INRA - Unité MaIAGE
Charité Berlin - Institut für Biochemie
Kinetic models are essential to better understand the dynamics and function of enzyme regulation, and hence to predict the metabolic effects of differential enzyme expression and enzyme-inhibiting drugs. However, kinetic modelling is not yet applicable to large, genome-scale networks. Existing genome-scale modelling approaches, such as flux balance analysis, are based on stoichiometry only and therefore inherently limited in use. This project aims to fill the gap between genome-scale stoichiometric and small-scale kinetic models by the development of a novel kinetic modelling approach for large metabolic networks. The method combines metabolic control analysis with data integration and sampling techniques and accounts for thermodynamic constraints. The results will be probabilistic, reflecting the availability and quality of input data.
The applicability of the new method will be tested through its use to create large network models and to predict flux changes caused by enzyme regulation, to couple these models with detailed kinetic pathway models, to compute synergisms between enzyme-inhibiting drugs, to predict the dynamic effects of alternating enzyme levels, and to study the advantages of enzymatic regulation and alternating enzyme levels in fluctuating environments through a computational cost-benefit approach. Through external collaborations, model predictions will be validated with experimental omics data from bacterial and yeast cultures and from human hepatocytes. By extending dynamic modelling to large metabolic networks, the project will substantially improve the understanding of enzyme regulation, with potential future applications in cell simulation, prediction of drug interactions and side effects, and chronotherapies.