic syndrome (MetS) is characterized by obesity concomitant with other metabolic abnormalities such as hypertriglyceridemia, reduced high-density lipoprotein levels, elevated blood pressure and raised fasting glucose levels. The precise definition of MetS, the relationships of its metabolic features, and what initiates it, are debated. However, obesity is on the rise worldwide, and its association with these metabolic symptoms increases the risk for diabetes and cardiovascular disease (among many other diseases). Research needs to determine the mechanisms by which obesity and MetS increase the risk of disease. In light of this growing epidemic, it is imperative to develop animal models of MetS. These models will help determine the pathophysiological basis for MetS and how MetS increases the risk for other diseases. Among the various animal models available to study MetS, mice are the most commonly used for several reasons. First, there are several spontaneously occurring obese mouse strains that have been used for decades and that are very well characterized. Second, high-fat feeding studies require only months to induce MetS. Third, it is relatively easy to study the effects of single genes by developing transgenic or gene knockouts to determine the influence of a gene on MetS. For these reasons, this review will focus on the benefits and caveats of the most common mouse models of MetS. It is our hope that the reader will be able to use this review as a guide for the selection of mouse models for their own studies.

  11. Deciphering the intracellular metabolism of Listeria monocytogenes by mutant screening and modelling

    Directory of Open Access Journals (Sweden)

    Dandekar Thomas

    2010-10-01

    Full Text Available Abstract Background The human pathogen Listeria monocytogenes resides and proliferates within the cytoplasm of epithelial cells. While the virulence factors essentially contributing to this step of the infection cycle are well characterized, the set of listerial genes contributing to intracellular replication remains to be defined on a genome-wide level. Results A comprehensive library of L. monocytogenes strain EGD knockout mutants was constructed upon insertion-duplication mutagenesis, and 1491 mutants were tested for their phenotypes in rich medium and in a Caco-2 cell culture assay. Following sequencing of the plasmid insertion site, 141 different genes required for invasion of and replication in Caco-2 cells were identified. Ten in-frame deletion mutants were constructed that confirmed the data. The genes with known functions are mainly involved in cellular processes including transport, in the intermediary metabolism of sugars, nucleotides and lipids, and in information pathways such as regulatory functions. No function could be ascribed to 18 genes, and a counterpart of eight genes is missing in the apathogenic species L. innocua. Mice infection studies revealed the in vivo requirement of IspE (Lmo0190 involved in mevalonate synthesis, and of the novel ABC transporter Lmo0135-0137 associated with cysteine transport. Based on the data of this genome-scale screening, an extreme pathway and elementary mode analysis was applied that demonstrates the critical role of glycerol and purine metabolism, of fucose utilization, and of the synthesis of glutathione, aspartate semialdehyde, serine and branched chain amino acids during intracellular replication of L. monocytogenes. Conclusion The combination of a genetic screening and a modelling approach revealed that a series of transporters help L. monocytogenes to overcome a putative lack of nutrients within cells, and that a high metabolic flexibility contributes to the intracellular replication of

  12. Complex network perspective on structure and function of Staphylococcus aureus metabolic network

    Indian Academy of Sciences (India)

    L Ying; D W Ding

    2013-02-01

    With remarkable advances in reconstruction of genome-scale metabolic networks, uncovering complex network structure and function from these networks is becoming one of the most important topics in system biology. This work aims at studying the structure and function of Staphylococcus aureus (S. aureus) metabolic network by complex network methods. We first generated a metabolite graph from the recently reconstructed high-quality S. aureus metabolic network model. Then, based on `bow tie' structure character, we explain and discuss the global structure of S. aureus metabolic network. The functional significance, global structural properties, modularity and centrality analysis of giant strong component in S. aureus metabolic networks are studied.

  13. 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

  14. The design of long-term effective uranium bioremediation strategy using a community metabolic model.

    Science.gov (United States)

    Zhuang, K; Ma, E; Lovley, Derek R; Mahadevan, Radhakrishnan

    2012-10-01

    Acetate amendment at uranium contaminated sites in Rifle, CO. leads to an initial bloom of Geobacter accompanied by the removal of U(VI) from the groundwater, followed by an increase of sulfate-reducing bacteria (SRBs) which are poor reducers of U(VI). One of the challenges associated with bioremediation is the decay in Geobacter abundance, which has been attributed to the depletion of bio-accessible Fe(III), motivating the investigation of simultaneous amendments of acetate and Fe(III) as an alternative bioremediation strategy. In order to understand the community metabolism of Geobacter and SRBs during artificial substrate amendment, we have created a genome-scale dynamic community model of Geobacter and SRBs using the previously described Dynamic Multi-species Metabolic Modeling framework. Optimization techniques are used to determine the optimal acetate and Fe(III) addition profile. Field-scale simulation of acetate addition accurately predicted the in situ data. The simulations suggest that batch amendment of Fe(III) along with continuous acetate addition is insufficient to promote long-term bioremediation, while continuous amendment of Fe(III) along with continuous acetate addition is sufficient to promote long-term bioremediation. By computationally minimizing the acetate and Fe(III) addition rates as well as the difference between the predicted and target uranium concentration, we showed that it is possible to maintain the uranium concentration below the environmental safety standard while minimizing the cost of chemical additions. These simulations show that simultaneous addition of acetate and Fe(III) has the potential to be an effective uranium bioremediation strategy. They also show that computational modeling of microbial community is an important tool to design effective strategies for practical applications in environmental biotechnology.

  15. Dynamic Metabolic Model Building Based on the Ensemble Modeling Approach

    Energy Technology Data Exchange (ETDEWEB)

    Liao, James C. [Univ. of California, Los Angeles, CA (United States)

    2016-10-01

    Ensemble modeling of kinetic systems addresses the challenges of kinetic model construction, with respect to parameter value selection, and still allows for the rich insights possible from kinetic models. This project aimed to show that constructing, implementing, and analyzing such models is a useful tool for the metabolic engineering toolkit, and that they can result in actionable insights from models. Key concepts are developed and deliverable publications and results are presented.

  16. The genome sequence of E. coli W (ATCC 9637: comparative genome analysis and an improved genome-scale reconstruction of E. coli

    Directory of Open Access Journals (Sweden)

    Lee Sang

    2011-01-01

    Full Text Available Abstract Background Escherichia coli is a model prokaryote, an important pathogen, and a key organism for industrial biotechnology. E. coli W (ATCC 9637, one of four strains designated as safe for laboratory purposes, has not been sequenced. E. coli W is a fast-growing strain and is the only safe strain that can utilize sucrose as a carbon source. Lifecycle analysis has demonstrated that sucrose from sugarcane is a preferred carbon source for industrial bioprocesses. Results We have sequenced and annotated the genome of E. coli W. The chromosome is 4,900,968 bp and encodes 4,764 ORFs. Two plasmids, pRK1 (102,536 bp and pRK2 (5,360 bp, are also present. W has unique features relative to other sequenced laboratory strains (K-12, B and Crooks: it has a larger genome and belongs to phylogroup B1 rather than A. W also grows on a much broader range of carbon sources than does K-12. A genome-scale reconstruction was developed and validated in order to interrogate metabolic properties. Conclusions The genome of W is more similar to commensal and pathogenic B1 strains than phylogroup A strains, and therefore has greater utility for comparative analyses with these strains. W should therefore be the strain of choice, or 'type strain' for group B1 comparative analyses. The genome annotation and tools created here are expected to allow further utilization and development of E. coli W as an industrial organism for sucrose-based bioprocesses. Refinements in our E. coli metabolic reconstruction allow it to more accurately define E. coli metabolism relative to previous models.

  17. 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

  18. Cycling Transcriptional Networks Optimize Energy Utilization on a Genome Scale.

    Science.gov (United States)

    Wang, Guang-Zhong; Hickey, Stephanie L; Shi, Lei; Huang, Hung-Chung; Nakashe, Prachi; Koike, Nobuya; Tu, Benjamin P; Takahashi, Joseph S; Konopka, Genevieve

    2015-12-01

    Genes expressing circadian RNA rhythms are enriched for metabolic pathways, but the adaptive significance of cyclic gene expression remains unclear. We estimated the genome-wide synthetic and degradative cost of transcription and translation in three organisms and found that the cost of cycling genes is strikingly higher compared to non-cycling genes. Cycling genes are expressed at high levels and constitute the most costly proteins to synthesize in the genome. We demonstrate that metabolic cycling is accelerated in yeast grown under higher nutrient flux and the number of cycling genes increases ∼40%, which are achieved by increasing the amplitude and not the mean level of gene expression. These results suggest that rhythmic gene expression optimizes the metabolic cost of global gene expression and that highly expressed genes have been selected to be downregulated in a cyclic manner for energy conservation.

  19. In Silico Strategies for Modeling Stereoselective Metabolism of Pyrethroids

    Science.gov (United States)

    In silico methods are invaluable tools to researchers seeking to understand and predict metabolic processes within PBPK models. Even though these methods have been successfully utilized to predict and quantify metabolic processes, there are many challenges involved. Stereochemica...

  20. Systems biology analysis of hepatitis C virus infection reveals the role of copy number increases in regions of chromosome 1q in hepatocellular carcinoma metabolism

    DEFF Research Database (Denmark)

    Elsemman, Ibrahim; Mardinoglu, Adil; Shoaie, Saeed;

    2016-01-01

    on hepatocellular metabolism. Here, we integrated HCV assembly reactions with a genome-scale hepatocyte metabolic model to identify metabolic targets for HCV assembly and metabolic alterations that occur between different HCV progression states (cirrhosis, dysplastic nodule, and early and advanced hepatocellular...... carcinoma (HCC)) and healthy liver tissue. We found that diacylglycerolipids were essential for HCV assembly. In addition, the metabolism of keratan sulfate and chondroitin sulfate was significantly changed in the cirrhosis stage, whereas the metabolism of acyl-carnitine was significantly changed...

  1. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

    Science.gov (United States)

    Schellenberger, Jan; Que, Richard; Fleming, Ronan M T; Thiele, Ines; Orth, Jeffrey D; Feist, Adam M; Zielinski, Daniel C; Bordbar, Aarash; Lewis, Nathan E; Rahmanian, Sorena; Kang, Joseph; Hyduke, Daniel R; Palsson, Bernhard Ø

    2011-08-04

    Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.

  2. Integrating flux balance analysis into kinetic models to decipher the dynamic metabolism of Shewanella oneidensis MR-1.

    Directory of Open Access Journals (Sweden)

    Xueyang Feng

    2012-02-01

    Full Text Available Shewanella oneidensis MR-1 sequentially utilizes lactate and its waste products (pyruvate and acetate during batch culture. To decipher MR-1 metabolism, we integrated genome-scale flux balance analysis (FBA into a multiple-substrate Monod model to perform the dynamic flux balance analysis (dFBA. The dFBA employed a static optimization approach (SOA by dividing the batch time into small intervals (i.e., ∼400 mini-FBAs, then the Monod model provided time-dependent inflow/outflow fluxes to constrain the mini-FBAs to profile the pseudo-steady-state fluxes in each time interval. The mini-FBAs used a dual-objective function (a weighted combination of "maximizing growth rate" and "minimizing overall flux" to capture trade-offs between optimal growth and minimal enzyme usage. By fitting the experimental data, a bi-level optimization of dFBA revealed that the optimal weight in the dual-objective function was time-dependent: the objective function was constant in the early growth stage, while the functional weight of minimal enzyme usage increased significantly when lactate became scarce. The dFBA profiled biologically meaningful dynamic MR-1 metabolisms: 1. the oxidative TCA cycle fluxes increased initially and then decreased in the late growth stage; 2. fluxes in the pentose phosphate pathway and gluconeogenesis were stable in the exponential growth period; and 3. the glyoxylate shunt was up-regulated when acetate became the main carbon source for MR-1 growth.

  3. Selection of objective function in genome scale flux balance analysis for process feed development in antibiotic production.

    Science.gov (United States)

    Khannapho, Chiraphan; Zhao, Hongjuan; Bonde, Bhushan K; Kierzek, Andrzej M; Avignone-Rossa, Claudio A; Bushell, Michael E

    2008-09-01

    Using flux variability analysis of a genome scale metabolic network of Streptomyces coelicolor, a series of reactions were identified, from disparate pathways that could be combined into an actinorhodin-generating mini-network. Candidate process feed nutrients that might be expected to influence this network were used in process simulations and in silico predictions compared to experimental findings. Ranking potential process feeds by flux balance analysis optimisation, using either growth or antibiotic production as objective function, did not correlate with experimental actinorhodin yields in fed processes. However, the effect of the feeds on glucose assimilation rate (using glucose uptake as objective function) ranked them in the same order as in vivo antibiotic production efficiency, consistent with results of a robustness analysis of the effect of glucose assimilation on actinorhodin production.

  4. 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...

  5. Parallel labeling experiments for pathway elucidation and (13)C metabolic flux analysis.

    Science.gov (United States)

    Antoniewicz, Maciek R

    2015-12-01

    Metabolic pathway models provide the foundation for quantitative studies of cellular physiology through the measurement of intracellular metabolic fluxes. For model organisms metabolic models are well established, with many manually curated genome-scale model reconstructions, gene knockout studies and stable-isotope tracing studies. However, for non-model organisms a similar level of knowledge is often lacking. Compartmentation of cellular metabolism in eukaryotic systems also presents significant challenges for quantitative (13)C-metabolic flux analysis ((13)C-MFA). Recently, innovative (13)C-MFA approaches have been developed based on parallel labeling experiments, the use of multiple isotopic tracers and integrated data analysis, that allow more rigorous validation of pathway models and improved quantification of metabolic fluxes. Applications of these approaches open new research directions in metabolic engineering, biotechnology and medicine.

  6. Modeling dietary influences on offspring metabolic programming in Drosophila melanogaster.

    Science.gov (United States)

    Brookheart, Rita T; Duncan, Jennifer G

    2016-09-01

    The influence of nutrition on offspring metabolism has become a hot topic in recent years owing to the growing prevalence of maternal and childhood obesity. Studies in mammals have identified several factors correlating with parental and early offspring dietary influences on progeny health; however, the molecular mechanisms that underlie these factors remain undiscovered. Mammalian metabolic tissues and pathways are heavily conserved in Drosophila melanogaster, making the fly an invaluable genetic model organism for studying metabolism. In this review, we discuss the metabolic similarities between mammals and Drosophila and present evidence supporting its use as an emerging model of metabolic programming.

  7. Metabolism

    Science.gov (United States)

    ... Surgery? Choosing the Right Sport for You Shyness Metabolism KidsHealth > For Teens > Metabolism Print A A A ... food through a process called metabolism. What Is Metabolism? Metabolism (pronounced: meh-TAB-uh-lih-zem) is ...

  8. Metabolic and Blood Pressure Effects of Walnut Supplementation in a Mouse Model of the Metabolic Syndrome

    National Research Council Canada - National Science Library

    Nicola J. A. Scott; Leigh J. Ellmers; Anna P. Pilbrow; Lotte Thomsen; Arthur Mark Richards; Chris M. Frampton; Vicky A. Cameron

    2017-01-01

    ... with the metabolic syndrome (MetS) remain uncertain. We compared a range of cardio-metabolic traits and related tissue gene expression associated with 21 weeks of dietary walnut supplementation in a mouse model of MetS (MetS-Tg) and wild-type (WT) mice (n...

  9. Alterations in cancer cell metabolism: the Warburg effect and metabolic adaptation.

    Science.gov (United States)

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

    2015-05-01

    The Warburg effect means higher glucose uptake of cancer cells compared to normal tissues, whereas a smaller fraction of this glucose is employed for oxidative phosphorylation. With the advent of high throughput technologies and computational systems biology, cancer cell metabolism has been reinvestigated over the last decades toward identifying various events underlying "how" and "why" a cancer cell employs aerobic glycolysis. Significant progress has been shaped to revise the Warburg effect. In this study, we have integrated the gene expression of 13 different cancer cells with the genome-scale metabolic network of human (Recon1) based on the E-Flux method, and analyzed them based on constraint-based modeling. Results show that regardless of significant up- and down-regulated metabolic genes, the distribution of metabolic changes is similar in different cancer types. These findings support the theory that the Warburg effect is a consequence of metabolic adaptation in cancer cells. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. Optimization based automated curation of metabolic reconstructions

    Directory of Open Access Journals (Sweden)

    Maranas Costas D

    2007-06-01

    Full Text Available Abstract Background Currently, there exists tens of different microbial and eukaryotic metabolic reconstructions (e.g., Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis with many more under development. All of these reconstructions are inherently incomplete with some functionalities missing due to the lack of experimental and/or homology information. A key challenge in the automated generation of genome-scale reconstructions is the elucidation of these gaps and the subsequent generation of hypotheses to bridge them. Results In this work, an optimization based procedure is proposed to identify and eliminate network gaps in these reconstructions. First we identify the metabolites in the metabolic network reconstruction which cannot be produced under any uptake conditions and subsequently we identify the reactions from a customized multi-organism database that restores the connectivity of these metabolites to the parent network using four mechanisms. This connectivity restoration is hypothesized to take place through four mechanisms: a reversing the directionality of one or more reactions in the existing model, b adding reaction from another organism to provide functionality absent in the existing model, c adding external transport mechanisms to allow for importation of metabolites in the existing model and d restore flow by adding intracellular transport reactions in multi-compartment models. We demonstrate this procedure for the genome- scale reconstruction of Escherichia coli and also Saccharomyces cerevisiae wherein compartmentalization of intra-cellular reactions results in a more complex topology of the metabolic network. We determine that about 10% of metabolites in E. coli and 30% of metabolites in S. cerevisiae cannot carry any flux. Interestingly, the dominant flow restoration mechanism is directionality reversals of existing reactions in the respective models. Conclusion We have proposed systematic methods to identify and

  11. 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...

