277795 Thermodynamics-Based Flux-Balance Analysis: Incorporation of Thermodynamic and Metabolomic Data Into Genome-Scale Constraint-Based Models
Genome-scale metabolic reconstructions provide a concise, mathematical representation of cellular metabolism, and can be used as tools to probe a variety of biological questions via constraint-based methods. These methods have been successfully used to predict cellular phenotypes, guide metabolic engineering, and provide context to high-throughput (‘-omics’) data. The predictive accuracy of constraint-based methods relies on an accurate representation of the underlying biochemical network, including both the network stoichiometry and the quantitative assignment of reaction directions. Existing approaches to quantitatively assign reaction directionality rely on group contribution theory  to calculate thermodynamically feasible directions of individual reactions based on a standard metabolite concentration , or to enforce thermodynamic feasibility via the imposition of additional reversibility constraints for reactions identified as qualitatively reversible .
We have used a thermodynamic-based flux balance analysis approach, TFBA, to ensure thermodynamically feasible directions for all reactions in a genome-scale metabolic network. We used group contribution theory estimates and errors of Gibbs free energies of formation for all metabolites, and calculate Gibbs free energies of reaction in response to metabolite concentrations. Metabolite concentrations were allowed to vary freely, or were constrained based on experimental measurements. We compared TFBA predictions for growth phenotypes of Escherichia coli single-gene knockout mutants to both experimental data and to existing flux balance analysis predictions. We also examined the sensitivity of predicted growth rate to uncertainties in intracellular metabolite concentrations and Gibbs free energies of formation. We subsequently identified a set of metabolites for which precise concentration and thermodynamic data are necessary for accurate in silicopredictions. These results emphasize the need for precise metabolomic data and experimental measurements of thermodynamic properties of key metabolites.
 Jankowski, et al. “Group contribution method for thermodynamic analysis of complex metabolic networks.” Biophysical Journal2008. 95:1487-1499.
 Fleming, et al. “Quantitative assignment of reaction directionality in constraint-based models of metabolism: Application to Escherichia coli.” Biophysical Chemistry2009. 145:47-56.
 Henry, et al. “Thermodynamics-Based Metabolic Flux Analysis.” Biophysical Journal 2007. 92:1792-1805.
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