Control Theory Concepts for Modeling Uncertainty in Enzyme Kinetics of Biochemical Networks

Abstract

Analysis of the dynamic and steady-state properties of biochemical networks hinges on information about the parameters of enzyme kinetics. The lack of experimental data characterizing enzyme activities and kinetics along with the associated uncertainties impedes the development of kinetic models, and researchers commonly use Monte Carlo sampling to explore the parameter space. However, the sampling of parameter spaces is a computationally expensive task for larger biochemical networks. To address this issue, we exploit the fact that reaction rates of biochemical reactions and network responses can be expressed as a function of displacements from the thermodynamic equilibrium of elementary reaction steps and concentrations of free enzymes and their intermediary complexes. For a set of kinetic mechanisms ubiquitously found in biochemistry, we express kinetic responses of enzymes to changes in network metabolite concentrations through these quantities both analytically and schematically. The tailor-made sampling of these quantities allows for characterizing efficiently the missing kinetic parameters and accelerating the efforts toward building genome-scale kinetic metabolic models, and further, it advances efforts in the Bayesian inference context. The proposed schematic method is simple and lends itself to a computer implementation that can be computationally more efficient than computer implementations of similar schematic methods.