Modeling cyclic and acyclic therapeutic methods with persistent intervention effect in probabilistic Boolean networks

In cancer therapy, mostly in the form of chemotherapy, the goal is to alter the likelihood of undesirable states such as those associated with disease in the long run. After delivery, a drug will be effective on the target cell(s) for some period of time, followed by a recovery phase. This paper presents a methodology to devise optimal intervention strategies for two classes of cyclic and acyclic therapeutic methods with fixed-length duration of effect for any Markovian genetic regulatory network.