Finite-Horizon Control of Genetic Regulatory Networks with Multiple Hard-Constraints

Probabilistic Boolean Networks (PBNs) provide a convenient tool for studying the interactions among different genes while allowing uncertainty. This paper deals with the issue of finite-horizon control with multiple hard-constraints in a PBN. More precisely, under the constraint of the number of times that each control method can be applied, we develop a control strategy by which the state of a given genetic network falls into a desired state set with a prescribed minimum probability. We propose an efficient algorithm to find the feasible solutions. An upper bound for the computational cost is also given. An numerical experiment is then conducted to demonstrate the efficiency of our proposed method.