Enzyme States Allow Identification of Rate-Limiting Steps
A precise quantification of the effect of perturbations in a metabolic network depends on explicit knowledge of the kinetic properties of the enzymes of the individual reactions. However, a comprehensive knowledge of enzymes kinetics in a metabolic network is very difficult, if not impossible, to obtain. Furthermore, experimental data obtained under different conditions introduce uncertainty in enzyme kinetic parameters. In this work, we model the uncertainty in the kinetic data and we predict quantitatively the responses of metabolic networks in the presence of genetic, biochemical, and environmental variations. The proposed methodology accounts explicitly for mechanistic properties of enzymes and physicochemical and thermodynamic constraints and is based on formalism from process control and metabolic control. The method employs a novel, efficient Monte Carlo sampling procedure that allows us to simulate all possible meaningful states of a metabolic network and to compute the corresponding values of the kinetic constants of the individual reaction steps. We demonstrate the properties of the proposed framework through a number of case studies.