Integration of large-scale metabolic, signaling, and gene regulatory networks with application to infection responses

Mathematical models of biochemical networks, such as metabolic, signaling, and gene networks, have been studied extensively and have been shown to provide accurate descriptions of various cell processes. Nevertheless, their usage is restricted by the fact that they are usually studied in isolation, without feedback from the environment in which they evolve. Integrating these models in a global framework is a promising direction in order to increase both their accuracy and predictive capacity. In this paper, we describe the integration of large-scale metabolic and signaling networks with a regulatory gene network. We focus on the response to infection in mouse macrophage cells. Our computational framework allows to virtually simulate any type of infection and to follow its effect on the cell. The model comprises 3,507 chemical species involved in 4,630 reactions evolving at the fast time scale of metabolic and signaling processes. These interact with 20 genes evolving at the slow time scale of gene expression and regulation. We develop a simulator for this model and use it to study infections with Porphyromonas gingivalis.