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A framework to shift basins of attraction of gene regulatory networks through batch reinforcement learning
چارچوبی برای تغییر حوزه جذب شبکه های نظارتی ژن از طریق یادگیری تقویتی دسته ای-2020 A major challenge in gene regulatory networks (GRN) of biological systems is to discover when and what interventions
should be applied to shift them to healthy phenotypes. A set of gene activity profiles, called basin of
attraction (BOA), takes this network to a specific phenotype; therefore, a healthy BOA leads the GRN to a healthy
phenotype. However, without the complete observability of the genes, it is not possible to identify whether the
current BOA is healthy. In this article we investigate external interventions in GRN with partial observability
aiming to bring it to healthy BOAs. We propose a new batch reinforcement learning method (BRL), called mSFQI,
to define intervention strategies based on the probabilities of the gene activity profiles being in healthy BOAs,
which are calculated from a set of previous observed experiences. BRL uses approximation functions and repeated
applications of previous experiences to accelerate learning. Results demonstrate that our proposal can
quickly shift a partially observable GRN to healthy BOAs, while reducing the number of interventions. In addition,
when observability is poor, mSFQI produces better results when the probabilities for a greater amount of
previous observations are available. Keywords: Reinforcement learning | Gene regulatory network | Basin of attraction |
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