Consistent Estimation of Gibbs Energy Using Component Contributions

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.Author Summary: The metabolism of living organisms is a complex system with a large number of parameters and interactions. Nevertheless, it is governed by a strict set of rules that make it somewhat predictable and amenable to modeling. The laws of thermodynamics play a pivotal role by determining reaction feasibility and by governing the kinetics of enzymes. Here we introduce estimations for the standard Gibbs energy of reactions, with the best combination of accuracy and coverage to date. The estimations are derived using a new method which we denote component contribution. This method integrates multiple sources of information into a consistent framework that obeys the laws of thermodynamics, and provides a significant improvement in accuracy compared to previous genome-wide estimations of standard Gibbs energies. We apply and test our method on reconstructions of E. coli and human metabolism and, in addition, do our best to facilitate the use of these estimations in future models by providing open-source software that performs the integration in a streamlined process.