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
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Metabolomic and flux-balance analysis of age-related decline of hypoxia tolerance in Drosophila muscle tissue.
Laurence Coquin,Jacob D. Feala,Andrew D. McCulloch,Giovanni Paternostro,Giovanni Paternostro +4 more
TL;DR: It is shown that hypoxia tolerance degrades with age in post‐hypoxic recovery of whole‐body movement, heart rate and ATP content in Drosophila melanogaster flies.
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An introduction to the maximum entropy approach and its application to inference problems in biology.
TL;DR: The basic elements of the maximum entropy principle are reviewed, starting from the notion of 'entropy', and its usefulness for the analysis of biological systems is described, focusing specifically on the problem of reconstructing gene interaction networks from expression data.
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Functional and Computational Genomics Reveal Unprecedented Flexibility in Stage-Specific Toxoplasma Metabolism.
Aarti Krishnan,Joachim Kloehn,Matteo Lunghi,Anush Chiappino-Pepe,Benjamin S. Waldman,Damien Nicolas,Emmanuel Varesio,Adrian B. Hehl,Sebastian Lourido,Vassily Hatzimanikatis,Dominique Soldati-Favre +10 more
TL;DR: A genome-scale metabolic model for the fast-replicating tachyzoite stage of Toxoplasma gondii, harmonized with experimentally observed phenotypes, leads to a deeper understanding of the parasite's biology, opening avenues for the development of therapeutic intervention against apicomplexans.
Journal ArticleDOI
Stoichiometric Representation of Gene-Protein-Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction.
TL;DR: The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification, and automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.
Journal ArticleDOI
Do genome-scale models need exact solvers or clearer standards?
Ali Ebrahim,Eivind Almaas,Eugen Bauer,Aarash Bordbar,Anthony P. Burgard,Roger L. Chang,Andreas Dräger,Andreas Dräger,Imam Famili,Adam M. Feist,Ronan M. T. Fleming,Stephen S. Fong,Vassily Hatzimanikatis,Markus J. Herrgård,Allen Holder,Michael Hucka,Daniel R. Hyduke,Neema Jamshidi,Neema Jamshidi,Sang Yup Lee,Sang Yup Lee,Nicolas Le Novère,Joshua A. Lerman,Nathan E. Lewis,Ding Ma,Radhakrishnan Mahadevan,Costas D. Maranas,Harish Nagarajan,Ali Navid,Jens Nielsen,Jens Nielsen,Lars K. Nielsen,Juan Nogales,Alberto Noronha,Csaba Pál,Bernhard O. Palsson,Jason A. Papin,Kiran Raosaheb Patil,Nathan D. Price,Jennifer L. Reed,Michael A. Saunders,Ryan S. Senger,Nikolaus Sonnenschein,Yuekai Sun,Ines Thiele +44 more
TL;DR: It is demonstrated that the claims made by Chindelevitch et al (2014) stem from an incorrect parsing of models from files rather than actual problems with numerical error or COBRA computations.
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