Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0
Jan Schellenberger,Richard Que,Ronan M. T. Fleming,Ines Thiele,Jeffrey D. Orth,Adam M. Feist,Daniel C. Zielinski,Aarash Bordbar,Nathan E. Lewis,Sorena Rahmanian,Joseph Kang,Daniel R. Hyduke,Bernhard O. Palsson +12 more
TLDR
The constraint-based reconstruction and analysis toolbox as discussed by the authors is a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraintbased approach and allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules.Abstract:
The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.read more
Citations
More filters
Journal ArticleDOI
Metabolic Network Model of a Human Oral Pathogen
TL;DR: A stoichiometric model is built that encompasses 679 metabolic reactions of Porphyromonas gingivalis, a gram-negative anaerobe that is endemic in the human population and largely responsible for adult periodontitis and could prove useful in evaluating the oral microbiome dynamics and in the development of novel biomedical applications.
Journal ArticleDOI
Functional cooperation of the glycine synthase-reductase and Wood-Ljungdahl pathways for autotrophic growth of Clostridium drakei.
Yoseb Song,Jin Soo Lee,Jongoh Shin,Gyu Min Lee,Sangrak Jin,Seulgi Kang,Jung-Kul Lee,Dong Rip Kim,Eun Yeol Lee,Sun Chang Kim,Suhyung Cho,Donghyuk Kim,Byung-Kwan Cho +12 more
TL;DR: It is discovered that the WLP and the glycine synthase pathway are functionally interconnected to fix CO2, subsequently converting CO2 into acetyl-CoA, acetyl -phosphate, and serine, which is a unique coutilization of the pathways under autotrophic conditions in acetogens.
Journal ArticleDOI
The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models.
TL;DR: A model formulation that efficiently simulates thermodynamic-compliant fluxes, enzyme and mRNA concentration levels, allowing omics integration and broad analysis of in silico cellular physiology.
Journal ArticleDOI
Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models.
Meaziane Aite,Marie Chevallier,Clémence Frioux,Camille Trottier,Jeanne Got,María Paz Cortés,Sebastián N. Mendoza,Gregory Carrier,Olivier Dameron,Nicolas Guillaudeux,Mauricio Latorre,Nicolás Loira,Gabriel V. Markov,Alejandro Maass,Anne Siegel +14 more
TL;DR: This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines, and illustrates how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae.
Journal ArticleDOI
Metabolic modelling in a dynamic evolutionary framework predicts adaptive diversification of bacteria in a long-term evolution experiment
Tobias Großkopf,Jessika Consuegra,Jessika Consuegra,Joël Gaffé,Joël Gaffé,John C. Willison,John C. Willison,John C. Willison,Richard E. Lenski,Orkun S. Soyer,Dominique Schneider,Dominique Schneider +11 more
TL;DR: The evoFBA framework represents a promising new way to model biochemical evolution, one that can generate testable predictions about evolutionary and ecosystem-level outcomes.
References
More filters
Journal ArticleDOI
Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks
Paul Shannon,Andrew Markiel,Owen Ozier,Nitin S. Baliga,Jonathan T. Wang,Daniel Ramage,Nada Amin,Benno Schwikowski,Trey Ideker +8 more
TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Journal ArticleDOI
KEGG: Kyoto Encyclopedia of Genes and Genomes
Minoru Kanehisa,Susumu Goto +1 more
TL;DR: The Kyoto Encyclopedia of Genes and Genomes (KEGG) as discussed by the authors is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules.
Journal ArticleDOI
The KEGG resource for deciphering the genome
TL;DR: A knowledge-based approach for network prediction is developed, which is to predict, given a complete set of genes in the genome, the protein interaction networks that are responsible for various cellular processes.
Journal ArticleDOI
The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.
Michael Hucka,Andrew Finney,Herbert M. Sauro,Hamid Bolouri,Hamid Bolouri,John Doyle,Hiroaki Kitano,Adam P. Arkin,Benjamin Bornstein,Dennis Bray,Athel Cornish-Bowden,Autumn A. Cuellar,S. Dronov,E. D. Gilles,Martin Ginkel,V. Gor,Igor Goryanin,W. J. Hedley,T. C. Hodgman,J.-H.S. Hofmeyr,Peter Hunter,Nick Juty,J. L. Kasberger,Andreas Kremling,Ursula Kummer,N Le Novère,Leslie M. Loew,D. Lucio,Pedro Mendes,E. Minch,Eric Mjolsness,Yoichi Nakayama,Melanie R. Nelson,Poul M. F. Nielsen,T. Sakurada,James C. Schaff,Bruce E. Shapiro,Thomas S. Shimizu,H. D. Spence,Jörg Stelling,Koichi Takahashi,Masaru Tomita,John Wagner,J. Wang +43 more
TL;DR: This work summarizes the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks, a software-independent language for describing models common to research in many areas of computational biology.
Journal ArticleDOI
Global Mapping of the Yeast Genetic Interaction Network
Amy Hin Yan Tong,Guillaume Lesage,Gary D. Bader,Huiming Ding,Hong Xu,Xiaofeng Xin,James D. Young,Gabriel F. Berriz,Renee L. Brost,Michael Chang,Yiqun Chen,Xin Cheng,Gordon Chua,Helena Friesen,Debra S. Goldberg,Jennifer Haynes,Christine Humphries,Grace He,Shamiza Hussein,Lizhu Ke,Nevan J. Krogan,Zhijian Li,Joshua N. Levinson,Hong Lu,Patrice Menard,Christella Munyana,Ainslie B. Parsons,Owen Ryan,Raffi Tonikian,Tania Michelle Roberts,Anne-Marie Sdicu,Jesse Shapiro,Bilal N. Sheikh,Bernhard Suter,Sharyl L. Wong,Lan V. Zhang,Hongwei Zhu,Christopher G. Burd,Sean Munro,Chris Sander,Jasper Rine,Jack Greenblatt,Matthias Peter,Anthony Bretscher,Graham Bell,Frederick P. Roth,Grant W. Brown,Brenda J. Andrews,Howard Bussey,Charles Boone +49 more
TL;DR: Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.