Robust Intervention in Probabilistic Boolean Networks

One of the objectives of genetic regulatory network modeling is to design intervention approaches for affecting the time evolution of the gene activity profile of the network. The intervention strategies proposed in the context of Probabilistic Boolean Networks(PBNs) assume perfect knowledge of the transition probability matrix of the PBN. This assumption cannot be satisfied in practice due to estimation errors or mismatch between the PBN model and the actual genetic regulatory network. In this paper, we develop a robust intervention strategy that is obtained by minimizing the worst-case cost over the uncertainties in the entries of the transition probability matrix.