  12. A versatile genome-scale PCR-based pipeline for high-definition DNA FISH

    NARCIS (Netherlands)

    Bienko, M.; Crosetto, N.; Teytelman, L.; Klemm, S.; Itzkovitz, S.; van Oudenaarden, A.

    2013-01-01

    We developed a cost-effective genome-scale PCR-based method for high-definition DNA FISH (HD-FISH). We visualized gene loci with diffraction-limited resolution, chromosomes as spot clusters and single genes together with transcripts by combining HD-FISH with single-molecule RNA FISH. We provide a da

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

    Science.gov (United States)

    Cottret, Ludovic; Jourdan, Fabien

    2010-08-01

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

  14. 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.

  15. Tools for metabolic engineering in Streptomyces.

    Science.gov (United States)

    Bekker, Valerie; Dodd, Amanda; Brady, Dean; Rumbold, Karl

    2014-01-01

    During the last few decades, Streptomycetes have shown to be a very important and adaptable group of bacteria for the production of various beneficial secondary metabolites. These secondary metabolites have been of great interest in academia and the pharmaceutical industries. To date, a vast variety of techniques and tools for metabolic engineering of relevant structural biosynthetic gene clusters have been developed. The main aim of this review is to summarize and discuss the published literature on tools for metabolic engineering of Streptomyces over the last decade. These strategies involve precursor engineering, structural and regulatory gene engineering, and the up or downregulation of genes, as well as genome shuffling and the use of genome scale metabolic models, which can reconstruct bacterial metabolic pathways to predict phenotypic changes and hence rationalize engineering strategies. These tools are continuously being developed to simplify the engineering strategies for this vital group of bacteria.

  16. Tools for metabolic engineering in Streptomyces

    Science.gov (United States)

    Bekker, Valerie; Dodd, Amanda; Brady, Dean; Rumbold, Karl

    2014-01-01

    During the last few decades, Streptomycetes have shown to be a very important and adaptable group of bacteria for the production of various beneficial secondary metabolites. These secondary metabolites have been of great interest in academia and the pharmaceutical industries. To date, a vast variety of techniques and tools for metabolic engineering of relevant structural biosynthetic gene clusters have been developed. The main aim of this review is to summarize and discuss the published literature on tools for metabolic engineering of Streptomyces over the last decade. These strategies involve precursor engineering, structural and regulatory gene engineering, and the up or downregulation of genes, as well as genome shuffling and the use of genome scale metabolic models, which can reconstruct bacterial metabolic pathways to predict phenotypic changes and hence rationalize engineering strategies. These tools are continuously being developed to simplify the engineering strategies for this vital group of bacteria. PMID:25482230

  17. 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.

  18. Dissecting Germ Cell Metabolism through Network Modeling.

    Directory of Open Access Journals (Sweden)

    Leanne S Whitmore

    Full Text Available Metabolic pathways are increasingly postulated to be vital in programming cell fate, including stemness, differentiation, proliferation, and apoptosis. The commitment to meiosis is a critical fate decision for mammalian germ cells, and requires a metabolic derivative of vitamin A, retinoic acid (RA. Recent evidence showed that a pulse of RA is generated in the testis of male mice thereby triggering meiotic commitment. However, enzymes and reactions that regulate this RA pulse have yet to be identified. We developed a mouse germ cell-specific metabolic network with a curated vitamin A pathway. Using this network, we implemented flux balance analysis throughout the initial wave of spermatogenesis to elucidate important reactions and enzymes for the generation and degradation of RA. Our results indicate that primary RA sources in the germ cell include RA import from the extracellular region, release of RA from binding proteins, and metabolism of retinal to RA. Further, in silico knockouts of genes and reactions in the vitamin A pathway predict that deletion of Lipe, hormone-sensitive lipase, disrupts the RA pulse thereby causing spermatogenic defects. Examination of other metabolic pathways reveals that the citric acid cycle is the most active pathway. In addition, we discover that fatty acid synthesis/oxidation are the primary energy sources in the germ cell. In summary, this study predicts enzymes, reactions, and pathways important for germ cell commitment to meiosis. These findings enhance our understanding of the metabolic control of germ cell differentiation and will help guide future experiments to improve reproductive health.

  19. Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network.

    Science.gov (United States)

    Pacheco, Maria Pires; John, Elisabeth; Kaoma, Tony; Heinäniemi, Merja; Nicot, Nathalie; Vallar, Laurent; Bueb, Jean-Luc; Sinkkonen, Lasse; Sauter, Thomas

    2015-10-19

    The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied. Here we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks. To understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism. By integrating metabolic modelling and epigenomic analysis we have identified high regulatory load as a common feature of metabolic

  20. Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction

    Directory of Open Access Journals (Sweden)

    Palsson Bernhard

    2009-04-01

    Full Text Available Abstract Background Infections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction. Results We have developed and validated a genome-scale metabolic network of Salmonella typhimurium LT2 (iRR1083. This model accounts for 1,083 genes that encode proteins catalyzing 1,087 unique metabolic and transport reactions in the bacterium. We employed flux balance analysis and in silico gene essentiality analysis to investigate growth under a wide range of conditions that mimic in vitro and host cell environments. Gene expression profiling of S. typhimurium isolated from macrophage cell lines was used to constrain the model to predict metabolic pathways that are likely to be operational during infection. Conclusion Our analysis suggests that there is a robust minimal set of metabolic pathways that is required for successful replication of Salmonella inside the host cell. This model also serves as platform for the integration of high-throughput data. Its computational power allows identification of networked metabolic pathways and generation of hypotheses about metabolism during infection, which might be used for the rational design of novel antibiotics or vaccine strains.

  1. 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.

  2. Optimization of lysine metabolism in Corynebacterium glutamicum

    DEFF Research Database (Denmark)

    Rytter, Jakob Vang

    the project intends to eliminate. PGI catalyzes the conversion of alpha-D-glucose-6-phosphate to fructose-6-phosphate just downstream of the branch in the glycolysis, but it also catalyzes the reverse reaction. It is unknown whether up- or down-regulation of the pgi is required to increase the flux through......, and increased NADPH availability is therefore a potential way to enhance lysine production. The generation of NADPH is mainly located in the pentose phosphate pathway (PPP). Using the genome scale model the phosphoglucoisomerase enzyme (PGI) has been identified as a possible bottleneck in the metabolism, which...

  3. A minimal model of metabolism-based chemotaxis.

    Directory of Open Access Journals (Sweden)

    Matthew D Egbert

    Full Text Available Since the pioneering work by Julius Adler in the 1960's, bacterial chemotaxis has been predominantly studied as metabolism-independent. All available simulation models of bacterial chemotaxis endorse this assumption. Recent studies have shown, however, that many metabolism-dependent chemotactic patterns occur in bacteria. We hereby present the simplest artificial protocell model capable of performing metabolism-based chemotaxis. The model serves as a proof of concept to show how even the simplest metabolism can sustain chemotactic patterns of varying sophistication. It also reproduces a set of phenomena that have recently attracted attention on bacterial chemotaxis and provides insights about alternative mechanisms that could instantiate them. We conclude that relaxing the metabolism-independent assumption provides important theoretical advances, forces us to rethink some established pre-conceptions and may help us better understand unexplored and poorly understood aspects of bacterial chemotaxis.

  4. First mass spectrometry metabolic fingerprinting of bacterial metabolism in a model cheese.

    Science.gov (United States)

    Le Boucher, C; Courant, F; Jeanson, S; Chereau, S; Maillard, M-B; Royer, A-L; Thierry, A; Dervilly-Pinel, G; Le Bizec, B; Lortal, S

    2013-11-15

    Metabolic fingerprinting is an untargeted approach which has not yet been undertaken to investigate cheese. This study is a proof of concept, concerning the ability of mass spectrometry (MS) metabolic fingerprinting to investigate modifications induced by bacterial metabolism in cheese over time. An ultrafiltrated milk concentrate was used to manufacture model cheeses inoculated with Lactococcus lactis LD61. Metabolic fingerprints were acquired after 0, 8 and 48h from two different fractions of the metabolome: the water-soluble fraction using liquid chromatography-high resolution-MS and a volatile fraction using gas chromatography-MS. Metabolic fingerprints differed significantly over time. Forty-five metabolites were identified, including well-known cheese metabolites, such as 12 amino acids and 25 volatile metabolites, and less studied ones, such as four vitamins, uric acid, creatine and l-carnitine. These results showed the relevance of cheese MS fingerprinting to generate new findings and to detect even slight differences between two conditions.

  5. Parallel labeling experiments validate Clostridium acetobutylicum metabolic network model for (13)C metabolic flux analysis.

    Science.gov (United States)

    Au, Jennifer; Choi, Jungik; Jones, Shawn W; Venkataramanan, Keerthi P; Antoniewicz, Maciek R

    2014-11-01

    In this work, we provide new insights into the metabolism of Clostridium acetobutylicum ATCC 824 obtained using a systematic approach for quantifying fluxes based on parallel labeling experiments and (13)C-metabolic flux analysis ((13)C-MFA). Here, cells were grown in parallel cultures with [1-(13)C]glucose and [U-(13)C]glucose as tracers and (13)C-MFA was used to quantify intracellular metabolic fluxes. Several metabolic network models were compared: an initial model based on current knowledge, and extended network models that included additional reactions that improved the fits of experimental data. While the initial network model did not produce a statistically acceptable fit of (13)C-labeling data, an extended network model with five additional reactions was able to fit all data with 292 redundant measurements. The model was subsequently trimmed to produce a minimal network model of C. acetobutylicum for (13)C-MFA, which could still reproduce all of the experimental data. The flux results provided valuable new insights into the metabolism of C. acetobutylicum. First, we found that TCA cycle was effectively incomplete, as there was no measurable flux between α-ketoglutarate and succinyl-CoA, succinate and fumarate, and malate and oxaloacetate. Second, an active pathway was identified from pyruvate to fumarate via aspartate. Third, we found that isoleucine was produced exclusively through the citramalate synthase pathway in C. acetobutylicum and that CAC3174 was likely responsible for citramalate synthase activity. These model predictions were confirmed in several follow-up tracer experiments. The validated metabolic network model established in this study can be used in future investigations for unbiased (13)C-flux measurements in C. acetobutylicum. Copyright © 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  6. Monte-Carlo modeling of the central carbon metabolism of Lactococcus lactis: insights into metabolic regulation.

    Science.gov (United States)

    Murabito, Ettore; Verma, Malkhey; Bekker, Martijn; Bellomo, Domenico; Westerhoff, Hans V; Teusink, Bas; Steuer, Ralf

    2014-01-01

    Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models.

  7. Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO2 Levels.

    Science.gov (United States)

    Levering, Jennifer; Dupont, Christopher L; Allen, Andrew E; Palsson, Bernhard O; Zengler, Karsten

    2017-01-01

    Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean's primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom's metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and shared metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum's response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. IMPORTANCE Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum's metabolism.

  8. 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.

  9. Evaluating computational models of cholesterol metabolism

    NARCIS (Netherlands)

    Paalvast, Yared; Kuivenhoven, Jan Albert; Groen, Albert K.

    2015-01-01

    Regulation of cholesterol homeostasis has been studied extensively during the last decades. Many of the metabolic pathways involved have been discovered. Yet important gaps in our knowledge remain. For example, knowledge on intracellular cholesterol traffic and its relation to the regulation of chol

  10. Developing 'integrative' zebrafish models of behavioral and metabolic disorders.

    Science.gov (United States)

    Nguyen, Michael; Yang, Ester; Neelkantan, Nikhil; Mikhaylova, Alina; Arnold, Raymond; Poudel, Manoj K; Stewart, Adam Michael; Kalueff, Allan V

    2013-11-01

    Recently, the pathophysiological overlap between metabolic and mental disorders has received increased recognition. Zebrafish (Danio rerio) are rapidly becoming a popular model organism for translational biomedical research due to their genetic tractability, low cost, quick reproductive cycle, and ease of behavioral, pharmacological or genetic manipulation. High homology to mammalian physiology and the availability of well-developed assays also make the zebrafish an attractive organism for studying human disorders. Zebrafish neurobehavioral and endocrine phenotypes show promise for the use of zebrafish in studies of stress, obesity and related behavioral and metabolic disorders. Here, we discuss the parallels between zebrafish and other model species in stress and obesity physiology, as well as outline the available zebrafish models of weight gain, metabolic deficits, feeding, stress, anxiety and related behavioral disorders. Overall, zebrafish demonstrate a strong potential for modeling human behavioral and metabolic disorders, and their comorbidity.

  11. A workflow for mathematical modeling of subcellular metabolic pathways in leaf metabolism of Arabidopsis thaliana

    Directory of Open Access Journals (Sweden)

    Thomas eNägele

    2013-12-01

    Full Text Available During the last decade genome sequencing has experienced a rapid technological development resulting in numerous sequencing projects and applications in life science. In plant molecular biology, the availability of sequence data on whole genomes has enabled the reconstruction of metabolic networks. Enzymatic reactions are predicted by the sequence information. Pathways arise due to the participation of chemical compounds as substrates and products in these reactions. Although several of these comprehensive networks have been reconstructed for the genetic model plant Arabidopsis thaliana, the integration of experimental data is still challenging. Particularly the analysis of subcellular organization of plant cells limits the understanding of regulatory instances in these metabolic networks in vivo. In this study, we develop an approach for the functional integration of experimental high-throughput data into such large-scale networks. We present a subcellular metabolic network model comprising 524 metabolic intermediates and 548 metabolic interactions derived from a total of 2769 reactions. We demonstrate how to link the metabolite covariance matrix of different Arabidopsis thaliana accessions with the subcellular metabolic network model for the inverse calculation of the biochemical Jacobian, finally resulting in the calculation of a matrix which satisfies a Lyaponov equation involving a covariance matrix. In this way, differential strategies of metabolite compartmentation and involved reactions were identified in the accessions when exposed to low temperature.

  12. A versatile genome-scale PCR-based pipeline for high-definition DNA FISH.

    Science.gov (United States)

    Bienko, Magda; Crosetto, Nicola; Teytelman, Leonid; Klemm, Sandy; Itzkovitz, Shalev; van Oudenaarden, Alexander

    2013-02-01

    We developed a cost-effective genome-scale PCR-based method for high-definition DNA FISH (HD-FISH). We visualized gene loci with diffraction-limited resolution, chromosomes as spot clusters and single genes together with transcripts by combining HD-FISH with single-molecule RNA FISH. We provide a database of over 4.3 million primer pairs targeting the human and mouse genomes that is readily usable for rapid and flexible generation of probes.

  13. Effect of substrate competition in kinetic models of metabolic networks.

    Science.gov (United States)

    Schäuble, Sascha; Stavrum, Anne Kristin; Puntervoll, Pål; Schuster, Stefan; Heiland, Ines

    2013-09-02

    Substrate competition can be found in many types of biological processes, ranging from gene expression to signal transduction and metabolic pathways. Although several experimental and in silico studies have shown the impact of substrate competition on these processes, it is still often neglected, especially in modelling approaches. Using toy models that exemplify different metabolic pathway scenarios, we show that substrate competition can influence the dynamics and the steady state concentrations of a metabolic pathway. We have additionally derived rate laws for substrate competition in reversible reactions and summarise existing rate laws for substrate competition in irreversible reactions.

  14. BioMet Toolbox: genome-wide analysis of metabolism

    DEFF Research Database (Denmark)

    Cvijovic, M.; Olivares Hernandez, Roberto; Agren, R.

