Quantitative Assessment of Thermodynamic Constraints on the Solution Space of Genome-Scale Metabolic Models

Abstract

Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations.

Computer-Guided Metabolic Engineering

“…Additional constraints can be applied to further reduce the number of allowable flux distributions [18]. Limits on the range of individual flux values can be used for this purpose; thermodynamic constraints [19] expressed as the directionality of a given reaction [16] can thus be used by setting one of the boundaries for that reaction to zero if the reaction is irreversible. In a similar way, maximum flux values can be estimated based on enzymatic capacity limitations [20], or for the case of exchange reactions, measured maximal uptake rates can be used (Sect.…”

Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

“…Methods that use thermodynamics data such as the thermodynamics-based flux analysis TFA [35][36][37][38][39] allow us to integrate the metabolomics data together with the fluxomics data, to eliminate in silico designed biosynthetic pathways not obeying the second law of thermodynamics [40,41], to eliminate infeasible thermodynamic cycles [42][43][44], and to identify how far reactions operate from thermodynamic equilibrium [45,46]. Despite the fact that usefulness of thermodynamics has been demonstrated in many applications, only a few reconstructed GEMs are curated for this important property [45,[47][48][49][50].…”

Temperature-dependent estimation of Gibbs energies using an updated group contribution method

“…An increasing number of systems biology methods have begun to take advantage of the intimate connection between thermodynamics and metabolism to obtain insights into the function of metabolic networks. These methods have been used in a number of applications including the calculation of thermodynamically-feasible optimal states (1,2), the identification of thermodynamic bottlenecks in metabolism (3,4), and the constraint of kinetic constants via Haldane relationships (5).…”