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.
Recommended Citation
Neupane, T., Zhang, Z., Madsen, C., Zheng, H., Myers, C.J. (2019). Approximation Techniques for Stochastic Analysis of Biological Systems. In: Liò, P., Zuliani, P. (eds) Automated Reasoning for Systems Biology and Medicine. Computational Biology, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-17297-8_12
