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
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Aerobic fermentation of D-glucose by an evolved cytochrome oxidase-deficient Escherichia coli strain.
TL;DR: It is demonstrated that three independently adaptively evolved ECOM3 populations acquired different phenotypes: one produced lactate as a sole fermentation product, while the other two strains exhibited a mixed-acid fermentation under oxic growth conditions with lactate remaining as the major product.
Book ChapterDOI
Genome-scale metabolic models: reconstruction and analysis.
Gino Baart,Dirk E. Martens +1 more
TL;DR: A stoichiometric model of metabolism is proposed that can be used for detailed analysis of the metabolic potential of the organism using constraint-based modeling approaches and hence is valuable in understanding its metabolic capabilities.
Journal ArticleDOI
An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism
TL;DR: An in silico multi‐compartmental model of the central metabolism of the plant Brassica napus (Rapeseed), aiming to investigate the metabolic properties of the Brassicaceae family, successfully simulated seed growth during the stage of oil accumulation and provided insight, regarding certain aspects of network plasticity.
Journal ArticleDOI
Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia.
TL;DR: An in silico approach to create state-specific models based on readily available gene expression data to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia and predicts reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA) program.
Journal ArticleDOI
SurreyFBA: a command line tool and graphics user interface for constraint-based modeling of genome-scale metabolic reaction networks
TL;DR: The SurreyFBA, which provides constraint-based simulations and network map visualization in a free, stand-alone software, is presented, which is based on a command line interface to the GLPK solver distributed as binary and source code for the three major operating systems.
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TL;DR: Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.