An in vivo data-driven framework for classification and quantification of enzyme kinetics and determination of apparent thermodynamic data

Kinetic modeling of metabolism holds great potential for metabolic engineering but is hindered by the gap between model complexity and availability of in vivo data. There is also growing interest in network-wide thermodynamic analyses, which are currently limited by the scarcity and unreliability of thermodynamic reference data. Here we propose an in vivo data-driven approach to simultaneously address both problems. We then demonstrate the procedure in Saccharomyces cerevisiae, using chemostats to generate a large flux/metabolite dataset, under 32 conditions spanning a large range of fluxes. ons were classified as pseudo-, near- or far-from-equilibrium, allowing the complexity of mathematical description to be tailored to the kinetic behavior displayed in vivo. For 3/4 of the reactions we derived fully in vivo-parameterized kinetic descriptions which can be readily incorporated into models. For near-equilibrium reactions this involved a new simplified format, dubbed “Q-linear kinetics”. We also demonstrate, for the first time, systematic estimation of apparent in vivo Keq values. Remarkably, comparison with E. coli data suggests they constitute a suitable in vivo interspecies thermodynamic reference.