Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism

Abstract

The quantitative analysis of biochemical reactions and metabolites is at frontier of biological sciences. The recent availability of high-throughput technology data sets in biology has paved the way for new modelling approaches at various levels of complexity including the metabolome of a cell or an organism. Understanding the metabolism of a single cell and multi-cell organism will provide the knowledge for the rational design of growth conditions to produce commercially valuable reagents in biotechnology. Here, we demonstrate how equations representing steady state mass conservation, energy conservation, the second law of thermodynamics, and reversible enzyme kinetics can be formulated as a single system of linear equalities and inequalities, in addition to linear equalities on exponential variables. Even though the feasible set is non-convex, the reformulation is exact and amenable to large-scale numerical analysis, a prerequisite for computationally feasible genome scale modelling. Integrating flux, concentration and kinetic variables in a unified constraint-based formulation is aimed at increasing the quantitative predictive capacity of flux balance analysis. Incorporation of experimental and theoretical bounds on thermodynamic and kinetic variables ensures that the predicted steady state fluxes are both thermodynamically and biochemically feasible. The resulting in silico predictions are tested against fluxomic data for central metabolism in E. coli and compare favourably with in silico prediction by flux balance analysis.

Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods

“…In addition, unrealistic fluxes can be predicted in silico if a reaction is reversible in a model, but irreversible in vivo . Thus, methods are now applying more rigorous thermodynamic constraints (Figure 2) by removing thermodynamically infeasible pathway usage 95–97 or constraining flux based on Gibbs free energy calculations 51, 98, 99 . Methods are also being used to infer thermodynamic parameters 100 .…”

Systematizing the generation of missing metabolic knowledge

“…Another potential area for improvement of gap-filling methods is the inclusion of thermodynamic information. Several studies have already shown that the inclusion of thermodynamic constraints can improve the accuracy of constraint-based model predictions (Beard et al 2004; Fleming et al 2009; Fleming et al 2010; Henry et al 2006). So far, only studies using BNICE and the modified GrowMatch used to improve the iBsu1103 B. subtilis model have considered thermodynamic constraints when making gap-filling predictions.…”

The Uses and Future Prospects of Metabolomics and Targeted Metabolite Profiling in Cell Factory Development

“…A number of promising approaches has been proposed for incorporating metabolite-level information into genome-scale metabolic models. [51][52][53][54] The goal of these modeling approaches is to use incomplete metabolite-level or metabolic-flux measurements to determine the metabolic state (both fluxes and concentrations) of the cell as completely and accurately as possible. The combination of statistical and mechanistic modeling strategies presents a promising strategy to leverage the strengths of both approaches for future design of improved cell factories.…”