Stochastic analysis of synthetic genetic circuits

Synthetic biology combines the research of biology with the engineering principles of standards, abstraction, and automated construction with the ultimate goal of being able to design and build useful biological systems. To realize this goal, researchers are actively working on better ways to model and analyze synthetic genetic circuits; however, there is still a great need for design space exploration techniques that allow synthetic biologists to easily explore the effect of varying parameters and efficiently consider alternative designs of their systems. In my talk, I will highlight some efforts to address this need by presenting research on the development of a new incremental stochastic simulation algorithm (iSSA) based on Gillespie's stochastic simulation algorithm (SSA) that is capable of presenting a researcher with a simulation trace of the typical behavior of the system. Before the development of this algorithm, discerning this information was extremely error-prone as it involved performing many simulations and attempting to wade through the massive amounts of data. Additionally, I will show how stochastic model checking techniques can be applied to models of genetic circuits in order to ensure that they behave correctly and are as robust as possible for a variety of different inputs and parameter settings. Finally, I will illustrate how both the iSSA and stochastic model checking can be used in concert to give a researcher the likelihood that the system exhibits its most typical behavior, as well as, non-typical behaviors.