Optimal therapeutic methods with random-length response in probabilistic boolean networks

Any antitumor agent should act very rapidly with high level of efficiency so that it may increase the patient´s chance of survival along with a reasonable quality of life during the course of treatment. The goal is to kill as many tumor cells as possible or shift them into a state where they can no longer proliferate. However, biological variabilities among cells in a population and the way they interact with each other or respond to a drug introduce randomness and uncertainty at different levels. This uncertainty should be modeled when designing an intervention strategy. In this paper, we implement a tumor growth model in the presence of the antitumor agent and characterize the variability in the drug response. Then, we present a methodology to devise optimal intervention policies for probabilistic Boolean networks when the antitumor drug has a random-length duration of action.