Wednesday, February 18, 2009
3:00 - 4:00
Room 457 Blocker
Analysis and Control for Gene Regulatory Networks
Department of Statistics / Electrical Engineering
Texas A&M University
Probabilistic Boolean networks constitute a class of gene regulatory networks to model biological processes with the network dynamics determined by logic-rule regulatory functions in conjunction with probabilistic parameters involved in network transitions. Since our ultimate purpose for studying networks is to apply intervention to living organisms, it is incumbent that we analyze long-run network behavior based on the underlying Markov chain. Its steady-state distribution reflects the long-run behavior of the network and it can give insight into the dynamics or momentum existing in a system. The change of steady-state distribution caused by possible perturbations to a network is the key measure for intervention. We derive analytic results for changes in the steady-state distributions of probabilistic Boolean networks resulting from modifications to the underlying regulatory rules and probabilistic parameters. From these analytic results, we derive both optimal and greedy intervention strategies to obtain therapeutic benefits for future drug design or gene therapy design, and analyze the sensitivity of gene regulatory networks. The preliminary results in two real biological networks have shown that our methods can potentially serve as future intervention strategies to identify potential drug targets and design gene-based therapeutic strategies.