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Open AccessJournal ArticleDOI

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

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.

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Reconstruction and analysis of a genome-scale metabolic model of the vitamin C producing industrial strain Ketogulonicigenium vulgare WSH-001.

TL;DR: Phenotypic features, such as the defect in sulfate metabolism hampering the syntheses of L-cysteine, L-methionine, coenzyme A (CoA), and glutathione, were investigated and provided an explanation for the poor growth of K. vulgare in monoculture.
Journal ArticleDOI

Identification of novel knockout targets for improving terpenoids biosynthesis in Saccharomyces cerevisiae.

TL;DR: The yield of amorphadiene produced by most single mutants was 8–10-fold greater compared to the wild type, indicating that the knockout sites can be engineered to promote the synthesis of exogenous terpenoids.
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MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

TL;DR: A user-friendly software framework, built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis, and shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture.
Journal ArticleDOI

Predicting outcomes of steady-state 13C isotope tracing experiments using Monte Carlo sampling

TL;DR: The dimensionality of experimental data was found to be considerably less than anticipated, suggesting that effectiveness of 13C experiments for determining reaction fluxes across a large-scale metabolic network is less than previously believed.
Journal ArticleDOI

A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis

TL;DR: A mathematical framework to simulate the effects on the growth of a pathogen when enzymes in its metabolic pathways are inhibited is developed and quantitatively reproduced the experimentally measured dose-response curves.
References
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Journal ArticleDOI

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

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.
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KEGG: Kyoto Encyclopedia of Genes and Genomes

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.
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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.
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The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

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.
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