    2010-01-01

    models. Systematic analysis of biological processes by means of modelling and simulations has made the identification of metabolic networks and prediction of metabolic capabilities under different conditions possible. For facilitating such systemic analysis, we have developed the BioMet Toolbox, a web......-based resource for stoichiometric analysis and for integration of transcriptome and interactome data, thereby exploiting the capabilities of genome-scale metabolic models. The BioMet Toolbox provides an effective user-friendly way to perform linear programming simulations towards maximized or minimized growth...... rates, substrate uptake rates and metabolic production rates by detecting relevant fluxes, simulate single and double gene deletions or detect metabolites around which major transcriptional changes are concentrated. These tools can be used for high-throughput in silico screening and allows fully...

  15. Weighted Statistical Binning: Enabling Statistically Consistent Genome-Scale Phylogenetic Analyses

    Science.gov (United States)

    Bayzid, Md Shamsuzzoha; Mirarab, Siavash; Boussau, Bastien; Warnow, Tandy

    2015-01-01

    Because biological processes can result in different loci having different evolutionary histories, species tree estimation requires multiple loci from across multiple genomes. While many processes can result in discord between gene trees and species trees, incomplete lineage sorting (ILS), modeled by the multi-species coalescent, is considered to be a dominant cause for gene tree heterogeneity. Coalescent-based methods have been developed to estimate species trees, many of which operate by combining estimated gene trees, and so are called "summary methods". Because summary methods are generally fast (and much faster than more complicated coalescent-based methods that co-estimate gene trees and species trees), they have become very popular techniques for estimating species trees from multiple loci. However, recent studies have established that summary methods can have reduced accuracy in the presence of gene tree estimation error, and also that many biological datasets have substantial gene tree estimation error, so that summary methods may not be highly accurate in biologically realistic conditions. Mirarab et al. (Science 2014) presented the "statistical binning" technique to improve gene tree estimation in multi-locus analyses, and showed that it improved the accuracy of MP-EST, one of the most popular coalescent-based summary methods. Statistical binning, which uses a simple heuristic to evaluate "combinability" and then uses the larger sets of genes to re-calculate gene trees, has good empirical performance, but using statistical binning within a phylogenomic pipeline does not have the desirable property of being statistically consistent. We show that weighting the re-calculated gene trees by the bin sizes makes statistical binning statistically consistent under the multispecies coalescent, and maintains the good empirical performance. Thus, "weighted statistical binning" enables highly accurate genome-scale species tree estimation, and is also statistically

  16. Weighted Statistical Binning: Enabling Statistically Consistent Genome-Scale Phylogenetic Analyses.

    Directory of Open Access Journals (Sweden)

    Md Shamsuzzoha Bayzid

    Full Text Available Because biological processes can result in different loci having different evolutionary histories, species tree estimation requires multiple loci from across multiple genomes. While many processes can result in discord between gene trees and species trees, incomplete lineage sorting (ILS, modeled by the multi-species coalescent, is considered to be a dominant cause for gene tree heterogeneity. Coalescent-based methods have been developed to estimate species trees, many of which operate by combining estimated gene trees, and so are called "summary methods". Because summary methods are generally fast (and much faster than more complicated coalescent-based methods that co-estimate gene trees and species trees, they have become very popular techniques for estimating species trees from multiple loci. However, recent studies have established that summary methods can have reduced accuracy in the presence of gene tree estimation error, and also that many biological datasets have substantial gene tree estimation error, so that summary methods may not be highly accurate in biologically realistic conditions. Mirarab et al. (Science 2014 presented the "statistical binning" technique to improve gene tree estimation in multi-locus analyses, and showed that it improved the accuracy of MP-EST, one of the most popular coalescent-based summary methods. Statistical binning, which uses a simple heuristic to evaluate "combinability" and then uses the larger sets of genes to re-calculate gene trees, has good empirical performance, but using statistical binning within a phylogenomic pipeline does not have the desirable property of being statistically consistent. We show that weighting the re-calculated gene trees by the bin sizes makes statistical binning statistically consistent under the multispecies coalescent, and maintains the good empirical performance. Thus, "weighted statistical binning" enables highly accurate genome-scale species tree estimation, and is also

  17. Modelling microbial metabolic rewiring during growth in a complex medium.

    Science.gov (United States)

    Fondi, Marco; Bosi, Emanuele; Presta, Luana; Natoli, Diletta; Fani, Renato

    2016-11-24

    In their natural environment, bacteria face a wide range of environmental conditions that change over time and that impose continuous rearrangements at all the cellular levels (e.g. gene expression, metabolism). When facing a nutritionally rich environment, for example, microbes first use the preferred compound(s) and only later start metabolizing the other one(s). A systemic re-organization of the overall microbial metabolic network in response to a variation in the composition/concentration of the surrounding nutrients has been suggested, although the range and the entity of such modifications in organisms other than a few model microbes has been scarcely described up to now. We used multi-step constraint-based metabolic modelling to simulate the growth in a complex medium over several time steps of the Antarctic model organism Pseudoalteromonas haloplanktis TAC125. As each of these phases is characterized by a specific set of amino acids to be used as carbon and energy source our modelling framework describes the major consequences of nutrients switching at the system level. The model predicts that a deep metabolic reprogramming might be required to achieve optimal biomass production in different stages of growth (different medium composition), with at least half of the cellular metabolic network involved (more than 50% of the metabolic genes). Additionally, we show that our modelling framework is able to capture metabolic functional association and/or common regulatory features of the genes embedded in our reconstruction (e.g. the presence of common regulatory motifs). Finally, to explore the possibility of a sub-optimal biomass objective function (i.e. that cells use resources in alternative metabolic processes at the expense of optimal growth) we have implemented a MOMA-based approach (called nutritional-MOMA) and compared the outcomes with those obtained with Flux Balance Analysis (FBA). Growth simulations under this scenario revealed the deep impact of

  18. Concepts, Challenges and Successes in Modeling Thermodynamics of Metabolism

    Directory of Open Access Journals (Sweden)

    William R. Cannon

    2014-11-01

    Full Text Available 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, flux-based approaches have been the methods of choice because they do not require the use of the rate constants of the law of mass action. However, this convenience also limits the predictive power of flux-based approaches in that the law of mass action is not used directly, making it very 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.

  19. Genome-scale transcriptome analysis in response to nitric oxide in birch cells: implications of the triterpene biosynthetic pathway.

    Directory of Open Access Journals (Sweden)

    Fansuo Zeng

    Full Text Available Evidence supporting nitric oxide (NO as a mediator of plant biochemistry continues to grow, but its functions at the molecular level remains poorly understood and, in some cases, controversial. To study the role of NO at the transcriptional level in Betula platyphylla cells, we conducted a genome-scale transcriptome analysis of these cells. The transcriptome of untreated birch cells and those treated by sodium nitroprusside (SNP were analyzed using the Solexa sequencing. Data were collected by sequencing cDNA libraries of birch cells, which had a long period to adapt to the suspension culture conditions before SNP-treated cells and untreated cells were sampled. Among the 34,100 UniGenes detected, BLASTX search revealed that 20,631 genes showed significant (E-values≤10-5 sequence similarity with proteins from the NR-database. Numerous expressed sequence tags (i.e., 1374 were identified as differentially expressed between the 12 h SNP-treated cells and control cells samples: 403 up-regulated and 971 down-regulated. From this, we specifically examined a core set of NO-related transcripts. The altered expression levels of several transcripts, as determined by transcriptome analysis, was confirmed by qRT-PCR. The results of transcriptome analysis, gene expression quantification, the content of triterpenoid and activities of defensive enzymes elucidated NO has a significant effect on many processes including triterpenoid production, carbohydrate metabolism and cell wall biosynthesis.

  20. Exploiting the pathway structure of metabolism to reveal high-order epistasis

    Directory of Open Access Journals (Sweden)

    Imielinski Marcin

    2008-04-01

    Full Text Available Abstract Background Biological robustness results from redundant pathways that achieve an essential objective, e.g. the production of biomass. As a consequence, the biological roles of many genes can only be revealed through multiple knockouts that identify a set of genes as essential for a given function. The identification of such "epistatic" essential relationships between network components is critical for the understanding and eventual manipulation of robust systems-level phenotypes. Results We introduce and apply a network-based approach for genome-scale metabolic knockout design. We apply this method to uncover over 11,000 minimal knockouts for biomass production in an in silico genome-scale model of E. coli. A large majority of these "essential sets" contain 5 or more reactions, and thus represent complex epistatic relationships between components of the E. coli metabolic network. Conclusion The complex minimal biomass knockouts discovered with our approach illuminate robust essential systems-level roles for reactions in the E. coli metabolic network. Unlike previous approaches, our method yields results regarding high-order epistatic relationships and is applicable at the genome-scale.

  1. Genome –Scale Reconstruction of Metabolic Networks of Lactobacillus casei ATCC 334 and 12A

    Science.gov (United States)

    Vinay-Lara, Elena; Hamilton, Joshua J.; Stahl, Buffy; Broadbent, Jeff R.; Reed, Jennifer L.; Steele, James L.

    2014-01-01

    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. PMID:25365062

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

    DEFF Research Database (Denmark)

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

    2015-01-01

    by inherent inconsistencies and gaps. RESULTS: Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass......MOTIVATION: Genome-scale metabolic network reconstructions have been established as a powerful tool for the prediction of cellular phenotypes and metabolic capabilities of organisms. In recent years, the number of network reconstructions has been constantly increasing, mostly because...... of the availability of novel (semi-)automated procedures, which enabled the reconstruction of metabolic models based on individual genomes and their annotation. The resulting models are widely used in numerous applications. However, the accuracy and predictive power of network reconstructions are commonly limited...

  3. Flux analysis and metabolomics for systematic metabolic engineering of microorganisms.

    Science.gov (United States)

    Toya, Yoshihiro; Shimizu, Hiroshi

    2013-11-01

    Rational engineering of metabolism is important for bio-production using microorganisms. Metabolic design based on in silico simulations and experimental validation of the metabolic state in the engineered strain helps in accomplishing systematic metabolic engineering. Flux balance analysis (FBA) is a method for the prediction of metabolic phenotype, and many applications have been developed using FBA to design metabolic networks. Elementary mode analysis (EMA) and ensemble modeling techniques are also useful tools for in silico strain design. The metabolome and flux distribution of the metabolic pathways enable us to evaluate the metabolic state and provide useful clues to improve target productivity. Here, we reviewed several computational applications for metabolic engineering by using genome-scale metabolic models of microorganisms. We also discussed the recent progress made in the field of metabolomics and (13)C-metabolic flux analysis techniques, and reviewed these applications pertaining to bio-production development. Because these in silico or experimental approaches have their respective advantages and disadvantages, the combined usage of these methods is complementary and effective for metabolic engineering.

  4. Swimming in light: a large-scale computational analysis of the metabolism of Dinoroseobacter shibae.

    Directory of Open Access Journals (Sweden)

    Rene Rex

    Full Text Available The Roseobacter clade is a ubiquitous group of marine α-proteobacteria. To gain insight into the versatile metabolism of this clade, we took a constraint-based approach and created a genome-scale metabolic model (iDsh827 of Dinoroseobacter shibae DFL12T. Our model is the first accounting for the energy demand of motility, the light-driven ATP generation and experimentally determined specific biomass composition. To cover a large variety of environmental conditions, as well as plasmid and single gene knock-out mutants, we simulated 391,560 different physiological states using flux balance analysis. We analyzed our results with regard to energy metabolism, validated them experimentally, and revealed a pronounced metabolic response to the availability of light. Furthermore, we introduced the energy demand of motility as an important parameter in genome-scale metabolic models. The results of our simulations also gave insight into the changing usage of the two degradation routes for dimethylsulfoniopropionate, an abundant compound in the ocean. A side product of dimethylsulfoniopropionate degradation is dimethyl sulfide, which seeds cloud formation and thus enhances the reflection of sunlight. By our exhaustive simulations, we were able to identify single-gene knock-out mutants, which show an increased production of dimethyl sulfide. In addition to the single-gene knock-out simulations we studied the effect of plasmid loss on the metabolism. Moreover, we explored the possible use of a functioning phosphofructokinase for D. shibae.

  5. Mathematical modeling of isotope labeling experiments for metabolic flux analysis.

    Science.gov (United States)

    Nargund, Shilpa; Sriram, Ganesh

    2014-01-01

    Isotope labeling experiments (ILEs) offer a powerful methodology to perform metabolic flux analysis. However, the task of interpreting data from these experiments to evaluate flux values requires significant mathematical modeling skills. Toward this, this chapter provides background information and examples to enable the reader to (1) model metabolic networks, (2) simulate ILEs, and (3) understand the optimization and statistical methods commonly used for flux evaluation. A compartmentalized model of plant glycolysis and pentose phosphate pathway illustrates the reconstruction of a typical metabolic network, whereas a simpler example network illustrates the underlying metabolite and isotopomer balancing techniques. We also discuss the salient features of commonly used flux estimation software 13CFLUX2, Metran, NMR2Flux+, FiatFlux, and OpenFLUX. Furthermore, we briefly discuss methods to improve flux estimates. A graphical checklist at the end of the chapter provides a reader a quick reference to the mathematical modeling concepts and resources.

  6. Kinetic modelling of central carbon metabolism in Escherichia coli.

    Science.gov (United States)

    Peskov, Kirill; Mogilevskaya, Ekaterina; Demin, Oleg

    2012-09-01

    In the present study, we developed a detailed kinetic model of Escherichia coli central carbon metabolism. The main model assumptions were based on the results of metabolic and regulatory reconstruction of the system and thorough model verification with experimental data. The development and verification of the model included several stages, which allowed us to take into account both in vitro and in vivo experimental data and avoid the ambiguity that frequently occurs in detailed models of biochemical pathways. The choice of the level of detail for the mathematical description of enzymatic reaction rates and the evaluation of parameter values were based on available published data. Validation of the complete model of the metabolic pathway describing specific physiological states was based on fluxomics and metabolomics data. In particular, we developed a model that describes aerobic growth of E. coli in continuous culture with a limiting concentration of glucose. Such modification of the model was used to integrate experimental metabolomics data obtained in steady-state conditions for wild-type E. coli and genetically modified strains, e.g. knockout of the pyruvate kinase gene (pykA). Following analysis of the model behaviour, and comparison of the coincidence between predicted and experimental data, it was possible to investigate the functional and regulatory properties of E. coli central carbon metabolism. For example, a novel metabolic regulatory mechanism for 6-phosphogluconate dehydrogenase inhibition by phosphoenolpyruvate was hypothesized, and the flux ratios between the reactions catalysed by enzyme isoforms were predicted. The mathematical model described here has been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/database/peskov/index.html © 2012 The Authors Journal compilation © 2012 FEBS.

  7. Systems approach to characterize the metabolism of liver cancer stem cells expressing CD133

    Science.gov (United States)

    Hur, Wonhee; Ryu, Jae Yong; Kim, Hyun Uk; Hong, Sung Woo; Lee, Eun Byul; Lee, Sang Yup; Yoon, Seung Kew

    2017-04-01

    Liver cancer stem cells (LCSCs) have attracted attention because they cause therapeutic resistance in hepatocellular carcinoma (HCC). Understanding the metabolism of LCSCs can be a key to developing therapeutic strategy, but metabolic characteristics have not yet been studied. Here, we systematically analyzed and compared the global metabolic phenotype between LCSCs and non-LCSCs using transcriptome and metabolome data. We also reconstructed genome-scale metabolic models (GEMs) for LCSC and non-LCSC to comparatively examine differences in their metabolism at genome-scale. We demonstrated that LCSCs exhibited an increased proliferation rate through enhancing glycolysis compared with non-LCSCs. We also confirmed that MYC, a central point of regulation in cancer metabolism, was significantly up-regulated in LCSCs compared with non-LCSCs. Moreover, LCSCs tend to have less active fatty acid oxidation. In this study, the metabolic characteristics of LCSCs were identified using integrative systems analysis, and these characteristics could be potential cures for the resistance of liver cancer cells to anticancer treatments.

  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. 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.

  10. 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

    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...... using Arabidopsis NimbleGen ATH6 microarrays. In total 6061 transcripts were significantly cold regulated (p ... be crucial for their local geographic adaptation to cold temperature. Additionally, since the approach presented here is general, it could be adapted to study networks regulating biological process in any biological systems....

  11. Metabolic modeling with Big Data and the gut microbiome.

    Science.gov (United States)

    Sung, Jaeyun; Hale, Vanessa; Merkel, Annette C; Kim, Pan-Jun; Chia, Nicholas

    2016-09-01

    The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework - or metabolic model - on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.

