The objective of this research is to develop the theory and advanced computational tools for a modular decomposition and analysis of complex gene networks. The key challenge to overcome is that biological networks are high-dimensional coupled stochastic nonlinear systems with uncertain components. The approach is to employ tools and expertise from computational and experimental systems biology, stochastic dynamic nonlinear networks, and control theory to systematically decompose the network into simple modules whose dynamic properties can be understood in isolation and then related to the behavior of the network.
The research has the potential to provide a framework that (a) enables the decomposition of complex biological networks into modules; (b) identifies the essential characteristics of each module necessary to account for its role in the network's behavior; and (c) provides the analytical and computational foundation for the analysis and synthetic construction of networks of modules. The systematic approach explicitly accounts for network dynamics, the stochastic nature, and uncertainty of biological networks. Development of the framework is guided by and validated through carefully selected biological experiments.
The research has the potential to provide new tools to help scientists reverse-engineer complex biological networks, leading to a deeper understanding of biological function. Such understanding is a key step in the rational design of therapies. Research and educational activities are tightly integrated to train a diverse cadre of scientists and engineers who are adept at employing computational thinking in multi-disciplinary research. Recruitment of women and students from under-represented groups through summer internships, campus programs, and special institutional partnerships is central to the investigators' strategy for achieving diversity.
