Georgia Institute of Technology College of Sciences

Modeling stochasticity in gene regulation is an important and complex problem in molecular systems biology due to probabilistic nature of gene regulation. This talk will introduce a stochastic modeling framework for gene regulatory networks which is an extension of the Boolean modeling approach. This framework incorporates propensity parameters for activation and degradation and is able to capture the cell-to-cell variability. It will be presented in the context of finite dynamical systems, where each gene can take on a finite number of states, and where time is also a discrete variable. Applications using methods from control theory for Markov decision processes will be presented for the purpose of developing optimal intervention strategies. A background to stochastic modeling will be given and the methods will be applied to the p53-mdm2 complex.