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

Abstract

The ability to direct a probabilistic Boolean network (PBN) to the 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 flavors of the control problem (e.g., with or without control inputs; having attractor states or a 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 only linear on the time steps or interactions between the agent (deep RL) and the environment (PBN), during training. Indeed, we explore the scalability of the deep RL approach to (set) stabilization of large-scale PBNs and demonstrate successful control on large networks, including a metastatic melanoma PBN with 200 nodes.