A scalable computational framework for establishing long-term behavior of stochastic reaction networks.

ABSTRACT: Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determin

SUBMITTER: Gupta A 

PROVIDER: S-EPMC4072526 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature