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

摘要

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.