30 March 2016
Department of Mathematics
Regulatory networks are often presented as a directed graph with annotated edges that indicate if one node is up-regulating or down-regulating another node. The question we attempt to answer is "What dynamics can this network generate?". Traditional answers to this problem require knowledge of the nonlinear interactions and parameter values. However, our motivation arises from gene regulatory networks where we assume that the nodes represent regulatory genes that lead to switch like behavior. In this context it is not reasonable to assume that analytic expressions of the nonlinearities are known nor that we have precise knowledge of parameter values.
With this in mind we have developed a novel description of global dynamics that is mathematically rigorous, computationally tractable and provides a finite queryable description of global dynamics over all parameter values. We encode this information into a database of Dynamics Signatures Generated by Regulatory Networks (DSGRN). In this talk we will explain how we describe dynamics, what the database encodes, give examples of a DSGRN database for moderate sized subnetworks of p53, and how we are using DSGRN to explore potential regulatory networks for malaria.