  12. The evolution of metabolic networks of E. coli

    Directory of Open Access Journals (Sweden)

    Baumler David J

    2011-11-01

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

  13. Impulsive mathematical modeling of ascorbic acid metabolism in healthy subjects.

    Science.gov (United States)

    Bachar, Mostafa; Raimann, Jochen G; Kotanko, Peter

    2016-03-07

    In this work, we develop an impulsive mathematical model of Vitamin C (ascorbic acid) metabolism in healthy subjects for daily intake over a long period of time. The model includes the dynamics of ascorbic acid plasma concentration, the ascorbic acid absorption in the intestines and a novel approach to quantify the glomerular excretion of ascorbic acid. We investigate qualitative and quantitative dynamics. We show the existence and uniqueness of the global asymptotic stability of the periodic solution. We also perform a numerical simulation for the entire time period based on published data reporting parameters reflecting ascorbic acid metabolism at different oral doses of ascorbic acid.

  14. Metabolism

    Science.gov (United States)

    ... Are More Common in People With Type 1 Diabetes Metabolic Syndrome Your Child's Weight Healthy Eating Endocrine System Blood Test: Basic Metabolic Panel (BMP) Activity: Endocrine System Growth Disorders Diabetes Center Thyroid Disorders Your Endocrine System Movie: Endocrine ...

  15. Metabolic alterations in experimental models of depression

    Directory of Open Access Journals (Sweden)

    Maria G. Puiu

    2016-10-01

    Full Text Available Introduction: Major depressive disorder is one of the most prevalent psychiatric disorders and is associated with a severe impact on the personal functioning, thus with incurring significant direct and indirect costs. The presence of depression in patients with medical comorbidities increases the risks of myocardial infarction and decreases diabetes control, and adherence to treatment. The mechanism through which these effects are produced is still uncertain. Objectives of this study were to evaluate the metabolic alterations in female Wistar rats with induced depression, with and without administration of Agomelatine. The methods included two experiments. All data were analyzed by comparison with group I (control, and with each other. In the first experiment we induced depression by: exposure to chronic mild stress-group II; olfactory bulbectomy-group III; and exposure to chronic mild stress and hyperlipidic/ hyper caloric dietgroup IV. The second experiment was similar with the first but the rats received Agomelatine (0.16mg/ animal: group V (depression induced through exposure to chronic mild stress, VI (depression induced through olfactory bulbectomy and VII (depression induced through exposure to chronic mild stressing hyperlipidic/ hypercaloric diet. Weight, cholesterol, triglycerides and glycaemia were measured at day 0 and 28, and leptin value was measured at day 28. The results in the 1st experiment revealed significant differences (p<0.01 for weight and cholesterol in Group IV, for triglycerides in groups III and IV (p<0.001, and for glycaemia in group II. The 2nd experiment revealed significant differences (p<0.001 in group VII for weight and triglycerides, and in groups V and VI for triglycerides (p<0.01. In conclusion, significant correlations were found between high level of triglycerides and depression induced by chronic stress and olfactory bulbectomy. Agomelatine groups had a lower increase of triglycerides levels.

  16. Metabolism

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    2008255 Serum adiponectin level declines in the elderly with metabolic syndrome.WU Xiaoyan(吴晓琰),et al.Dept Geriatr,Huashan Hosp,Fudan UnivShanghai200040.Chin J Geriatr2008;27(3):164-167.Objective To investigate the correlation between ser-um adiponectin level and metabolic syndrome in the elderly·Methods Sixty-one subjects with metabolic syndrome and140age matched subjects without metabolic

  17. Improving Metabolic Pathway Efficiency by Statistical Model-Based Multivariate Regulatory Metabolic Engineering.

    Science.gov (United States)

    Xu, Peng; Rizzoni, Elizabeth Anne; Sul, Se-Yeong; Stephanopoulos, Gregory

    2017-01-20

    Metabolic engineering entails target modification of cell metabolism to maximize the production of a specific compound. For empowering combinatorial optimization in strain engineering, tools and algorithms are needed to efficiently sample the multidimensional gene expression space and locate the desirable overproduction phenotype. We addressed this challenge by employing design of experiment (DoE) models to quantitatively correlate gene expression with strain performance. By fractionally sampling the gene expression landscape, we statistically screened the dominant enzyme targets that determine metabolic pathway efficiency. An empirical quadratic regression model was subsequently used to identify the optimal gene expression patterns of the investigated pathway. As a proof of concept, our approach yielded the natural product violacein at 525.4 mg/L in shake flasks, a 3.2-fold increase from the baseline strain. Violacein production was further increased to 1.31 g/L in a controlled benchtop bioreactor. We found that formulating discretized gene expression levels into logarithmic variables (Linlog transformation) was essential for implementing this DoE-based optimization procedure. The reported methodology can aid multivariate combinatorial pathway engineering and may be generalized as a standard procedure for accelerating strain engineering and improving metabolic pathway efficiency.

  18. Analysis of Data on Xanthan Fermentation in Stationary Phase Using Black Box and Metabolic Network Models

    Institute of Scientific and Technical Information of China (English)

    马红武; 赵学明; 唐寅杰

    1999-01-01

    The xanthan fermentation data in the stationary phase was analyzed using the black box and the metabolic network models. The data consistency ls checked through the elemental balance in the black box model. In the metabolic network model, the metabolic flux distribution in the cell is calculated using the metabolic flux analysis method, then the maintenance coefficients is calculated.

  19. Informed Consent in Genome-Scale Research: What Do Prospective Participants Think?

    Science.gov (United States)

    Trinidad, Susan Brown; Fullerton, Stephanie M.; Bares, Julie M.; Jarvik, Gail P.; Larson, Eric B.; Burke, Wylie

    2012-01-01

    Background To promote effective genome-scale research, genomic and clinical data for large population samples must be collected, stored, and shared. Methods We conducted focus groups with 45 members of a Seattle-based integrated healthcare delivery system to learn about their views and expectations for informed consent in genome-scale studies. Results Participants viewed information about study purpose, aims, and how and by whom study data could be used to be at least as important as information about risks and possible harms. They generally supported a tiered consent approach for specific issues, including research purpose, data sharing, and access to individual research results. Participants expressed a continuum of opinions with respect to the acceptability of broad consent, ranging from completely acceptable to completely unacceptable. Older participants were more likely to view the consent process in relational – rather than contractual – terms, compared with younger participants. The majority of participants endorsed seeking study subjects’ permission regarding material changes in study purpose and data sharing. Conclusions Although this study sample was limited in terms of racial and socioeconomic diversity, our results suggest a strong positive interest in genomic research on the part of at least some prospective participants and indicate a need for increased public engagement, as well as strategies for ongoing communication with study participants. PMID:23493836

  20. Cardiovascular Changes in Animal Models of Metabolic Syndrome

    Directory of Open Access Journals (Sweden)

    Alexandre M. Lehnen

    2013-01-01

    Full Text Available Metabolic syndrome has been defined as a group of risk factors that directly contribute to the development of cardiovascular disease and/or type 2 diabetes. Insulin resistance seems to have a fundamental role in the genesis of this syndrome. Over the past years to the present day, basic and translational research has used small animal models to explore the pathophysiology of metabolic syndrome and to develop novel therapies that might slow the progression of this prevalent condition. In this paper we discuss the animal models used for the study of metabolic syndrome, with particular focus on cardiovascular changes, since they are the main cause of death associated with the condition in humans.

  1. Computer Modeling of Carbon Metabolism Enables Biofuel Engineering (Fact Sheet)

    Energy Technology Data Exchange (ETDEWEB)

    2011-09-01

    In an effort to reduce the cost of biofuels, the National Renewable Energy Laboratory (NREL) has merged biochemistry with modern computing and mathematics. The result is a model of carbon metabolism that will help researchers understand and engineer the process of photosynthesis for optimal biofuel production.

  2. Fast reconstruction of compact context-specific metabolic network models.

    Directory of Open Access Journals (Sweden)

    Nikos Vlassis

    2014-01-01

    Full Text Available Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue, and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.

  3. Perspectives in metabolic engineering: understanding cellular regulation towards the control of metabolic routes.

    Science.gov (United States)

    Zadran, Sohila; Levine, Raphael D

    2013-01-01

    Metabolic engineering seeks to redirect metabolic pathways through the modification of specific biochemical reactions or the introduction of new ones with the use of recombinant technology. Many of the chemicals synthesized via introduction of product-specific enzymes or the reconstruction of entire metabolic pathways into engineered hosts that can sustain production and can synthesize high yields of the desired product as yields of natural product-derived compounds are frequently low, and chemical processes can be both energy and material expensive; current endeavors have focused on using biologically derived processes as alternatives to chemical synthesis. Such economically favorable manufacturing processes pursue goals related to sustainable development and "green chemistry". Metabolic engineering is a multidisciplinary approach, involving chemical engineering, molecular biology, biochemistry, and analytical chemistry. Recent advances in molecular biology, genome-scale models, theoretical understanding, and kinetic modeling has increased interest in using metabolic engineering to redirect metabolic fluxes for industrial and therapeutic purposes. The use of metabolic engineering has increased the productivity of industrially pertinent small molecules, alcohol-based biofuels, and biodiesel. Here, we highlight developments in the practical and theoretical strategies and technologies available for the metabolic engineering of simple systems and address current limitations.

  4. Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction: COMMUNITY DATA-DRIVEN METABOLIC NETWORK MODELING

    Energy Technology Data Exchange (ETDEWEB)

    Henry, Christopher S. [Division of Mathematics and Computer Science, Argonne National Laboratory, Argonne Illinois; Computation Institute, University of Chicago, Chicago Illinois; Bernstein, Hans C. [Biodetection Sciences, National Security Directorate, Pacific Northwest National Laboratory Richland Washington; Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman Washington; Weisenhorn, Pamela [Division of Mathematics and Computer Science, Argonne National Laboratory, Argonne Illinois; Division of Biosciences, Argonne National Laboratory, Argonne Illinois; Taylor, Ronald C. [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; Lee, Joon-Yong [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; Zucker, Jeremy [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; Song, Hyun-Seob [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington

    2016-06-02

    Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources.

  5. Mathematical model of galactose regulation and metabolic consumption in yeast.

    Science.gov (United States)

    Mitre, Tina M; Mackey, Michael C; Khadra, Anmar

    2016-10-21

    The galactose network has been extensively studied at the unicellular level to broaden our understanding of the regulatory mechanisms governing galactose metabolism in multicellular organisms. Although the key molecular players involved in the metabolic and regulatory processes of this system have been known for decades, their interactions and chemical kinetics remain incompletely understood. Mathematical models can provide an alternative method to study the dynamics of this network from a quantitative and a qualitative perspective. Here, we employ this approach to unravel the main properties of the galactose network, including equilibrium binary and temporal responses, as a way to decipher its adaptation to actively-changing inputs. We combine its two main components: the genetic branch, which allows for bistable responses, and a metabolic branch, encompassing the relevant metabolic processes that can be repressed by glucose. We use both computational tools to estimate model parameters based on published experimental data, as well as bifurcation analysis to decipher the properties of the system in various parameter regimes. Our model analysis reveals that the interplay between the inducer (galactose) and the repressor (glucose) creates a bistable regime which dictates the temporal responses of the system. Based on the same bifurcation techniques, we explain why the system is robust to genetic mutations and molecular instabilities. These findings may provide experimentalists with a theoretical framework with which they can determine how the galactose network functions under various conditions.

  6. Metabolic disruption identified in the Huntington's disease transgenic sheep model.

    Science.gov (United States)

    Handley, Renee R; Reid, Suzanne J; Patassini, Stefano; Rudiger, Skye R; Obolonkin, Vladimir; McLaughlan, Clive J; Jacobsen, Jessie C; Gusella, James F; MacDonald, Marcy E; Waldvogel, Henry J; Bawden, C Simon; Faull, Richard L M; Snell, Russell G

    2016-02-11

    Huntington's disease (HD) is a dominantly inherited, progressive neurodegenerative disorder caused by a CAG repeat expansion within exon 1 of HTT, encoding huntingtin. There are no therapies that can delay the progression of this devastating disease. One feature of HD that may play a critical role in its pathogenesis is metabolic disruption. Consequently, we undertook a comparative study of metabolites in our transgenic sheep model of HD (OVT73). This model does not display overt symptoms of HD but has circadian rhythm alterations and molecular changes characteristic of the early phase disease. Quantitative metabolite profiles were generated from the motor cortex, hippocampus, cerebellum and liver tissue of 5 year old transgenic sheep and matched controls by gas chromatography-mass spectrometry. Differentially abundant metabolites were evident in the cerebellum and liver. There was striking tissue-specificity, with predominantly amino acids affected in the transgenic cerebellum and fatty acids in the transgenic liver, which together may indicate a hyper-metabolic state. Furthermore, there were more strong pair-wise correlations of metabolite abundance in transgenic than in wild-type cerebellum and liver, suggesting altered metabolic constraints. Together these differences indicate a metabolic disruption in the sheep model of HD and could provide insight into the presymptomatic human disease.

  7. Persistent impaired glucose metabolism in a zebrafish hyperglycemia model.

    Science.gov (United States)

    Capiotti, Katiucia Marques; Antonioli, Régis; Kist, Luiza Wilges; Bogo, Maurício Reis; Bonan, Carla Denise; Da Silva, Rosane Souza

    2014-05-01

    Diabetes mellitus (DM) affects over 10% of the world's population. Hyperglycemia is the main feature for the diagnosis of this disease. The zebrafish (Danio rerio) is an established model organism for the study of various metabolic diseases. In this paper, hyperglycemic zebrafish, when immersed in a 111 mM glucose solution for 14 days, developed increased glycation of proteins from the eyes, decreased mRNA levels of insulin receptors in the muscle, and a reversion of high blood glucose level after treatment with anti-diabetic drugs (glimepiride and metformin) even after 7 days of glucose withdrawal. Additionally, hyperglycemic zebrafish developed an impaired response to exogenous insulin, which was recovered after 7 days of glucose withdrawal. These data suggest that the exposure of adult zebrafish to high glucose concentration is able to induce persistent metabolic changes probably underlined by a hyperinsulinemic state and impaired peripheral glucose metabolism.

  8. Dynamic Metabolic Modeling of Denitrifying Bacterial Growth: The Cybernetic Approach

    Energy Technology Data Exchange (ETDEWEB)

    Song, Hyun-Seob; Liu, Chongxuan

    2015-06-29

    Denitrification is a multistage reduction process converting nitrate ultimately to nitrogen gas, carried out mostly by facultative bacteria. Modeling of the denitrification process is challenging due to the complex metabolic regulation that modulates sequential formation and consumption of a series of nitrogen oxide intermediates, which serve as the final electron acceptors for denitrifying bacteria. In this work, we examined the effectiveness and accuracy of the cybernetic modeling framework in simulating the growth dynamics of denitrifying bacteria in comparison with kinetic models. In four different case studies using the literature data, we successfully simulated diauxic and triauxic growth patterns observed in anoxic and aerobic conditions, only by tuning two or three parameters. In order to understand the regulatory structure of the cybernetic model, we systematically analyzed the effect of cybernetic control variables on simulation accuracy. The results showed that the consideration of both enzyme synthesis and activity control through u- and v-variables is necessary and relevant and that uvariables are of greater importance in comparison to v-variables. In contrast, simple kinetic models were unable to accurately capture dynamic metabolic shifts across alternative electron acceptors, unless an inhibition term was additionally incorporated. Therefore, the denitrification process represents a reasonable example highlighting the criticality of considering dynamic regulation for successful metabolic modeling.

  9. Singular value decomposition of genome-scale mRNA lengths distribution reveals asymmetry in RNA gel electrophoresis band broadening.

    Science.gov (United States)

    Alter, Orly; Golub, Gene H

    2006-08-01

    We describe the singular value decomposition (SVD) of yeast genome-scale mRNA lengths distribution data measured by DNA microarrays. SVD uncovers in the mRNA abundance levels data matrix of genes x arrays, i.e., electrophoretic gel migration lengths or mRNA lengths, mathematically unique decorrelated and decoupled "eigengenes." The eigengenes are the eigenvectors of the arrays x arrays correlation matrix, with the corresponding series of eigenvalues proportional to the series of the "fractions of eigen abundance." Each fraction of eigen abundance indicates the significance of the corresponding eigengene relative to all others. We show that the eigengenes fit "asymmetric Hermite functions," a generalization of the eigenfunctions of the quantum harmonic oscillator and the integral transform which kernel is a generalized coherent state. The fractions of eigen abundance fit a geometric series as do the eigenvalues of the integral transform which kernel is a generalized coherent state. The "asymmetric generalized coherent state" models the measured data, where the profiles of mRNA abundance levels of most genes as well as the distribution of the peaks of these profiles fit asymmetric Gaussians. We hypothesize that the asymmetry in the distribution of the peaks of the profiles is due to two competing evolutionary forces. We show that the asymmetry in the profiles of the genes might be due to a previously unknown asymmetry in the gel electrophoresis thermal broadening of a moving, rather than a stationary, band of RNA molecules.

