Deep Reinforcement Learning for Controlled Traversing of the Attractor Landscape of Boolean Models in the Context of Cellular Reprogramming
CoRR(2024)
摘要
Cellular reprogramming can be used for both the prevention and cure of
different diseases. However, the efficiency of discovering reprogramming
strategies with classical wet-lab experiments is hindered by lengthy time
commitments and high costs. In this study, we develop a novel computational
framework based on deep reinforcement learning that facilitates the
identification of reprogramming strategies. For this aim, we formulate
a control problem in the context of cellular reprogramming for the frameworks
of BNs and PBNs under the asynchronous update mode. Furthermore, we introduce
the notion of a pseudo-attractor and a procedure for identification of
pseudo-attractor state during training. Finally, we devise a computational
framework for solving the control problem, which we test on a number of
different models.
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