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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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What is flux balance analysis

TL;DR: This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.
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A protocol for generating a high-quality genome-scale metabolic reconstruction.

TL;DR: This protocol provides a helpful manual for all stages of the reconstruction process and presents a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction.
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A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.

TL;DR: An updated genome‐scale reconstruction of the metabolic network in Escherichia coli K‐12 MG1655 with increased scope and computational capability is presented, expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ROS

TL;DR: It is shown that selective accumulation of the citric acid cycle intermediate succinate is a universal metabolic signature of ischaemia in a range of tissues and is responsible for mitochondrial ROS production during reperfusion, and a new pathway for metabolic control of ROS production in vivo is revealed.
References
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Journal ArticleDOI

LibSBML: an API library for SBML.

TL;DR: LibSBML is an application programming interface library for reading, writing, manipulating and validating content expressed in the Systems Biology Markup Language (SBML) format that provides language bindings for Common Lisp, Java, Python, Perl, MATLAB and Octave.
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Towards multidimensional genome annotation

TL;DR: All four levels of genome annotation are discussed, with specific emphasis on two-dimensional annotation methods, and the study of changes in genome sequences that occur during adaptive evolution is studied.
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Comparison of network-based pathway analysis methods

TL;DR: The relationship between elementary modes and extreme pathways of previously published metabolic reconstructions of the human red blood cell and the human pathogen Helicobacter pylori are compared and contrasted.
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Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes.

TL;DR: It is shown that strains of Escherichia coli carrying a deletion of a single metabolic gene increase their growth rates during adaptive evolution and that the endpoint growth rates can be predicted computationally in 39 of 50 strains tested.
Journal ArticleDOI

Optimization based automated curation of metabolic reconstructions

TL;DR: This work proposes systematic methods to identify and fill gaps in genome-scale metabolic reconstructions and demonstrates this procedure for the genome- scale reconstruction of Escherichia coli and also Saccharomyces cerevisiae wherein compartmentalization of intra-cellular reactions results in a more complex topology of the metabolic network.
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