Stochastic Analysis of Synthetic Genetic Circuits
Synthetic biology is a relatively new area of research that combines biology and engineering with the ultimate goal of designing new, useful biological systems. In order to efficiently accomplish this goal, researchers evaluate their models of biological systems by using simulation and numerical techniques to ensure that they exhibit desired behaviors. However, as the complexity of the systems grow, these types of analysis become prohibitively expensive both in time and memory. To address this issue, we are developing new methods to analyze the design space of genetic circuits. These methods include the use of an incremental adaptation of Gillespie's stochastic simulation algorithm to determine the typical behavior of the system and the use of stochastic model checking to determine the likelihood that the system exhibits this typical behavior or another desired behavior. Researchers using these methods will be able to more efficiently explore the design space of their models and arrive at more robust implementations of their systems.