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A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.

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TLDR
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.
Abstract
An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

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References
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Journal ArticleDOI

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TL;DR: These mutants—the ‘Keio collection’—provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome‐wide testing of mutational effects in a common strain background, E. coli K‐12 BW25113.
Journal ArticleDOI

Energy conservation in chemotrophic anaerobic bacteria.

TL;DR: This article corrects the article on p. 100 in vol.
Journal ArticleDOI

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

TL;DR: 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.
Journal ArticleDOI

The Maintenance Energy of Bacteria in Growing Cultures

TL;DR: From the laws of growth, a simple relation between the maintenance requirement, the growth yield and the growth rate is derived and is shown to be in good agreement with the available data.
Journal ArticleDOI

Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation.

TL;DR: In this article, the authors examined the dynamics of evolutionary adaptation, the genetic bases of adaptation, tradeoffs and the environmental specificity of adaptation and the origin and evolutionary consequences of mutators.
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