A Pathway-Based Technique For Analysis Of Metabolic Reaction Networks

Recently, it has become possible to acquire a large metabolome data set from high-throughput instruments. Time-series metabolome data includes important information to understand metabolism as a system. The present work proposes a new pathway-based technique for in silico analysis of a metabolic reaction network by using time-series metabolome data. In this approach, a mathematical model is constructed in power-law formulations in the framework of biochemical systems theory based on available information on metabolic pathways, which are composed of chemical reactions and feedback regulations by metabolites, from databases. The parameters in differential equations, namely, kinetic orders and rate constants, are estimated from actual time-series data of metabolite concentrations. The obtained mathematical model enables us to simulate metabolic behaviors and conduct the system analysis of a metabolic reaction network to identify rate-limiting steps in metabolic pathways. In this study, we constructed a mathematical model of Arabidopsis based on AraCyc. The result of our on-going study will be introduced in the workshop.