Approximating Stochastic Biochemical Networks

David Thorsley
Research Associate
Department of Electrical Engineering
University of Washington

Understanding stochastic phenomena is a fundamental problem throughout science and engineering. When faced with new types of complex or complicated systems, we as engineers strive to develop intuitive methods that allow us to intelligently and safely make simple approximations, thus allowing us to more easily and efficiently answer questions about these systems. In the emerging field of systems biology, we are still striving to develop the necessary approaches to approximation that will make the analyses of such systems tractable. In this talk, I will present a general methodology for quantifying the differences between stochastic biochemical networks that can be used in to address the problems of model comparison, model reduction, and parameter optimization. The potential of this approach will be illustrated using a stochastic reaction network model of gene expression in bacteria.