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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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Extensive in vivo resilience of persistent Salmonella.

TL;DR: Evaluation of twelve metabolic defects revealed dramatically different requirements for Salmonella during persistency as compared to acute infections, suggesting that this pathway might contain suitable targets for antimicrobial chemotherapy of chronic infection.
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Reconstruction of a charge balanced genome-scale metabolic model to study the energy-uncoupled growth of Zymomonas mobilis ZM1.

TL;DR: The model presents a much better prediction for biomass and ethanol concentrations in a batch culture by using dynamic flux balance analysis compared with the two previous genome-scale metabolic models and was used to study the energy-uncoupled growth of Z. mobilis and to predict its effect on flux distribution in the central metabolism.
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Metabolic network model guided engineering ethylmalonyl-CoA pathway to improve ascomycin production in Streptomyces hygroscopicus var. ascomyceticus

TL;DR: The successful constructing and experimental validation of the metabolic model of S. hygroscopicus var.
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Comprehensive reconstruction and evaluation of Pichia pastoris genome-scale metabolic model that accounts for 1243 ORFs

TL;DR: iRY1243 is an upgraded P. pastoris genome-scale metabolic model with significant improvements in the metabolic coverage and prediction ability, and thus it will be a potential platform for further systematic investigation of P. Pastoris metabolism.
Journal ArticleDOI

Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond

TL;DR: This contribution proposes to improve the predictive power of metabolic models by switching from gene-protein-re reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements.
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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