Deep Reinforcement Learning for Control of Probabilistic Boolean Networks
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Probabilistic Boolean Networks (PBNs) were introduced as a computational model for studying gene interactions in Gene Regulatory Networks (GRNs). Controllability of PBNs, and hence GRNs, is the process of making strategic interventions to a network in order to drive it from a particular state towards some other potentially more desirable state. This is of significant importance to systems biology as successful control could be used to obtain potential gene treatments by making therapeutic interventions. Recent advancements in Deep Reinforcement Learning have enabled systems to develop policies merely by interacting with the environment, without complete knowledge of the underlying Markov Decision Process (MDP). In this paper we have implemented a Deep Q Network with Double Q Learning, that directly interacts with the environment -that is, a Probabilistic Boolean Network. Our approach develops a control policy by sampling experiences obtained from the environment using Prioritized Experience Replay which successfully drives a PBN from any state towards the desired one. This novel approach sets the foundations for overcoming the inability to scale to larger PBNs and opens up the spectrum in which to consider control of GRNs without the need of a computational model, i.e. by direct interventions to the GRN.
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