Integration of Biological High throughput Data with a Metabolic Model of a Liver Cell

A large number of potentially harmful chemicals and pollutants in the environment make comprehensive experimental chemical testing cost prohibitive and unrealistic. Methods that can decrease the required experimental work and aid in the streamlining of this process would provide a valuable tool in this area. Computational cellular modeling can provide a significant improvement in linking exposure of potentially harmful chemicals to the effects those chemicals have on the metabolism of a host. With the latest advances in biological high-throughput technologies, vast amounts of data exist that represent gene expression profiles, protein interactions, and metabolite concentrations. These multivariate data sets allow the construction of detailed genome-scale metabolic network models. By using a constraint-based approach for constructing a model of liver metabolism, the effect of compound exposure can be linked to detailed mechanisms in the metabolic network. Metabolism plays a central role in the toxicological realm from how a xenobiotic compound is metabolized to determining downstream side effects. The combination of linking the results of statistical analysis of high-throughout data with a comprehensive model of liver metabolism creates a powerful tool that can have wide market appeal in many areas. From pharmaceutical and biotechnological companies to government and military institutions, a product of this type can increase productivity significantly and aid in understanding the metabolic effects of toxicity.