Consistent Estimation of Gibbs Energy Using Component Contributions

Noor, Elad; Haraldsdóttir, Hulda S.; Milo, Ron; Fleming, Ronan M. T.

doi:10.1371/journal.pcbi.1003098

Consistent Estimation of Gibbs Energy Using Component Contributions

Journal Article · Thu Jul 11 00:00:00 EDT 2013 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1003098· OSTI ID:1904772

Noor, Elad ^[1]; Haraldsdóttir, Hulda S. ^[2]; Milo, Ron ^[3]; Fleming, Ronan M. T. ^[4]

Weizmann Institute of Science, Rehovot (Israel); Weizmann Institute of Science, Rehovot (Israel)
University of Iceland, Reykjavik (Iceland)
Weizmann Institute of Science, Rehovot (Israel)
University of Iceland, Reykjavik (Iceland); University of Luxembourg, Esch-sur-Alzette, (Luxembourg)

Standard Gibbs energies of reactions are increasingly being used in metabolic modeling for applying thermodynamic constraints on reaction rates, metabolite concentrations and kinetic parameters. The increasing scope and diversity of metabolic models has led scientists to look for genome-scale solutions that can estimate the standard Gibbs energy of all the reactions in metabolism. Group contribution methods greatly increase coverage, albeit at the price of decreased precision. We present here a way to combine the estimations of group contribution with the more accurate reactant contributions by decomposing each reaction into two parts and applying one of the methods on each of them. This method gives priority to the reactant contributions over group contributions while guaranteeing that all estimations will be consistent, i.e. will not violate the first law of thermodynamics. We show that there is a significant increase in the accuracy of our estimations compared to standard group contribution. Specifically, our cross-validation results show an 80% reduction in the median absolute residual for reactions that can be derived by reactant contributions only. We provide the full framework and source code for deriving estimates of standard reaction Gibbs energy, as well as confidence intervals, and believe this will facilitate the wide use of thermodynamic data for a better understanding of metabolism.

View Accepted Manuscript (DOE)

Research Organization:: Stanford University, CA (United States); Weizmann Institute of Science, Rehovot (Israel)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); Azrieli Foundation; European Research Council; USDOE Office of Science (SC), Biological and Environmental Research (BER); Icelandic Research Fund

Grant/Contract Number:: SC0002009; SC0002009

OSTI ID:: 1904772

Journal Information:: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 7 Vol. 9; ISSN 1553-7358

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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