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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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Citations
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Journal ArticleDOI

Shrinking the metabolic solution space using experimental datasets.

TL;DR: This mini-review provides a summary of the data-driven computational approaches that are available for reducing the solution space and thereby improve predictions of intracellular fluxes by constraint-based models.
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Use of genome-scale metabolic models for understanding microbial physiology.

TL;DR: The requirement for having detailed physiological insight in order to exploit microorganisms for production of fuels, chemicals and pharmaceuticals is discussed and the reconstruction process of genome‐scale metabolic models and different algorithms that can be used to apply these models to gain improved insight into microbial physiology are described.
Journal ArticleDOI

Computational systems biology and in silico modeling of the human microbiome

TL;DR: The pressing need for the development of predictive system- level models and for a system-level understanding of the microbiome is highlighted, and potential computational frameworks for metagenomic-based modeling of the microbiota at the cellular, ecological and supra-organismal level are discussed.
Journal ArticleDOI

Standardizing biomass reactions and ensuring complete mass balance in genome-scale metabolic models.

TL;DR: This work introduced a systematic procedure for checking the biomass weight and ensuring complete mass balance of a model and proposes the presented procedure as a standard practice for metabolic reconstructions.
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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