Applied Mathematics Seminar: Stochastic Approximate Model-Checker for Infinite-State Analysis

Speaker: Zhen Zhang, Department of E & CE, Utah State University

Abstract: There has been an increasing demand for providing formal guarantees in the design process of safety-critical synthetic genetic circuits. As a prominent formal verification technique, probabilistic model checking has demonstrated significant potential in formally analyzing the intrinsic probabilistic behaviors of complex genetic circuit designs. However, its scalability is limited as probabilistic models of real-world applications typically have very large or infinite state space. In this talk, I will present a new infinite state CTMC model checker, STAMINA, with improved scalability. It uses a novel state space approximation method to reduce large and possibly infinite state CTMC models to finite state representations that are amenable to existing probabilistic model checkers. It is integrated with a new property-guided state expansion approach that improves the analysis accuracy. This method is evaluated on a design of a genetic toggle switch and several other benchmarking examples. Comparisons with another state-of- the-art tool demonstrate both accuracy and efficiency of the presented method.