Large-scale approximate intervention strategies for Probabilistic Boolean Networks as models of gene regulation

Control of Probabilistic Boolean Networks as models of gene regulation is an important problem; the solution may help researchers in various different areas. But as generally applies to control problems, the size of the state space in gene regulatory networks is too large to be considered for comprehensive solution to the problem; this is evident from the work done in the field, where only very small portions of the whole genome of an organism could be used in control applications. The Factored Markov Decision Problem (FMDP) framework avoids enumerating the whole state space by representing the probability distribution of state transitions using compact models like dynamic bayesian networks. In this paper, we successfully applied FMDP to gene regulatory network control, and proposed a model minimization method that helps finding better approximate policies by using existing FMDP solvers. The results reported on gene expression data demonstrate the applicability and effectiveness of the proposed approach.