Title

Approximation Techniques for Stochastic Analysis of Biological Systems

Document Type

Article

Journal/Book Title/Conference

Automated Reasoning for Systems Biology and Medicine

Publisher

Springer Netherlands

Publication Date

6-12-2019

First Page

327

Last Page

348

Abstract

There has been an increasing demand for formal methods in the design process of safety-critical synthetic genetic circuits. Probabilistic model checking techniques have demonstrated significant potential in analyzing the intrinsic probabilistic behaviors of complex genetic circuit designs. However, its inability to scale limits its applicability in practice. This chapter addresses the scalability problem by presenting a state-space approximation method to remove unlikely states resulting in a reduced, finite state representation of the infinite-state continuous-time Markov chain that is amenable to probabilistic model checking. The proposed method is evaluated on a design of a genetic toggle switch. Comparisons with another state-of-the-art tool demonstrate both accuracy and efficiency of the presented method.

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