Bayesian Integrative Modeling of Genome-Scale Metabolic and Regulatory Networks

The integration of high-throughput data to build predictive computational models of cellular metabolism is a major challenge of systems biology. These models are needed to predict cellular responses to genetic and environmental perturbations. Typically, this response involves both metabolic regulations related to the kinetic properties of enzymes and a genetic regulation affecting their concentrations. Thus, the integration of the transcriptional regulatory information is required to improve the accuracy and predictive ability of metabolic models. Integrative modeling is of primary importance to guide the search for various applications such as discovering novel potential drug targets to develop efficient therapeutic strategies for various diseases. In this paper, we propose an integrative predictive model based on techniques combining semantic web, probabilistic modeling, and constraint-based modeling methods. We applied our approach to human cancer metabolism to predict in silico the growth response of specific cancer cells under approved drug effects. Our method has proven successful in predicting the biomass rates of human liver cancer cells under drug-induced transcriptional perturbations.