Optimal control of steady-state probability distributions of Probabilistic Boolean Networks

Probabilistic Boolean networks (PBNs) have been proved to be a useful tool for modeling genetic regulatory interactions. This letter investigates the issue concerning optimal control of steady-state probability distributions of PBNs. Using the recently proposed semi-tensor product technique, we establish a specific objective function and thus transform the issue concerned into an optimization problem in the algebraic form. Then we present a genetic algorithm to find the best solution which corresponds to control inputs that drive the PBN to desired steady-state probability distributions. Experiments and an example are provided to show the effectiveness of the proposed method.