Integration of Postgenomic Data for GMA to Simulate a Metabolic Circuit

In order to understand cell metabolism as a system, it is important to formulize its molecular interaction network and to analyze it in the system level. In the postgenomic era, various experimental data such as transcriptome, proteome, and metabolome have accumulated rapidly. Such information is greatly available for constructing a molecular interaction map and simulating its dynamic features. We aim at developing the strategy that integrates such postgenomic data efficiently for simulating a metabolic system. General Mass Action (GMA) is a powerful candidate that integrates the postgenomic data [1], because GMA readily employs the profile of metabolite concentrations, fluxes, and enzyme concentrations. Then, a problem is that it is difficult to measure the kinetic parameters in vivo based on the above technologies. Generally, the measurement of a kinetic parameter requires the extensive and laborious experiments based on the kinetic theory. Therefore, we present a novel strategy that connects the postgenomic data to the GMA simulation.