Literature DB >> 16461408

Systematic analysis of conservation relations in Escherichia coli genome-scale metabolic network reveals novel growth media.

Marcin Imielinski1, Calin Belta, Harvey Rubin, Adam Halász.   

Abstract

A biochemical species is called producible in a constraints-based metabolic model if a feasible steady-state flux configuration exists that sustains its nonzero concentration during growth. Extreme semipositive conservation relations (ESCRs) are the simplest semipositive linear combinations of species concentrations that are invariant to all metabolic flux configurations. In this article, we outline a fundamental relationship between the ESCRs of a metabolic network and the producibility of a biochemical species under a nutrient media. We exploit this relationship in an algorithm that systematically enumerates all minimal nutrient sets that render an objective species weakly producible (i.e., producible in the absence of thermodynamic constraints) through a simple traversal of ESCRs. We apply our results to a recent genome scale model of Escherichia coli metabolism, in which we traverse the 51 anhydrous ESCRs of the metabolic network to determine all 928 minimal aqueous nutrient media that render biomass weakly producible. Applying irreversibility constraints, we find 287 of these 928 nutrient sets to be thermodynamically feasible. We also find that an additional 365 of these nutrient sets are thermodynamically feasible in the presence of oxygen. Since biomass producibility is commonly used as a surrogate for growth in genome scale metabolic models, our results represent testable hypotheses of alternate growth media derived from in silico analysis of the E. coli genome scale metabolic network.

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Year:  2006        PMID: 16461408      PMCID: PMC1414550          DOI: 10.1529/biophysj.105.069278

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  23 in total

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3.  Comparison of network-based pathway analysis methods.

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Authors:  Nathan D Price; Jennifer L Reed; Bernhard Ø Palsson
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7.  Investigating metabolite essentiality through genome-scale analysis of Escherichia coli production capabilities.

Authors:  Marcin Imieliński; Călin Belta; Adám Halász; Harvey Rubin
Journal:  Bioinformatics       Date:  2005-01-25       Impact factor: 6.937

8.  Elucidation and structural analysis of conserved pools for genome-scale metabolic reconstructions.

Authors:  Evgeni V Nikolaev; Anthony P Burgard; Costas D Maranas
Journal:  Biophys J       Date:  2004-10-15       Impact factor: 4.033

9.  The underlying pathway structure of biochemical reaction networks.

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Journal:  Proc Natl Acad Sci U S A       Date:  1998-04-14       Impact factor: 11.205

10.  Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data.

Authors:  Thuy D Vo; Harvey J Greenberg; Bernhard O Palsson
Journal:  J Biol Chem       Date:  2004-06-17       Impact factor: 5.157

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  12 in total

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3.  Enumeration of minimal stoichiometric precursor sets in metabolic networks.

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4.  Differential producibility analysis (DPA) of transcriptomic data with metabolic networks: deconstructing the metabolic response of M. tuberculosis.

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5.  Reaction networks as systems for resource allocation: a variational principle for their non-equilibrium steady states.

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Journal:  PLoS One       Date:  2012-07-16       Impact factor: 3.240

6.  A scalable algorithm to explore the Gibbs energy landscape of genome-scale metabolic networks.

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Journal:  PLoS Comput Biol       Date:  2012-06-21       Impact factor: 4.475

7.  Minimal cut sets and the use of failure modes in metabolic networks.

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8.  Identifying all moiety conservation laws in genome-scale metabolic networks.

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Review 9.  Genome-scale models of bacterial metabolism: reconstruction and applications.

Authors:  Maxime Durot; Pierre-Yves Bourguignon; Vincent Schachter
Journal:  FEMS Microbiol Rev       Date:  2008-12-03       Impact factor: 16.408

Review 10.  An introduction to the maximum entropy approach and its application to inference problems in biology.

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Journal:  Heliyon       Date:  2018-04-13
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