Analyzing steady state probability distributions of Context-sensitive Probabilistic Boolean Networks

Context-sensitive probabilistic Boolean networks (PBN) have been recently introduced as a paradigm for modeling genetic regulatory networks and have served as the main model for the application of intervention methods, including optimal control strategies, to favorably effect system dynamics. Since it is believed that the steady state behavior of a context-sensitive PBN is indicative of the phenotype, it is important to study the alternation in the steady state distribution due to any variations in the formulations of the context-sensitive PBNs. The goal of this paper is to study the effects of the various definitions of context-sensitive PBNs on the steady state probability distributions and the one-step control policy design.