  10. Piecewise multivariate modelling of sequential metabolic profiling data

    Directory of Open Access Journals (Sweden)

    Nicholson Jeremy K

    2008-02-01

    Full Text Available Abstract Background Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. Results A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. Conclusion The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA for modelling and analysis of short time-series data.

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

    Science.gov (United States)

    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.

  12. 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

    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...... using Arabidopsis NimbleGen ATH6 microarrays. In total 6061 transcripts were significantly cold regulated (p majority of the transcripts (75%) showed ecotype specific expression pattern. By using sequence data...... 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...

  13. 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.

  14. 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.

  15. Identification of novel targets for breast cancer by exploring gene switches on a genome scale

    Directory of Open Access Journals (Sweden)

    Wu Ming

    2011-11-01

    Full Text Available Abstract Background An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches. Results We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2 is uncovered. We further suggest a likely transcription factor that regulates TACSTD2. Conclusions Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.

  16. CFMDS: CUDA-based fast multidimensional scaling for genome-scale data.

    Science.gov (United States)

    Park, Sungin; Shin, Soo-Yong; Hwang, Kyu-Baek

    2012-01-01

    Multidimensional scaling (MDS) is a widely used approach to dimensionality reduction. It has been applied to feature selection and visualization in various areas. Among diverse MDS methods, the classical MDS is a simple and theoretically sound solution for projecting data objects onto a low dimensional space while preserving the original distances among them as much as possible. However, it is not trivial to apply it to genome-scale data (e.g., microarray gene expression profiles) on regular desktop computers, because of its high computational complexity. We implemented a highly-efficient software application, called CFMDS (CUDA-based Fast MultiDimensional Scaling), which produces an approximate solution of the classical MDS based on CUDA (compute unified device architecture) and the divide-and-conquer principle. CUDA is a parallel computing architecture exploiting the power of the GPU (graphics processing unit). The principle of divide-and-conquer was adopted for circumventing the small memory problem of usual graphics cards. Our application software has been tested on various benchmark datasets including microarrays and compared with the classical MDS algorithms implemented using C# and MATLAB. In our experiments, CFMDS was more than a hundred times faster for large data than such general solutions. Regarding the quality of dimensionality reduction, our approximate solutions were as good as those from the general solutions, as the Pearson's correlation coefficients between them were larger than 0.9. CFMDS is an expeditious solution for the data dimensionality reduction problem. It is especially useful for efficient processing of genome-scale data consisting of several thousands of objects in several minutes.

  17. Metabolic investigation of host/pathogen interaction using MS2-infected Escherichia coli

    Directory of Open Access Journals (Sweden)

    Jain Rishi

    2009-12-01

    Full Text Available Abstract Background RNA viruses are responsible for a variety of illnesses among people, including but not limited to the common cold, the flu, HIV, and ebola. Developing new drugs and new strategies for treating diseases caused by these viruses can be an expensive and time-consuming process. Mathematical modeling may be used to elucidate host-pathogen interactions and highlight potential targets for drug development, as well providing the basis for optimizing patient treatment strategies. The purpose of this work was to determine whether a genome-scale modeling approach could be used to understand how metabolism is impacted by the host-pathogen interaction during a viral infection. Escherichia coli/MS2 was used as the host-pathogen model system as MS2 is easy to work with, harmless to humans, but shares many features with eukaryotic viruses. In addition, the genome-scale metabolic model of E. coli is the most comprehensive model at this time. Results Employing a metabolic modeling strategy known as "flux balance analysis" coupled with experimental studies, we were able to predict how viral infection would alter bacterial metabolism. Based on our simulations, we predicted that cell growth and biosynthesis of the cell wall would be halted. Furthermore, we predicted a substantial increase in metabolic activity of the pentose phosphate pathway as a means to enhance viral biosynthesis, while a break down in the citric acid cycle was predicted. Also, no changes were predicted in the glycolytic pathway. Conclusions Through our approach, we have developed a technique of modeling virus-infected host metabolism and have investigated the metabolic effects of viral infection. These studies may provide insight into how to design better drugs. They also illustrate the potential of extending such metabolic analysis to higher order organisms, including humans.

  18. Temporal expression-based analysis of metabolism.

    Directory of Open Access Journals (Sweden)

    Sara B Collins

    Full Text Available Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM. We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such "history-dependent" sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.

  19. Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.

    Science.gov (United States)

    Adadi, Roi; Volkmer, Benjamin; Milo, Ron; Heinemann, Matthias; Shlomi, Tomer

    2012-01-01

    Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.

  20. Human metabolic atlas: an online resource for human metabolism.

    Science.gov (United States)

    Pornputtapong, Natapol; Nookaew, Intawat; Nielsen, Jens

    2015-01-01

    Human tissue-specific genome-scale metabolic models (GEMs) provide comprehensive understanding of human metabolism, which is of great value to the biomedical research community. To make this kind of data easily accessible to the public, we have designed and deployed the human metabolic atlas (HMA) website (http://www.metabolicatlas.org). This online resource provides comprehensive information about human metabolism, including the results of metabolic network analyses. We hope that it can also serve as an information exchange interface for human metabolism knowledge within the research community. The HMA consists of three major components: Repository, Hreed (Human REaction Entities Database) and Atlas. Repository is a collection of GEMs for specific human cell types and human-related microorganisms in SBML (System Biology Markup Language) format. The current release consists of several types of GEMs: a generic human GEM, 82 GEMs for normal cell types, 16 GEMs for different cancer cell types, 2 curated GEMs and 5 GEMs for human gut bacteria. Hreed contains detailed information about biochemical reactions. A web interface for Hreed facilitates an access to the Hreed reaction data, which can be easily retrieved by using specific keywords or names of related genes, proteins, compounds and cross-references. Atlas web interface can be used for visualization of the GEMs collection overlaid on KEGG metabolic pathway maps with a zoom/pan user interface. The HMA is a unique tool for studying human metabolism, ranging in scope from an individual cell, to a specific organ, to the overall human body. This resource is freely available under a Creative Commons Attribution-NonCommercial 4.0 International License.

  1. 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.

  2. Metabolism

    Science.gov (United States)

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

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

    DEFF Research Database (Denmark)

    Montagud, Arnau; Zelezniak, Aleksej; Navarro, Emilio

    2011-01-01

    activities and metabolic physiology, flux coupling analysis was performed for iSyn811 under four different growth conditions, viz., autotrophy, mixotrophy, heterotrophy, and light-activated heterotrophy (LH). Initial steps of carbon acquisition and catabolism formed the versatile center of the flux coupling...... and reporter flux coupling groups - regulatory hot spots during metabolic shifts triggered by the availability of light. Overall, flux coupling analysis provided insight into the structural organization of Synechocystis sp. PCC6803 metabolic network toward designing of a photosynthesis-based production......-scale metabolic model is a pre-requisite toward achieving a proficient photosynthetic cell factory. To this end, we report iSyn811, an upgraded genome-scale metabolic model of Synechocystis sp. PCC6803 consisting of 956 reactions and accounting for 811 genes. To gain insights into the interplay between flux...

  4. Metabolic engineering of lactic acid bacteria, the combined approach: kinetic modelling, metabolic control and experimental analysis.

    Science.gov (United States)

    Hoefnagel, Marcel H N; Starrenburg, Marjo J C; Martens, Dirk E; Hugenholtz, Jeroen; Kleerebezem, Michiel; Van Swam, Iris I; Bongers, Roger; Westerhoff, Hans V; Snoep, Jacky L

    2002-04-01

    Everyone who has ever tried to radically change metabolic fluxes knows that it is often harder to determine which enzymes have to be modified than it is to actually implement these changes. In the more traditional genetic engineering approaches 'bottle-necks' are pinpointed using qualitative, intuitive approaches, but the alleviation of suspected 'rate-limiting' steps has not often been successful. Here the authors demonstrate that a model of pyruvate distribution in Lactococcus lactis based on enzyme kinetics in combination with metabolic control analysis clearly indicates the key control points in the flux to acetoin and diacetyl, important flavour compounds. The model presented here (available at http://jjj.biochem.sun.ac.za/wcfs.html) showed that the enzymes with the greatest effect on this flux resided outside the acetolactate synthase branch itself. Experiments confirmed the predictions of the model, i.e. knocking out lactate dehydrogenase and overexpressing NADH oxidase increased the flux through the acetolactate synthase branch from 0 to 75% of measured product formation rates.

  5. Application of Gray Metabolic Model in the Prediction of the Cotton Output in China

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    In order to forecast the cotton output of China in the year 2011, Gray Metabolic Forecast Model is established based on both the Gray Forecast Model and the Metabolic Theory. According to the actual situation, forecast results of conventional GM (1, 1) Model and Metabolism GM (1, 1) Model are analyzed, showing that Metabolic Forecast Model has higher precision than the conventional forecast model. Therefore, Metabolism GM (1, 1) Model is used to forecast the cotton output of China in the year 2011, which is 614 968.3 thousand tons.

  6. Mouse models for inherited endocrine and metabolic disorders.

    Science.gov (United States)

    Piret, Siân E; Thakker, Rajesh V

    2011-12-01

    In vivo models represent important resources for investigating the physiological mechanisms underlying endocrine and metabolic disorders, and for pre-clinical translational studies that may include the assessments of new treatments. In the study of endocrine diseases, which affect multiple organs, in vivo models provide specific advantages over in vitro models, which are limited to investigation of isolated systems. In recent years, the mouse has become the popular choice for developing such in vivo mammalian models, as it has a genome that shares ∼85% identity to that of man, and has many physiological systems that are similar to those in man. Moreover, methods have been developed to alter the expression of genes in the mouse, thereby generating models for human diseases, which may be due to loss- or gain-of-function mutations. The methods used to generate mutations in the mouse genome include: chemical mutagenesis; conventional, conditional and inducible knockout models; knockin models and transgenic models, and these strategies are often complementary. This review describes some of the different strategies that are utilised for generating mouse models. In addition, some mouse models that have been successfully generated by these methods for some human hereditary endocrine and metabolic disorders are reviewed. In particular, the mouse models generated for parathyroid disorders, which include: the multiple endocrine neoplasias; hyperparathyroidism-jaw tumour syndrome; disorders of the calcium-sensing receptor and forms of inherited hypoparathyroidism are discussed. The advances that have been made in our understanding of the mechanisms of these human diseases by investigations of these mouse models are described.

  7. A flexible state-space approach for the modeling of metabolic networks II: advanced interrogation of hybridoma metabolism.

    Science.gov (United States)

    Baughman, Adam C; Sharfstein, Susan T; Martin, Lealon L

    2011-03-01

    Having previously introduced the mathematical framework of topological metabolic analysis (TMA) - a novel optimization-based technique for modeling metabolic networks of arbitrary size and complexity - we demonstrate how TMA facilitates unique methods of metabolic interrogation. With the aid of several hybridoma metabolic investigations as case-studies (Bonarius et al., 1995, 1996, 2001), we first establish that the TMA framework identifies biologically important aspects of the metabolic network under investigation. We also show that the use of a structured weighting approach within our objective provides a substantial modeling benefit over an unstructured, uniform, weighting approach. We then illustrate the strength of TAM as an advanced interrogation technique, first by using TMA to prove the existence of (and to quantitatively describe) multiple topologically distinct configurations of a metabolic network that each optimally model a given set of experimental observations. We further show that such alternate topologies are indistinguishable using existing stoichiometric modeling techniques, and we explain the biological significance of the topological variables appearing within our model. By leveraging the manner in which TMA implements metabolite inputs and outputs, we also show that metabolites whose possible metabolic fates are inadequately described by a given network reconstruction can be quickly identified. Lastly, we show how the use of the TMA aggregate objective function (AOF) permits the identification of modeling solutions that can simultaneously consider experimental observations, underlying biological motivations, or even purely engineering- or design-based goals.

  8. Mathematically modelling the dynamics of cholesterol metabolism and ageing.

    Science.gov (United States)

    Morgan, A E; Mooney, K M; Wilkinson, S J; Pickles, N A; Mc Auley, M T

    2016-07-01

    Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the UK. This condition becomes increasingly prevalent during ageing; 34.1% and 29.8% of males and females respectively, over 75 years of age have an underlying cardiovascular problem. The dysregulation of cholesterol metabolism is inextricably correlated with cardiovascular health and for this reason low density lipoprotein cholesterol (LDL-C) and high density lipoprotein cholesterol (HDL-C) are routinely used as biomarkers of CVD risk. The aim of this work was to use mathematical modelling to explore how cholesterol metabolism is affected by the ageing process. To do this we updated a previously published whole-body mathematical model of cholesterol metabolism to include an additional 96 mechanisms that are fundamental to this biological system. Additional mechanisms were added to cholesterol absorption, cholesterol synthesis, reverse cholesterol transport (RCT), bile acid synthesis, and their enterohepatic circulation. The sensitivity of the model was explored by the use of both local and global parameter scans. In addition, acute cholesterol feeding was used to explore the effectiveness of the regulatory mechanisms which are responsible for maintaining whole-body cholesterol balance. It was found that our model behaves as a hypo-responder to cholesterol feeding, while both the hepatic and intestinal pools of cholesterol increased significantly. The model was also used to explore the effects of ageing in tandem with three different cholesterol ester transfer protein (CETP) genotypes. Ageing in the presence of an atheroprotective CETP genotype, conferring low CETP activity, resulted in a 0.6% increase in LDL-C. In comparison, ageing with a genotype reflective of high CETP activity, resulted in a 1.6% increase in LDL-C. Thus, the model has illustrated the importance of CETP genotypes such as I405V, and their potential role in healthy ageing. Copyright © 2016 Elsevier Ireland Ltd. All

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  10. [Postoperative metabolic acidosis: use of three different fluid therapy models].

    Science.gov (United States)

    Tellan, Guglielmo; Antonucci, Adriana; Marandola, Maurizio; Naclerio, Michele; Fiengo, Leslie; Molinari, Stefania; Delogu, Giovanna

    2008-01-01

    Intraoperative fluid administration is considered an important factor in the management of metabolic acidosis following surgical procedures. The aim of this study was to compare three types of intraoperative infusional models in order to evaluate their effect on acid-base changes in the immediate postoperative period as calculated by both the Henderson-Hasselbach equation and the Stewart approach. Forty-seven patients undergoing left hemicolectomy were enrolled in the study and assigned randomly to receiving 0.9% saline alone (Group A, n=16), lactated Ringer's solution alone (Group B, n=16) or 0.9% saline and Ringer's solution, 1:1 ratio (Group C, n=15). Arterial blood samples were taken before operation (t0) and 30 min after extubation (t1) in order to measure the acid-base balance. The results showed a metabolic acidosis status in Group A patients, whereas Group B exhibited metabolic alkalosis only by means of the Stewart method. No difference was found in Group C between the time points t0 and t1 when using either the Henderson-Hasselbach equation or using the Stewart model. We conclude that saline solution in association with Ringer's solution (1:1 ratio) appears to be the most suitable form of intraoperative fluid management in order to guarantee a stable acid-base balance in selected surgical patients during the immediate postoperative period.

  11. A continuum model for metabolic gas exchange in pear fruit.

    Directory of Open Access Journals (Sweden)

    Q Tri Ho

    2008-03-01

    Full Text Available Exchange of O(2 and CO(2 of plants with their environment is essential for metabolic processes such as photosynthesis and respiration. In some fruits such as pears, which are typically stored under a controlled atmosphere with reduced O(2 and increased CO(2 levels to extend their commercial storage life, anoxia may occur, eventually leading to physiological disorders. In this manuscript we have developed a mathematical model to predict the internal gas concentrations, including permeation, diffusion, and respiration and fermentation kinetics. Pear fruit has been selected as a case study. The model has been used to perform in silico experiments to evaluate the effect of, for example, fruit size or ambient gas concentration on internal O(2 and CO(2 levels. The model incorporates the actual shape of the fruit and was solved using fluid dynamics software. Environmental conditions such as temperature and gas composition have a large effect on the internal distribution of oxygen and carbon dioxide in fruit. Also, the fruit size has a considerable effect on local metabolic gas concentrations; hence, depending on the size, local anaerobic conditions may result, which eventually may lead to physiological disorders. The model developed in this manuscript is to our knowledge the most comprehensive model to date to simulate gas exchange in plant tissue. It can be used to evaluate the effect of environmental stresses on fruit via in silico experiments and may lead to commercial applications involving long-term storage of fruit under controlled atmospheres.

  12. Genome-scale identification method applied to find cryptic aminoglycoside resistance genes in Pseudomonas aeruginosa.

    Directory of Open Access Journals (Sweden)

    Julie M Struble

    Full Text Available BACKGROUND: The ability of bacteria to rapidly evolve resistance to antibiotics is a critical public health problem. Resistance leads to increased disease severity and death rates, as well as imposes pressure towards the discovery and development of new antibiotic therapies. Improving understanding of the evolution and genetic basis of resistance is a fundamental goal in the field of microbiology. RESULTS: We have applied a new genomic method, Scalar Analysis of Library Enrichments (SCALEs, to identify genomic regions that, given increased copy number, may lead to aminoglycoside resistance in Pseudomonas aeruginosa at the genome scale. We report the result of selections on highly representative genomic libraries for three different aminoglycoside antibiotics (amikacin, gentamicin, and tobramycin. At the genome-scale, we show significant (p<0.05 overlap in genes identified for each aminoglycoside evaluated. Among the genomic segments identified, we confirmed increased resistance associated with an increased copy number of several genomic regions, including the ORF of PA5471, recently implicated in MexXY efflux pump related aminoglycoside resistance, PA4943-PA4946 (encoding a probable GTP-binding protein, a predicted host factor I protein, a delta 2-isopentenylpyrophosphate transferase, and DNA mismatch repair protein mutL, PA0960-PA0963 (encoding hypothetical proteins, a probable cold shock protein, a probable DNA-binding stress protein, and aspartyl-tRNA synthetase, a segment of PA4967 (encoding a topoisomerase IV subunit B, as well as a chimeric clone containing two inserts including the ORFs PA0547 and PA2326 (encoding a probable transcriptional regulator and a probable hypothetical protein, respectively. CONCLUSIONS: The studies reported here demonstrate the application of new a genomic method, SCALEs, which can be used to improve understanding of the evolution of antibiotic resistance in P. aeruginosa. In our demonstration studies, we

  13. Comparisons of Shewanella strains based on genome annotations, modeling and experiments

    Energy Technology Data Exchange (ETDEWEB)

    Ong, Wai Kit; Vu, Trang; Lovendahl, Klaus N.; Llull, Jenna; Serres, Margaret; Romine, Margaret F.; Reed, Jennifer L.

    2014-01-01

    Shewanella is a genus of facultatively anaerobic, Gram-negative bacteria that have highly adaptable metabolism which allows them to thrive in diverse environments. This quality makes them attractive target bacteria for research in bioremediation and microbial fuel cell applications. Constraint-based modeling is a useful tool for helping researchers gain insights into the metabolic capabilities of these bacteria. However, Shewanella oneidensis MR-1 is the only strain with a genome-scale metabolic model constructed out of the 22 sequenced Shewanella strains.

  14. Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm

    Science.gov (United States)

    Megchelenbrink, Wout; Rossell, Sergio; Huynen, Martijn A.

    2015-01-01

    Motivation Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA), which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental “omics” data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more “flexible” metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions. Results Here, we propose Maximum Metabolic Flexibility (MMF) a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i) indeed, most of the measured fluxes agree with a high adaptability of the network, ii) this result can be used to further reduce the space of feasible solutions iii) this reduced space improves the quantitative predictions

  15. Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm.

    Directory of Open Access Journals (Sweden)

    Wout Megchelenbrink

    Full Text Available Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA, which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental "omics" data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more "flexible" metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions.Here, we propose Maximum Metabolic Flexibility (MMF a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i indeed, most of the measured fluxes agree with a high adaptability of the network, ii this result can be used to further reduce the space of feasible solutions iii this reduced space improves the quantitative predictions made by FBA and

  16. Stockhome: A Spreadsheet Model of Urban Heavy Metal Metabolism

    Energy Technology Data Exchange (ETDEWEB)

    Hedbrant, J. [Linkoeping University, Department of Water and Environmental Studies (Sweden)], E-mail: johhe@ikp.liu.se

    2001-05-15

    Computer models for analysis,visualising and decision support in environmental research have become increasingly popular. The Stockhome project, where the urban metabolism of heavy metals in Stockholm was studied, resulted in a database with historical data of the use of goods containing cadmium (Cd), chromium (Cr),copper (Cu), lead (Pb), mercury (Hg), nickel (Ni)and zinc (Zn). A spreadsheet model was developed to study flows and stocks of the metal consumption process and emissions. The model indicates uncertainties of the data, societal aspects such as field of use and rights of disposition of the goods. By considering goods as the drivers of the emissions, the model would be well suited for policy support.

  17. Salmonella Modulates Metabolism During Growth under Conditions that Induce Expression of Virulence Genes

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Young-Mo; Schmidt, Brian; Kidwai, Afshan S.; Jones, Marcus B.; Deatherage, Brooke L.; Brewer, Heather M.; Mitchell, Hugh D.; Palsson, Bernhard O.; McDermott, Jason E.; Heffron, Fred; Smith, Richard D.; Peterson, Scott N.; Ansong, Charles; Hyduke, Daniel R.; Metz, Thomas O.; Adkins, Joshua N.

    2013-04-05

    Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Excitingly, we observed possible sequestration of metabolites recently suggested to have immune modulating roles. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Model-guided analysis suggested that alterations in metabolism prioritized other activities necessary for pathogenesis instead, such as lipopolysaccharide biosynthesis.

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

    Science.gov (United States)

    Prigent, Sylvain; Frioux, Clémence; Dittami, Simon M.; Larhlimi, Abdelhalim; Collet, Guillaume; Gutknecht, Fabien; Got, Jeanne; Eveillard, Damien; Bourdon, Jérémie; Plewniak, Frédéric; Tonon, Thierry; Siegel, Anne

    2017-01-01

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

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

    Science.gov (United States)

    Prigent, Sylvain; Frioux, Clémence; Dittami, Simon M; Thiele, Sven; Larhlimi, Abdelhalim; Collet, Guillaume; Gutknecht, Fabien; Got, Jeanne; Eveillard, Damien; Bourdon, Jérémie; Plewniak, Frédéric; Tonon, Thierry; Siegel, Anne

    2017-01-01

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

  20. METABOLISM

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    Objective: To determine the allele frequencies of genetic variants 373 Ala→Pro and 451 Arg→Gln of cholesteryl ester transfer protein (CETP) and to explore their potential impacts on serum lipid metabolism. Methods: The genotypes in CETP codon 373 and 451 in 91 German healthy students and 409 an-

  1. AtPID: a genome-scale resource for genotype–phenotype associations in Arabidopsis

    Science.gov (United States)

    Lv, Qi; Lan, Yiheng; Shi, Yan; Wang, Huan; Pan, Xia; Li, Peng; Shi, Tieliu

    2017-01-01

    AtPID (Arabidopsis thaliana Protein Interactome Database, available at http://www.megabionet.org/atpid) is an integrated database resource for protein interaction network and functional annotation. In the past few years, we collected 5564 mutants with significant morphological alterations and manually curated them to 167 plant ontology (PO) morphology categories. These single/multiple-gene mutants were indexed and linked to 3919 genes. After integrated these genotype–phenotype associations with the comprehensive protein interaction network in AtPID, we developed a Naïve Bayes method and predicted 4457 novel high confidence gene-PO pairs with 1369 genes as the complement. Along with the accumulated novel data for protein interaction and functional annotation, and the updated visualization toolkits, we present a genome-scale resource for genotype–phenotype associations for Arabidopsis in AtPID 5.0. In our updated website, all the new genotype–phenotype associations from mutants, protein network, and the protein annotation information can be vividly displayed in a comprehensive network view, which will greatly enhance plant protein function and genotype–phenotype association studies in a systematical way. PMID:27899679

  2. Genome scale evolution of myxoma virus reveals host-pathogen adaptation and rapid geographic spread.

    Science.gov (United States)

    Kerr, Peter J; Rogers, Matthew B; Fitch, Adam; Depasse, Jay V; Cattadori, Isabella M; Twaddle, Alan C; Hudson, Peter J; Tscharke, David C; Read, Andrew F; Holmes, Edward C; Ghedin, Elodie

    2013-12-01

    The evolutionary interplay between myxoma virus (MYXV) and the European rabbit (Oryctolagus cuniculus) following release of the virus in Australia in 1950 as a biological control is a classic example of host-pathogen coevolution. We present a detailed genomic and phylogeographic analysis of 30 strains of MYXV, including the Australian progenitor strain Standard Laboratory Strain (SLS), 24 Australian viruses isolated from 1951 to 1999, and three isolates from the early radiation in Britain from 1954 and 1955. We show that in Australia MYXV has spread rapidly on a spatial scale, with multiple lineages cocirculating within individual localities, and that both highly virulent and attenuated viruses were still present in the field through the 1990s. In addition, the detection of closely related virus lineages at sites 1,000 km apart suggests that MYXV moves freely in geographic space, with mosquitoes, fleas, and rabbit migration all providing means of transport. Strikingly, despite multiple introductions, all modern viruses appear to be ultimately derived from the original introductions of SLS. The rapidity of MYXV evolution was also apparent at the genomic scale, with gene duplications documented in a number of viruses. Duplication of potential virulence genes may be important in increasing the expression of virulence proteins and provides the basis for the evolution of novel functions. Mutations leading to loss of open reading frames were surprisingly frequent and in some cases may explain attenuation, but no common mutations that correlated with virulence or attenuation were identified.

  3. On the road to synthetic life: the minimal cell and genome-scale engineering.

    Science.gov (United States)

    Juhas, Mario

    2016-01-01

    Synthetic biology employs rational engineering principles to build biological systems from the libraries of standard, well characterized biological parts. Biological systems designed and built by synthetic biologists fulfill a plethora of useful purposes, ranging from better healthcare and energy production to biomanufacturing. Recent advancements in the synthesis, assembly and "booting-up" of synthetic genomes and in low and high-throughput genome engineering have paved the way for engineering on the genome-wide scale. One of the key goals of genome engineering is the construction of minimal genomes consisting solely of essential genes (genes indispensable for survival of living organisms). Besides serving as a toolbox to understand the universal principles of life, the cell encoded by minimal genome could be used to build a stringently controlled "cell factory" with a desired phenotype. This review provides an update on recent advances in the genome-scale engineering with particular emphasis on the engineering of minimal genomes. Furthermore, it presents an ongoing discussion to the scientific community for better suitability of minimal or robust cells for industrial applications.

  4. Genome-scale DNA sequence recognition by hybridization to short oligomers.

    Science.gov (United States)

    Milosavljević, A; Savković, S; Crkvenjakov, R; Salbego, D; Serrato, H; Kreuzer, H; Gemmell, A; Batus, S; Grujić, D; Carnahan, S; Tepavcević, J

    1996-01-01

    Recently developed hybridization technology (Drmanac et al. 1994) enables economical large-scale detection of short oligomers within DNA fragments. The newly developed recognition method (Milosavljević 1995b) enables comparison of lists of oligomers detected within DNA fragments against known DNA sequences. We here describe an experiment involving a set of 4,513 distinct genomic E.coli clones of average length 2kb, each hybridized with 636 randomly selected short oligomer probes. High hybridization signal with a particular probe was used as an indication of the presence of a complementary oligomer in the particular clone. For each clone, a list of oligomers with highest hybridization signals was compiled. The database consisting of 4,513 oligomer lists was then searched using known E.coli sequences as queries in an attempt to identify the clones that match the query sequence. Out of a total of 11 clones that were recognized at highest significance level by our method, 8 were single-pass sequenced from both ends. The single-pass sequenced ends were then compared against the query sequences. The sequence comparisons confirmed 7 out of the total of 8 examined recognitions. This experiment represents the first successful example of genome-scale sequence recognition based on hybridization data.

  5. DRUM: a new framework for metabolic modeling under non-balanced growth. Application to the carbon metabolism of unicellular microalgae.

    Directory of Open Access Journals (Sweden)

    Caroline Baroukh

    Full Text Available Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.

  6. DRUM: A New Framework for Metabolic Modeling under Non-Balanced Growth. Application to the Carbon Metabolism of Unicellular Microalgae

    Science.gov (United States)

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

    2014-01-01

    Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates. PMID:25105494

  7. Software applications toward quantitative metabolic flux analysis and modeling.

    Science.gov (United States)

    Dandekar, Thomas; Fieselmann, Astrid; Majeed, Saman; Ahmed, Zeeshan

    2014-01-01

    Metabolites and their pathways are central for adaptation and survival. Metabolic modeling elucidates in silico all the possible flux pathways (flux balance analysis, FBA) and predicts the actual fluxes under a given situation, further refinement of these models is possible by including experimental isotopologue data. In this review, we initially introduce the key theoretical concepts and different analysis steps in the modeling process before comparing flux calculation and metabolite analysis programs such as C13, BioOpt, COBRA toolbox, Metatool, efmtool, FiatFlux, ReMatch, VANTED, iMAT and YANA. Their respective strengths and limitations are discussed and compared to alternative software. While data analysis of metabolites, calculation of metabolic fluxes, pathways and their condition-specific changes are all possible, we highlight the considerations that need to be taken into account before deciding on a specific software. Current challenges in the field include the computation of large-scale networks (in elementary mode analysis), regulatory interactions and detailed kinetics, and these are discussed in the light of powerful new approaches.

  8. The emerging use of zebrafish to model metabolic disease

    Directory of Open Access Journals (Sweden)

    Asha Seth

    2013-09-01

    Full Text Available The zebrafish research community is celebrating! The zebrafish genome has recently been sequenced, the Zebrafish Mutation Project (launched by the Wellcome Trust Sanger Institute has published the results of its first large-scale ethylnitrosourea (ENU mutagenesis screen, and a host of new techniques, such as the genome editing technologies TALEN and CRISPR-Cas, are enabling specific mutations to be created in model organisms and investigated in vivo. The zebrafish truly seems to be coming of age. These powerful resources invoke the question of whether zebrafish can be increasingly used to model human disease, particularly common, chronic diseases of metabolism such as obesity and type 2 diabetes. In recent years, there has been considerable success, mainly from genomic approaches, in identifying genetic variants that are associated with these conditions in humans; however, mechanistic insights into the role of implicated disease loci are lacking. In this Review, we highlight some of the advantages and disadvantages of zebrafish to address the organism’s utility as a model system for human metabolic diseases.

  9. Basic concepts and principles of stoichiometric modeling of metabolic networks

    NARCIS (Netherlands)

    Maarleveld, T.R.; Khandelwal, R.A.; Olivier, B.E.; Teusink, B.; Bruggeman, F.J.

    2013-01-01

    Metabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into metabolic pla

  10. Quantum Mechanics/Molecular Mechanics Modeling of Drug Metabolism

    DEFF Research Database (Denmark)

    Lonsdale, Richard; Fort, Rachel M; Rydberg, Patrik;

    2016-01-01

    The mechanism of cytochrome P450(CYP)-catalyzed hydroxylation of primary amines is currently unclear and is relevant to drug metabolism; previous small model calculations have suggested two possible mechanisms: direct N-oxidation and H-abstraction/rebound. We have modeled the N-hydroxylation of (R......)-mexiletine in CYP1A2 with hybrid quantum mechanics/molecular mechanics (QM/MM) methods, providing a more detailed and realistic model. Multiple reaction barriers have been calculated at the QM(B3LYP-D)/MM(CHARMM27) level for the direct N-oxidation and H-abstraction/rebound mechanisms. Our calculated barriers...... indicate that the direct N-oxidation mechanism is preferred and proceeds via the doublet spin state of Compound I. Molecular dynamics simulations indicate that the presence of an ordered water molecule in the active site assists in the binding of mexiletine in the active site...

  11. Parameterization of a ruminant model of phosphorus digestion and metabolism.

    Science.gov (United States)

    Feng, X; Knowlton, K F; Hanigan, M D

    2015-10-01

    The objective of the current work was to parameterize the digestive elements of the model of Hill et al. (2008) using data collected from animals that were ruminally, duodenally, and ileally cannulated, thereby providing a better understanding of the digestion and metabolism of P fractions in growing and lactating cattle. The model of Hill et al. (2008) was fitted and evaluated for adequacy using the data from 6 animal studies. We hypothesized that sufficient data would be available to estimate P digestion and metabolism parameters and that these parameters would be sufficient to derive P bioavailabilities of a range of feed ingredients. Inputs to the model were dry matter intake; total feed P concentration (fPtFd); phytate (Pp), organic (Po), and inorganic (Pi) P as fractions of total P (fPpPt, fPoPt, fPiPt); microbial growth; amount of Pi and Pp infused into the omasum or ileum; milk yield; and BW. The available data were sufficient to derive all model parameters of interest. The final model predicted that given 75 g/d of total P input, the total-tract digestibility of P was 40.8%, Pp digestibility in the rumen was 92.4%, and in the total-tract was 94.7%. Blood P recycling to the rumen was a major source of Pi flow into the small intestine, and the primary route of excretion. A large proportion of Pi flowing to the small intestine was absorbed; however, additional Pi was absorbed from the large intestine (3.15%). Absorption of Pi from the small intestine was regulated, and given the large flux of salivary P recycling, the effective fractional small intestine absorption of available P derived from the diet was 41.6% at requirements. Milk synthesis used 16% of total absorbed P, and less than 1% was excreted in urine. The resulting model could be used to derive P bioavailabilities of commonly used feedstuffs in cattle production.

