Literature DB >> 15962336

How reliable are thermodynamic feasibility statements of biochemical pathways?

Thomas Maskow1, Urs von Stockar.   

Abstract

The driving force for organo- or lithotrophic growth as well as for each step in the metabolic network is the Gibbs reaction energy. For each enzymatic step it must be negative. Thermodynamics contributes therefore to the in-silico description of living systems. It may be used for assessing the feasibility of a given pathway because it provides a further constraint for those pathways which are feasible from the point of view of mass balance calculations (metabolic flux analysis) and the genetic potential of an organism. However, when this constraint was applied to lactic acid fermentation according to a method proposed by Mavrovouniotis (1993a, ISMB 93:273-283) it turned out that an unrealistically wide metabolite concentration range had to be assumed to make this well-known glycolytic pathway thermodynamically feasible. During a search for the reasons of this surprising result the insufficient consideration of the activity coefficients was identified as main cause. However, it is shown in the present contribution that the influence of the activity coefficients on Gibbs reaction energy can be easily taken into account based on the intracellular ionic strength. The uncertainty of the tabulated equilibrium constants and of the apparent standard Gibbs energies derived from them was found to be the second most important reason for the erroneous result of the feasibility analysis. Deviations of intracellular pH from the standard value and bad estimations of currency metabolites, e.g., NAD(+) and NADH, were found to be of lesser importance but not negligible. The pH dependency of Gibbs reaction enthalpy was proved to be easily taken into account. Therefore, the application of thermodynamics for a better in-silico prediction of the behavior of living cell factories calls predominantly for better equilibrium data determined under well defined conditions and also for a more detailed knowledge about the intracellular ionic strength and pH value. Copyright 2005 Wiley Periodicals, Inc.

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Year:  2005        PMID: 15962336     DOI: 10.1002/bit.20572

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  20 in total

1.  IGERS: inferring Gibbs energy changes of biochemical reactions from reaction similarities.

Authors:  Kristian Rother; Sabrina Hoffmann; Sascha Bulik; Andreas Hoppe; Johann Gasteiger; Herrmann-Georg Holzhütter
Journal:  Biophys J       Date:  2010-06-02       Impact factor: 4.033

2.  Thermodynamic calculations for biochemical transport and reaction processes in metabolic networks.

Authors:  Stefan J Jol; Anne Kümmel; Vassily Hatzimanikatis; Daniel A Beard; Matthias Heinemann
Journal:  Biophys J       Date:  2010-11-17       Impact factor: 4.033

3.  The thermodynamic meaning of metabolic exchange fluxes.

Authors:  Wolfgang Wiechert
Journal:  Biophys J       Date:  2007-05-25       Impact factor: 4.033

4.  Group contribution method for thermodynamic analysis of complex metabolic networks.

Authors:  Matthew D Jankowski; Christopher S Henry; Linda J Broadbelt; Vassily Hatzimanikatis
Journal:  Biophys J       Date:  2008-08       Impact factor: 4.033

5.  Thermodynamics-based metabolic flux analysis.

Authors:  Christopher S Henry; Linda J Broadbelt; Vassily Hatzimanikatis
Journal:  Biophys J       Date:  2006-12-15       Impact factor: 4.033

6.  Network thermodynamic curation of human and yeast genome-scale metabolic models.

Authors:  Verónica S Martínez; Lake-Ee Quek; Lars K Nielsen
Journal:  Biophys J       Date:  2014-07-15       Impact factor: 4.033

7.  Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli.

Authors:  Bryson D Bennett; Elizabeth H Kimball; Melissa Gao; Robin Osterhout; Stephen J Van Dien; Joshua D Rabinowitz
Journal:  Nat Chem Biol       Date:  2009-06-28       Impact factor: 15.040

8.  Thermodynamic analysis of biodegradation pathways.

Authors:  Stacey D Finley; Linda J Broadbelt; Vassily Hatzimanikatis
Journal:  Biotechnol Bioeng       Date:  2009-06-15       Impact factor: 4.530

9.  Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis.

Authors:  Ryan S Senger; Eleftherios T Papoutsakis
Journal:  Biotechnol Bioeng       Date:  2008-12-01       Impact factor: 4.530

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

Authors:  Daniele De Martino; Matteo Figliuzzi; Andrea De Martino; Enzo Marinari
Journal:  PLoS Comput Biol       Date:  2012-06-21       Impact factor: 4.475

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