Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks

Abstract

AbstractThe ability to direct a Probabilistic Boolean Network (PBN) to a desired state is important to applications such as targeted therapeutics in cancer biology. Reinforcement Learning (RL) has been proposed as a framework that solves a discrete-time optimal control problem cast as a Markov Decision Process. We focus on an integrative framework powered by a model-free deep RL method that can address different flavours of the control problem (e.g., withorwithout control inputs; attractor stateora subset of the state space as the target domain). The method is agnostic to the distribution of probabilities for the next state, hence it does not use the probability transition matrix. The time complexity is onlylinearon the time steps, or interactions between the agent (deep RL) and the environment (PBN), during training. Indeed, we explore thescalabilityof the deep RL approach to (set) stabilization of large-scale PBNs and demonstrate successful control on large networks, including a metastatic melanoma PBN with200 nodes.