Development of thermodynamic optimum searching (TOS) to improve the prediction accuracy of flux balance analysis

Abstract

Flux balance analysis (FBA) has been widely used in calculating steady-state flux distributions that provide important information for metabolic engineering. Several thermodynamics-based methods, for example, quantitative assignment of reaction directionality and energy balance analysis have been developed to improve the prediction accuracy of FBA. However, these methods can only generate a thermodynamically feasible range, rather than the most thermodynamically favorable solution. We therefore developed a novel optimization method termed as thermodynamic optimum searching (TOS) to calculate the thermodynamically optimal solution, based on the second law of thermodynamics, the minimum magnitude of the Gibbs free energy change and the maximum entropy production principle (MEPP). Then, TOS was applied to five physiological conditions of Escherichia coli to evaluate its effectiveness. The resulting prediction accuracy was found significantly improved (10.7-48.5%) by comparing with the (13)C-fluxome data, indicating that TOS can be considered an advanced calculation and prediction tool in metabolic engineering.

Elementary flux modes in a nutshell: Properties, calculation and applications

“…Nonetheless, both examples show that the number of biologically feasible modes may be dramatically smaller than expected by ordinary EFMA. Similar findings were reported in other constraint‐based approaches like flux balance analysis as well [46–48].…”

Mathematical Modeling of Microbial Community Dynamics: A Methodological Review

“…Another widely used thermodynamic concept in metabolic modeling, whether single organisms [82] or communities of organisms [83], is entropy production. In flux-based modeling, entropy production is used as a constraint to reduce the solution space to a set of reactions that are more likely to be feasible ones.…”

“…Zhu et al have developed a flux-based approach that uses entropy production as the optimization goal [82]. While several flux-based applications include constraints based on Equations (9) to (12), this approach selects the thermodynamically most optimal solution as the most likely one.…”

Handling Complexity in Animal and Plant Science Research—From Single to Functional Traits: Are We There Yet?

“…Recently, fluxomes have been incorporated as further lenses to “omics analyses” [ 31 , 32 , 33 , 34 , 35 , 36 ]. This addition is important when considering the dynamic relationships between proteome and metabolome in plant metabolomics [ 31 , 32 , 33 , 34 , 35 , 36 ]. Their interaction can be exploited to determine the rates of growth and product formation by monitoring the steady state rates of significant cellular phenotypes during their metabolic inter conversion within living cells [ 29 ].…”

“…Their interaction can be exploited to determine the rates of growth and product formation by monitoring the steady state rates of significant cellular phenotypes during their metabolic inter conversion within living cells [ 29 ]. Such analysis requires the determination of the steady-state flux distribution, which in turn is calculated using flux balance analysis: this approach relies on the quantification of a set of experimentally measured metabolic fluxes within a network, such as production excretion or substrate consumption [ 31 , 32 , 33 , 34 , 35 , 36 ]. This fluxome process may also be referred to as metabolic regulon, in which the system’s innate control of metabolite levels through the regulation of metabolic flux of the biosynthesis and catabolism pathway is impaired [ 31 , 32 , 33 , 34 , 35 , 36 ].…”