  12. 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

    translocation pathways, (2) assignment of all cellular proteins to one of four compartments (cytoplasm, inner membrane, periplasm, and outer membrane) and a translocation pathway, (3) experimentally determined translocase catalytic and porin diffusion rates, and (4) a novel membrane constraint that reflects......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...... in cell signaling, energy transduction, and other critical cellular functions. All these processes must share the limited space in the membrane; thus it represents a notable constraint on cellular functions. Membrane- and location-based processes have not yet been reconstructed and explicitly integrated...

  13. 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 lackin...

  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. Urban Metabolism Based on Emergy and Slack Based Model: A Case Study of Beijing, China

    Institute of Scientific and Technical Information of China (English)

    SONG Tao; CAI Jianming; XU Hui; DENG Yu; NIU Fangqu; YANG Zhenshan; DU Shanshan

    2015-01-01

    The key to studying urban sustainable development depends on quantifying stores,efficiencies of urban metabolisms and capturing urban metabolisms' mechanisms.This paper builds up the metabolic emergy account and quantifies some important concepts of emergy stores.Emphasis is placed on the urban metabolic model based on the slack based model (SBM) method to measure urban metabolic efficiencies.Urban metabolic mechanisms are discussed by using the regression method.By integrating these models,this paper analyzes the urban metabolic development in Beijing from 2001 to 2010.We conclude that the metabolic emergy stores of Beijing increased significantly from 2001 to 2010,with the emergy imported accotmting for most of the increase.The metabolic efficiencies in Beijing have improved since the 2008 Olympic Games.The population,economic growth,industrial structures,and environmental governance positively affect the overall urban metabolism,while the land expansion,urbanization and environmentally technical levels hinder the improving of urban metabolic efficiencies.The SBM metabolic method and the regression model based on the emergy analysis provide insights into the urban metabolic efficiencies and the mechanism.They can promote to integrate such concepts into their sustainability analyses and policy decisions.

  16. Metabolic and Blood Pressure Effects of Walnut Supplementation in a Mouse Model of the Metabolic Syndrome

    Directory of Open Access Journals (Sweden)

    Nicola J. A. Scott

    2017-07-01

    Full Text Available There is extensive evidence that walnut consumption is protective against cardiovascular disease and diabetes in the healthy population, but the beneficial effects of walnut consumption in individuals with the metabolic syndrome (MetS remain uncertain. We compared a range of cardio-metabolic traits and related tissue gene expression associated with 21 weeks of dietary walnut supplementation in a mouse model of MetS (MetS-Tg and wild-type (WT mice (n = 10 per genotype per diet, equal males and females. Compared to standard diet, walnuts did not significantly alter food consumption or body weight trajectory of either MetS-Tg or WT mice. In MetS-Tg mice, walnuts were associated with reductions in oral glucose area under the curve (gAUC, standard diet 1455 ± 54, walnut 1146 ± 91, p = 0.006 and mean arterial blood pressure (MAP, standard diet 100.6 ± 1.9, walnut 73.2 ± 1.8 mmHg, p < 0.001, with neutral effects on gAUC and MAP in WT mice. However, in MetS-Tg mice, walnuts were also associated with trends for higher plasma cholesterol (standard diet 4.73 ± 0.18, walnut 7.03 ± 1.99 mmol/L, p = 0.140 and triglyceride levels (standard diet 2.4 ± 0.5, walnut 5.4 ± 1.6 mmol/L, p = 0.061, despite lowering cholesterol and having no effect on triglycerides in WT mice. Moreover, in both MetS-Tg and WT mice, walnuts were associated with significantly increased liver expression of genes associated with metabolism (Fabp1, Insr, cell stress (Atf6, Ddit3, Eif2ak3, fibrosis (Hgf, Sp1, Timp1 and inflammation (Tnf, Ptpn22, Pparg. In conclusion, dietary walnuts were associated with modest favourable effects in WT mice, but a combination of beneficial and adverse effects in MetS-Tg mice, and up-regulation of hepatic pro-fibrotic and pro-inflammatory genes in both mouse strains.

  17. Metabolic and Blood Pressure Effects of Walnut Supplementation in a Mouse Model of the Metabolic Syndrome.

    Science.gov (United States)

    Scott, Nicola J A; Ellmers, Leigh J; Pilbrow, Anna P; Thomsen, Lotte; Richards, Arthur Mark; Frampton, Chris M; Cameron, Vicky A

    2017-07-07

    There is extensive evidence that walnut consumption is protective against cardiovascular disease and diabetes in the healthy population, but the beneficial effects of walnut consumption in individuals with the metabolic syndrome (MetS) remain uncertain. We compared a range of cardio-metabolic traits and related tissue gene expression associated with 21 weeks of dietary walnut supplementation in a mouse model of MetS (MetS-Tg) and wild-type (WT) mice (n = 10 per genotype per diet, equal males and females). Compared to standard diet, walnuts did not significantly alter food consumption or body weight trajectory of either MetS-Tg or WT mice. In MetS-Tg mice, walnuts were associated with reductions in oral glucose area under the curve (gAUC, standard diet 1455 ± 54, walnut 1146 ± 91, p = 0.006) and mean arterial blood pressure (MAP, standard diet 100.6 ± 1.9, walnut 73.2 ± 1.8 mmHg, p walnuts were also associated with trends for higher plasma cholesterol (standard diet 4.73 ± 0.18, walnut 7.03 ± 1.99 mmol/L, p = 0.140) and triglyceride levels (standard diet 2.4 ± 0.5, walnut 5.4 ± 1.6 mmol/L, p = 0.061), despite lowering cholesterol and having no effect on triglycerides in WT mice. Moreover, in both MetS-Tg and WT mice, walnuts were associated with significantly increased liver expression of genes associated with metabolism (Fabp1, Insr), cell stress (Atf6, Ddit3, Eif2ak3), fibrosis (Hgf, Sp1, Timp1) and inflammation (Tnf, Ptpn22, Pparg). In conclusion, dietary walnuts were associated with modest favourable effects in WT mice, but a combination of beneficial and adverse effects in MetS-Tg mice, and up-regulation of hepatic pro-fibrotic and pro-inflammatory genes in both mouse strains.

  18. Metabolic modelling of polyhydroxyalkanoate copolymers production by mixed microbial cultures

    Directory of Open Access Journals (Sweden)

    Reis Maria AM

    2008-07-01

    Full Text Available Abstract Background This paper presents a metabolic model describing the production of polyhydroxyalkanoate (PHA copolymers in mixed microbial cultures, using mixtures of acetic and propionic acid as carbon source material. Material and energetic balances were established on the basis of previously elucidated metabolic pathways. Equations were derived for the theoretical yields for cell growth and PHA production on mixtures of acetic and propionic acid as functions of the oxidative phosphorylation efficiency, P/O ratio. The oxidative phosphorylation efficiency was estimated from rate measurements, which in turn allowed the estimation of the theoretical yield coefficients. Results The model was validated with experimental data collected in a sequencing batch reactor (SBR operated under varying feeding conditions: feeding of acetic and propionic acid separately (control experiments, and the feeding of acetic and propionic acid simultaneously. Two different feast and famine culture enrichment strategies were studied: (i either with acetate or (ii with propionate as carbon source material. Metabolic flux analysis (MFA was performed for the different feeding conditions and culture enrichment strategies. Flux balance analysis (FBA was used to calculate optimal feeding scenarios for high quality PHA polymers production, where it was found that a suitable polymer would be obtained when acetate is fed in excess and the feeding rate of propionate is limited to ~0.17 C-mol/(C-mol.h. The results were compared with published pure culture metabolic studies. Conclusion Acetate was more conducive toward the enrichment of a microbial culture with higher PHA storage fluxes and yields as compared to propionate. The P/O ratio was not only influenced by the selected microbial culture, but also by the carbon substrate fed to each culture, where higher P/O ratio values were consistently observed for acetate than propionate. MFA studies suggest that when mixtures of

  19. Genome-Scale Assessment of Age-Related DNA Methylation Changes in Mouse Spermatozoa

    Science.gov (United States)

    Kobayashi, Norio; Okae, Hiroaki; Hiura, Hitoshi; Chiba, Hatsune; Shirakata, Yoshiki; Hara, Kenshiro; Tanemura, Kentaro; Arima, Takahiro

    2016-01-01

    DNA methylation plays important roles in the production and functioning of spermatozoa. Recent studies have suggested that DNA methylation patterns in spermatozoa can change with age, but the regions susceptible to age-related methylation changes remain to be fully elucidated. In this study, we conducted genome-scale DNA methylation profiling of spermatozoa obtained from C57BL/6N mice at 8 weeks (8w), 18 weeks (18w) and 17 months of age (17m). There was no substantial difference in the global DNA methylation patterns between 18w and 17m samples except for a slight increase of methylation levels in long interspersed nuclear elements in the 17m samples. We found that maternally methylated imprinting control regions (mICRs) and spermatogenesis-related gene promoters had 5–10% higher methylation levels in 8w samples than in 18w or 17m samples. Analysis of individual sequence reads suggested that these regions were fully methylated (80–100%) in a subset of 8w spermatozoa. These regions are also known to be highly methylated in a subset of postnatal spermatogonia, which might be the source of the increased DNA methylation in 8w spermatozoa. Another possible source was contamination by somatic cells. Although we carefully purified the spermatozoa, it was difficult to completely exclude the possibility of somatic cell contamination. Further studies are needed to clarify the source of the small increase in DNA methylation in the 8w samples. Overall, our findings suggest that DNA methylation patterns in mouse spermatozoa are relatively stable throughout reproductive life. PMID:27880848

  20. Genome-scale co-evolutionary inference identifies functions and clients of bacterial Hsp90.

    Directory of Open Access Journals (Sweden)

    Maximilian O Press

    Full Text Available The molecular chaperone Hsp90 is essential in eukaryotes, in which it facilitates the folding of developmental regulators and signal transduction proteins known as Hsp90 clients. In contrast, Hsp90 is not essential in bacteria, and a broad characterization of its molecular and organismal function is lacking. To enable such characterization, we used a genome-scale phylogenetic analysis to identify genes that co-evolve with bacterial Hsp90. We find that genes whose gain and loss were coordinated with Hsp90 throughout bacterial evolution tended to function in flagellar assembly, chemotaxis, and bacterial secretion, suggesting that Hsp90 may aid assembly of protein complexes. To add to the limited set of known bacterial Hsp90 clients, we further developed a statistical method to predict putative clients. We validated our predictions by demonstrating that the flagellar protein FliN and the chemotaxis kinase CheA behaved as Hsp90 clients in Escherichia coli, confirming the predicted role of Hsp90 in chemotaxis and flagellar assembly. Furthermore, normal Hsp90 function is important for wild-type motility and/or chemotaxis in E. coli. This novel function of bacterial Hsp90 agreed with our subsequent finding that Hsp90 is associated with a preference for multiple habitats and may therefore face a complex selection regime. Taken together, our results reveal previously unknown functions of bacterial Hsp90 and open avenues for future experimental exploration by implicating Hsp90 in the assembly of membrane protein complexes and adaptation to novel environments.

  1. GMATA: an integrated software package for genome-scale SSR mining, marker development and viewing

    Directory of Open Access Journals (Sweden)

    Xuewen Wang

    2016-09-01

    Full Text Available 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.

  2. Factors affecting reproducibility between genome-scale siRNA-based screens

    Science.gov (United States)

    Barrows, Nicholas J.; Le Sommer, Caroline; Garcia-Blanco, Mariano A.; Pearson, James L.

    2011-01-01

    RNA interference-based screening is a powerful new genomic technology which addresses gene function en masse. To evaluate factors influencing hit list composition and reproducibility, we performed two identically designed small interfering RNA (siRNA)-based, whole genome screens for host factors supporting yellow fever virus infection. These screens represent two separate experiments completed five months apart and allow the direct assessment of the reproducibility of a given siRNA technology when performed in the same environment. Candidate hit lists generated by sum rank, median absolute deviation, z-score, and strictly standardized mean difference were compared within and between whole genome screens. Application of these analysis methodologies within a single screening dataset using a fixed threshold equivalent to a p-value ≤ 0.001 resulted in hit lists ranging from 82 to 1,140 members and highlighted the tremendous impact analysis methodology has on hit list composition. Intra- and inter-screen reproducibility was significantly influenced by the analysis methodology and ranged from 32% to 99%. This study also highlighted the power of testing at least two independent siRNAs for each gene product in primary screens. To facilitate validation we conclude by suggesting methods to reduce false discovery at the primary screening stage. In this study we present the first comprehensive comparison of multiple analysis strategies, and demonstrate the impact of the analysis methodology on the composition of the “hit list”. Therefore, we propose that the entire dataset derived from functional genome-scale screens, especially if publicly funded, should be made available as is done with data derived from gene expression and genome-wide association studies. PMID:20625183

  3. 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. PMID:27679641

  4. Kinetic model of metabolic network for xiamenmycin biosynthetic optimisation.

    Science.gov (United States)

    Xu, Min-juan; Chen, Yong-cong; Xu, Jun; Ao, Ping; Zhu, Xiao-mei

    2016-02-01

    Xiamenmycins, a series of prenylated benzopyran compounds with anti-fibrotic bioactivities, were isolated from a mangrove-derived Streptomyces xiamenensis. To fulfil the requirements of pharmaceutical investigations, a high production of xiamenmycin is needed. In this study, the authors present a kinetic metabolic model to evaluate fluxes in an engineered Streptomyces lividans with xiamenmycin-oriented genetic modification based on generic enzymatic rate equations and stability constraints. Lyapunov function was used for a viability optimisation. From their kinetic model, the flux distributions for the engineered S. lividans fed on glucose and glycerol as carbon sources were calculated. They found that if the bacterium can utilise glucose simultaneously with glycerol, xiamenmycin production can be enhanced by 40% theoretically, while maintaining the same growth rate. Glycerol may increase the flux for phosphoenolpyruvate synthesis without interfering citric acid cycle. They therefore believe this study demonstrates a possible new direction for bioengineering of S. lividans.

  5. Plasticity of metabolic networks and the evolution of C4 photosynthesis

    Science.gov (United States)

    Bogart, Eli; Myers, Chris

    2012-02-01

    Over 50 groups of plants have independently developed a common mechanism (C4 photosynthesis) for increasing the efficiency of photosynthetic carbon dioxide assimilation. Understanding the high degree of evolvability of the C4 system could offer useful guidance for attempts to introduce it artificially to other plants. Previously, the nonlinear relationship between carbon dioxide levels and rates of carbon assimilation and photorespiration has prevented the application of genome-scale metabolic models to the problem of the evolution of the pathway. We apply a nonlinear optimization method to find feasible flux distributions in a plant metabolic model, allowing us to explore the plasticity of the metabolic network and characterize the fitness landscape of the transition from C3 to C4 photosynthesis.

  6. Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli

    Science.gov (United States)

    De Martino, Daniele; Capuani, Fabrizio; De Martino, Andrea

    2016-06-01

    The solution space of genome-scale models of cellular metabolism provides a map between physically viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the corresponding growth rates. By sampling the solution space of E. coli's metabolic network, we show that empirical growth rate distributions recently obtained in experiments at single-cell resolution can be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of a large bacterial population that captures this trade-off. The scaling relationships observed in experiments encode, in such frameworks, for the same distance from the maximum achievable growth rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being grounded on genome-scale metabolic network reconstructions, these results allow for multiple implications and extensions in spite of the underlying conceptual simplicity.

  7. Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts.

