Learning versus optimal intervention in random Boolean networks

Karlsen, Matthew R.; Moschoyiannis, Sotiris; Georgiev, Vlad B.

doi:10.1007/s41109-019-0243-z

Cited by 6 publications

(1 citation statement)

References 37 publications

(60 reference statements)

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“…Previous work on control that utilises Q-Learning includes the work of Karlsen, et al [33] on rule-based RL, in the form of an eXtended Classifier System (XCS) which was also applied to the yeast cell cycle BN (N =11) in [34]. The stabilization of PBNs with Q-Learning is studied in [35] but that work also only address a small apoptosis PBN (N =9).…”

Section: Introductionmentioning

confidence: 99%

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

Moschoyiannis

Chatzaroulas

Sliogeris

et al. 2023

IEEE Trans. Control Netw. Syst.

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The 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., with or without control inputs; attractor state 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.

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Section: Introductionmentioning

confidence: 99%

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

Moschoyiannis

Chatzaroulas

Sliogeris

et al. 2023

IEEE Trans. Control Netw. Syst.

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Deep Reinforcement Learning for Control of Probabilistic Boolean Networks

Papagiannis

Moschoyiannis

2021

Studies in Computational Intelligence

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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 propose the use of a Deep Q Network with Double Q Learning, that directly interacts with the environment -that is, a Probabilistic Boolean Network. The proposed approach is trained by sampling experiences obtained from the environment using Prioritised Experience Replay and successfully determines a control policy that directs a PBN from any state to the desired state (attractor). We demonstrate successful results on significantly larger PBNs compared to previous approaches under our control framework.

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State-flipped control and Q-learning for finite horizon output tracking of Boolean control networks

Sun

2023

International Journal of Systems Science

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Learning versus optimal intervention in random Boolean networks

Cited by 6 publications

References 37 publications

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

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

Deep Reinforcement Learning for Control of Probabilistic Boolean Networks

State-flipped control and Q-learning for finite horizon output tracking of Boolean control networks

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