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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ll-ACHRB: a scalable algorithm for sampling the feasible solution space of metabolic networks.
Pedro A. Saa,Lars K. Nielsen +1 more
TL;DR: An efficient sampling algorithm called loopless Artificially Centered Hit-and-Run on a Box (ll-ACHRB) that shows overall better performance than current strategies to generate feasible flux samples across several models and demonstrates that a failure to eliminate unfeasible loops greatly affects sample statistics, in particular the correlation structure.
Journal ArticleDOI
Interrogation of global mutagenesis data with a genome scale model of Neisseria meningitidis to assess gene fitness in vitro and in sera
TL;DR: This study describes the application of a genome scale transposon library combined with an experimentally validated genome-scale metabolic network of N. meningitidis to identify essential genes and provide novel insight into the pathogen's metabolism both in vitro and during infection.
Journal ArticleDOI
Metabolic model reconstruction and analysis of an artificial microbial ecosystem for vitamin C production.
TL;DR: This 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.
Journal ArticleDOI
Metabolome- and genome-scale model analyses for engineering of Aureobasidium pullulans to enhance polymalic acid and malic acid production from sugarcane molasses
TL;DR: It is shown that integrated metabolome- and genome-scale model analyses were an effective approach for engineering the metabolic node for PMA synthesis, and also developed an economical and green process forPMA and MA production from renewable biomass feedstocks.
Journal ArticleDOI
Impact of Stoichiometry Representation on Simulation of Genotype-Phenotype Relationships in Metabolic Networks
TL;DR: A new method is proposed, 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.
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