    Science.gov (United States)

    Crown, Scott B; Kelleher, Joanne K; Rouf, Rosanne; Muoio, Deborah M; Antoniewicz, Maciek R

    2016-10-01

    In many forms of cardiomyopathy, alterations in energy substrate metabolism play a key role in disease pathogenesis. Stable isotope tracing in rodent heart perfusion systems can be used to determine cardiac metabolic fluxes, namely those relative fluxes that contribute to pyruvate, the acetyl-CoA pool, and pyruvate anaplerosis, which are critical to cardiac homeostasis. Methods have previously been developed to interrogate these relative fluxes using isotopomer enrichments of measured metabolites and algebraic equations to determine a predefined metabolic flux model. However, this approach is exquisitely sensitive to measurement error, thus precluding accurate relative flux parameter determination. In this study, we applied a novel mathematical approach to determine relative cardiac metabolic fluxes using (13)C-metabolic flux analysis ((13)C-MFA) aided by multiple tracer experiments and integrated data analysis. Using (13)C-MFA, we validated a metabolic network model to explain myocardial energy substrate metabolism. Four different (13)C-labeled substrates were queried (i.e., glucose, lactate, pyruvate, and oleate) based on a previously published study. We integrated the analysis of the complete set of isotopomer data gathered from these mouse heart perfusion experiments into a single comprehensive network model that delineates substrate contributions to both pyruvate and acetyl-CoA pools at a greater resolution than that offered by traditional methods using algebraic equations. To our knowledge, this is the first rigorous application of (13)C-MFA to interrogate data from multiple tracer experiments in the perfused heart. We anticipate that this approach can be used widely to study energy substrate metabolism in this and other similar biological systems. Copyright © 2016 the American Physiological Society.

  8. Flux balance analysis of plant metabolism: the effect of biomass composition and model structure on model predictions

    Directory of Open Access Journals (Sweden)

    Huili eYuan

    2016-04-01

    Full Text Available The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA. Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modelling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models.

  9. 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.

  10. Constraint based modeling in R using metabolic reconstruction databases

    NARCIS (Netherlands)

    Gavai, A.K.; Hettinga, H.; Leunissen, J.A.M.

    2015-01-01

    This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fl

  11. 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

  12. Hydroxytyrosol ameliorates metabolic, cardiovascular and liver changes in a rat model of diet-induced metabolic syndrome: Pharmacological and metabolism-based investigation.

    Science.gov (United States)

    Poudyal, Hemant; Lemonakis, Nikolaos; Efentakis, Panagiotis; Gikas, Evangelos; Halabalaki, Maria; Andreadou, Ioanna; Skaltsounis, Leandros; Brown, Lindsay

    2017-03-01

    Metabolic syndrome is a clustering of interrelated risk factors for cardiovascular disease and diabetes. The Mediterranean diet has been proposed as an important dietary pattern to confer cardioprotection by attenuating risk factors of metabolic syndrome. Hydroxytyrosol (HT) is present in olive fruit and oil, which are basic constituents of the Mediterranean diet. In this study, we have shown that treatment with HT (20mg/kg/d for 8 weeks) decreased adiposity, improved impaired glucose and insulin tolerance, improved endothelial function with lower systolic blood pressure, decreased left ventricular fibrosis and resultant diastolic stiffness and reduced markers of liver damage in a diet-induced rat model of metabolic syndrome. These results were accompanied by reduced infiltration of monocytes/macrophages into the heart with reduced biomarkers of oxidative stress. Furthermore, in an HRMS-based metabolism study of HT, we have identified 24 HT phase I and II metabolites, six of them being over-produced in high-starch, low-fat diet fed rats treated with HT compared to obese rats on high-carbohydrate, high-fat diet. These results provide direct evidence for cardioprotective effects of hydroxytyrosol by attenuation of metabolic risk factors. The implications of altered metabolism of HT in high-carbohydrate, high-fat diet fed obese rats warrant further investigation.

  13. Flux balance analysis of cyanobacterial metabolism: the metabolic network of Synechocystis sp. PCC 6803.

    Directory of Open Access Journals (Sweden)

    Henning Knoop

    Full Text Available Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism.

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

    Science.gov (United States)

    Knoop, Henning; Gründel, Marianne; Zilliges, Yvonne; Lehmann, Robert; Hoffmann, Sabrina; Lockau, Wolfgang; Steuer, Ralf

    2013-01-01

    Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism. PMID:23843751

  15. Maximal sum of metabolic exchange fluxes outperforms biomass yield as a predictor of growth rate of microorganisms.

    Directory of Open Access Journals (Sweden)

    Raphy Zarecki

    Full Text Available Growth rate has long been considered one of the most valuable phenotypes that can be measured in cells. Aside from being highly accessible and informative in laboratory cultures, maximal growth rate is often a prime determinant of cellular fitness, and predicting phenotypes that underlie fitness is key to both understanding and manipulating life. Despite this, current methods for predicting microbial fitness typically focus on yields [e.g., predictions of biomass yield using GEnome-scale metabolic Models (GEMs] or notably require many empirical kinetic constants or substrate uptake rates, which render these methods ineffective in cases where fitness derives most directly from growth rate. Here we present a new method for predicting cellular growth rate, termed SUMEX, which does not require any empirical variables apart from a metabolic network (i.e., a GEM and the growth medium. SUMEX is calculated by maximizing the SUM of molar EXchange fluxes (hence SUMEX in a genome-scale metabolic model. SUMEX successfully predicts relative microbial growth rates across species, environments, and genetic conditions, outperforming traditional cellular objectives (most notably, the convention assuming biomass maximization. The success of SUMEX suggests that the ability of a cell to catabolize substrates and produce a strong proton gradient enables fast cell growth. Easily applicable heuristics for predicting growth rate, such as what we demonstrate with SUMEX, may contribute to numerous medical and biotechnological goals, ranging from the engineering of faster-growing industrial strains, modeling of mixed ecological communities, and the inhibition of cancer growth.

  16. Maximal sum of metabolic exchange fluxes outperforms biomass yield as a predictor of growth rate of microorganisms.

    Science.gov (United States)

    Zarecki, Raphy; Oberhardt, Matthew A; Yizhak, Keren; Wagner, Allon; Shtifman Segal, Ella; Freilich, Shiri; Henry, Christopher S; Gophna, Uri; Ruppin, Eytan

    2014-01-01

    Growth rate has long been considered one of the most valuable phenotypes that can be measured in cells. Aside from being highly accessible and informative in laboratory cultures, maximal growth rate is often a prime determinant of cellular fitness, and predicting phenotypes that underlie fitness is key to both understanding and manipulating life. Despite this, current methods for predicting microbial fitness typically focus on yields [e.g., predictions of biomass yield using GEnome-scale metabolic Models (GEMs)] or notably require many empirical kinetic constants or substrate uptake rates, which render these methods ineffective in cases where fitness derives most directly from growth rate. Here we present a new method for predicting cellular growth rate, termed SUMEX, which does not require any empirical variables apart from a metabolic network (i.e., a GEM) and the growth medium. SUMEX is calculated by maximizing the SUM of molar EXchange fluxes (hence SUMEX) in a genome-scale metabolic model. SUMEX successfully predicts relative microbial growth rates across species, environments, and genetic conditions, outperforming traditional cellular objectives (most notably, the convention assuming biomass maximization). The success of SUMEX suggests that the ability of a cell to catabolize substrates and produce a strong proton gradient enables fast cell growth. Easily applicable heuristics for predicting growth rate, such as what we demonstrate with SUMEX, may contribute to numerous medical and biotechnological goals, ranging from the engineering of faster-growing industrial strains, modeling of mixed ecological communities, and the inhibition of cancer growth.

  17. Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica

    DEFF Research Database (Denmark)

    Kerkhoven, Eduard J.; Pomraning, Kyle R.; Baker, Scott E.

    2016-01-01

    cultures. We first reconstructed a genome-scale metabolic model and used this for integrative analysis of multilevel omics data. Metabolite profiling and lipidomics was used to quantify the cellular physiology, while regulatory changes were measured using RNAseq. Analysis of the data showed that lipid......Yarrowia lipolytica is a promising microbial cell factory for the production of lipids to be used as fuels and chemicals, but there are few studies on regulation of its metabolism. Here we performed the first integrated data analysis of Y. lipolytica grown in carbon and nitrogen limited chemostat...... accumulation in Y. lipolytica does not involve transcriptional regulation of lipid metabolism but is associated with regulation of amino-acid biosynthesis, resulting in redirection of carbon flux during nitrogen limitation from amino acids to lipids. Lipid accumulation in Y. lipolytica at nitrogen limitation...

  18. Upregulated expression of brain enzymatic markers of arachidonic and docosahexaenoic acid metabolism in a rat model of the metabolic syndrome

    Directory of Open Access Journals (Sweden)

    Taha Ameer Y

    2012-10-01

    Full Text Available Abstract Background In animal models, the metabolic syndrome elicits a cerebral response characterized by altered phospholipid and unesterified fatty acid concentrations and increases in pro-apoptotic inflammatory mediators that may cause synaptic loss and cognitive impairment. We hypothesized that these changes are associated with phospholipase (PLA2 enzymes that regulate arachidonic (AA, 20:4n-6 and docosahexaenoic (DHA, 22:6n-6 acid metabolism, major polyunsaturated fatty acids in brain. Male Wistar rats were fed a control or high-sucrose diet for 8 weeks. Brains were assayed for markers of AA metabolism (calcium-dependent cytosolic cPLA2 IVA and cyclooxygenases, DHA metabolism (calcium-independent iPLA2 VIA and lipoxygenases, brain-derived neurotrophic factor (BDNF, and synaptic integrity (drebrin and synaptophysin. Lipid concentrations were measured in brains subjected to high-energy microwave fixation. Results The high-sucrose compared with control diet induced insulin resistance, and increased phosphorylated-cPLA2 protein, cPLA2 and iPLA2 activity and 12-lipoxygenase mRNA, but decreased BDNF mRNA and protein, and drebrin mRNA. The concentration of several n-6 fatty acids in ethanolamine glycerophospholipids and lysophosphatidylcholine was increased, as was unesterified AA concentration. Eicosanoid concentrations (prostaglandin E2, thromboxane B2 and leukotriene B4 did not change. Conclusion These findings show upregulated brain AA and DHA metabolism and reduced BDNF and drebrin, but no changes in eicosanoids, in an animal model of the metabolic syndrome. These changes might contribute to altered synaptic plasticity and cognitive impairment in rats and humans with the metabolic syndrome.

  19. Impact of Stoichiometry Representation on Simulation of Genotype-Phenotype Relationships in Metabolic Networks

    Science.gov (United States)

    Brochado, Ana Rita; Andrejev, Sergej; Maranas, Costas D.; Patil, Kiran R.

    2012-01-01

    Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in Saccharomyces cerevisiae. PMID:23133362

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

    Science.gov (United States)

    Brochado, Ana Rita; Andrejev, Sergej; Maranas, Costas D; Patil, Kiran R

    2012-01-01

    Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in Saccharomyces cerevisiae.

  1. Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation

    Energy Technology Data Exchange (ETDEWEB)

    Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha; Theriot, Casey M.; Young, Vincent B.; Jansson, Janet K.; Fredricks, David N.; Borenstein, Elhanan; Sanchez, Laura M.

    2015-12-22

    ABSTRACT

    Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in

  2. Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation.

    Science.gov (United States)

    Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha; Theriot, Casey M; Young, Vincent B; Jansson, Janet K; Fredricks, David N; Borenstein, Elhanan

    2016-01-01

    Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites' abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. Studies

  3. Considerations on pig models for appetite, metabolic syndrome and obese type 2 diabetes: Form food intake to metabolic disease

    NARCIS (Netherlands)

    Koopmans, S.J.; Schuurman, T.

    2015-01-01

    (Mini)pigs have proven to be a valuable animal model in nutritional, metabolic and cardiovascular research and in some other biomedical research areas (toxicology, neurobiology). The large resemblance of (neuro)anatomy, the gastro-intestinal tract, body size, body composition, and the omnivorous foo

  4. Considerations on pig models for appetite, metabolic syndrome and obese type 2 diabetes: Form food intake to metabolic disease

    NARCIS (Netherlands)

    Koopmans, S.J.; Schuurman, T.

    2015-01-01

    (Mini)pigs have proven to be a valuable animal model in nutritional, metabolic and cardiovascular research and in some other biomedical research areas (toxicology, neurobiology). The large resemblance of (neuro)anatomy, the gastro-intestinal tract, body size, body composition, and the omnivorous

  5. Modeling Central Carbon Metabolic Processes in Soil Microbial Communities: Comparing Measured With Modeled

    Science.gov (United States)

    Dijkstra, P.; Fairbanks, D.; Miller, E.; Salpas, E.; Hagerty, S.

    2013-12-01

    Understanding the mechanisms regulating C cycling is hindered by our inability to directly observe and measure the biochemical processes of glycolysis, pentose phosphate pathway, and TCA cycle in intact and complex microbial communities. Position-specific 13C labeled metabolic tracer probing is proposed as a new way to study microbial community energy production, biosynthesis, C use efficiency (the proportion of substrate incorporated into microbial biomass), and enables the quantification of C fluxes through the central C metabolic network processes (Dijkstra et al 2011a,b). We determined the 13CO2 production from U-13C, 1-13C, 2-13C, 3-13C, 4-13C, 5-13C, and 6-13C labeled glucose and 1-13C and 2,3-13C pyruvate in parallel incubations in three soils along an elevation gradient. Qualitative and quantitative interpretation of the results indicate a high pentose phosphate pathway activity in soils. Agreement between modeled and measured CO2 production rates for the six C-atoms of 13C-labeled glucose indicate that the metabolic model used is appropriate for soil community processes, but that improvements can be made. These labeling and modeling techniques may improve our ability to analyze the biochemistry and (eco)physiology of intact microbial communities. Dijkstra, P., Blankinship, J.C., Selmants, P.C., Hart, S.C., Koch, G.W., Schwartz, E., Hungate, B.A., 2011a. Probing C flux patterns of soil microbial metabolic networks using parallel position-specific tracer labeling. Soil Biology & Biochemistry 43, 126-132. Dijkstra, P., Dalder, J.J., Selmants, P.C., Hart, S.C., Koch, G.W., Schwartz, E., Hungate, B.A., 2011b. Modeling soil metabolic processes using isotopologue pairs of position-specific 13C-labeled glucose and pyruvate. Soil Biology & Biochemistry 43, 1848-1857.

  6. Metabolic Engineering and Modeling of Metabolic Pathways to Improve Hydrogen Production by Photosynthetic Bacteria

    Energy Technology Data Exchange (ETDEWEB)

    Jiao, Y. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Navid, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2014-12-19

    traits act as the biocatalysts of the process designed to both enhance the system efficiency of CO2 fixation and the net hydrogen production rate. Additionally we applied metabolic engineering approaches guided by computational modeling for the chosen model microorganisms to enable efficient hydrogen production.

  7. Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide.

    Science.gov (United States)

    Orth, Jeffrey D; Fleming, R M T; Palsson, Bernhard Ø

    2010-09-01

    Biochemical network reconstructions have become popular tools in systems biology. Metabolicnetwork reconstructions are biochemically, genetically, and genomically (BiGG) structured databases of biochemical reactions and metabolites. They contain information such as exact reaction stoichiometry, reaction reversibility, and the relationships between genes, proteins, and reactions. Network reconstructions have been used extensively to study the phenotypic behavior of wild-type and mutant stains under a variety of conditions, linking genotypes with phenotypes. Such phenotypic simulations have allowed for the prediction of growth after genetic manipulations, prediction of growth phenotypes after adaptive evolution, and prediction of essential genes. Additionally, because network reconstructions are organism specific, they can be used to understand differences between organisms of species in a functional context.There are different types of reconstructions representing various types of biological networks (metabolic, regulatory, transcription/translation). This chapter serves as an introduction to metabolic and regulatory network reconstructions and models and gives a complete description of the core Escherichia coli metabolic model. This model can be analyzed in any computational format (such as MATLAB or Mathematica) based on the information given in this chapter. The core E. coli model is a small-scale model that can be used for educational purposes. It is meant to be used by senior undergraduate and first-year graduate students learning about constraint-based modeling and systems biology. This model has enough reactions and pathways to enable interesting and insightful calculations, but it is also simple enough that the results of such calculations can be understoodeasily.

  8. Dynamic Modelling under Uncertainty : The Case of Trypanosoma brucei Energy Metabolism

    NARCIS (Netherlands)

    Achcar, Fiona; Kerkhoven, Eduard J.; Bakker, Barbara M.; Barrett, Michael P.; Breitling, Rainer; Papin, Jason A.

    2012-01-01

    Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabol

  9. A new mouse model of metabolic syndrome and associated complications.

    Science.gov (United States)

    Wang, Yun; Zheng, Yue; Nishina, Patsy M; Naggert, Jürgen K

    2009-07-01

    Metabolic syndrome (MS) encompasses a clustering of risk factors for cardiovascular disease, including obesity, insulin resistance, and dyslipidemia. We characterized a new mouse model carrying a dominant mutation, C57BL/6J-Nmf15/+ (B6-Nmf15/+), which develops additional complications of MS such as adipose tissue inflammation and cardiomyopathy. A backcross was used to genetically map the Nmf15 locus. Mice were examined in the comprehensive laboratory animal monitoring system, and dual energy X-ray absorptiometry