Probabilistic Control of Gene Networks

In this paper, an approach based on Markov chains for controlling gene networks is proposed. The state of the network is represented as a probabilitiy distribution, while state transitions are considered to be probabilistic. An algorithm is proposed to determine a sequence of control actions that drives the state of a given network to within a desired state set with a prescribed minimum or maximum probability. A heuristic is proposed and shown to improve the efficiency of the proposed algorithm for a class of gene networks.