Optimization of Stochastically-Simulated Gene Network Models

Jonathan R. Tomshine, Chemical Engineering and Materials Science, University of Minnesota -- Minneapolis, 421 Washington Ave SE, Box 78, Minneapolis, MN 55455 and Yiannis N. Kaznessis, Chemical Engineering. and Materials. Science, University of Minnesota, 499, Walter Library, 117, Pleasant St. SE, Minneapolis, MN 55455.

By rearranging naturally occurring genetic components, gene networks can be created that display novel functions. When designing these networks, the structure and kinetics are of great importance, as these parameters strongly influence the behavior of the resulting gene network. This paper presents an optimization method to locate combinations of kinetic parameters that produce a desired behavior in a genetic network. Since gene expression is an inherently stochastic process, the simulation component of the optimization is conducted using an accurate multiscale simulation algorithm to evaluate network behavior. Using the three-gene repressilator of Elowitz and Leibler as an example, we show that gene network optimizations can be conducted using a mechanistically realistic model simulated stochastically. The repressilator is optimized to give oscillations of an arbitrary specified period. These optimized designs may then provide a starting-point for the selection of genetic components needed to realize an in vivo system.