Deep Reinforcement Learning for Controlled Traversing of the Attractor
Landscape of Boolean Models in the Context of Cellular Reprogramming
- URL: http://arxiv.org/abs/2402.08491v2
- Date: Tue, 20 Feb 2024 14:40:23 GMT
- Title: Deep Reinforcement Learning for Controlled Traversing of the Attractor
Landscape of Boolean Models in the Context of Cellular Reprogramming
- Authors: Andrzej Mizera, Jakub Zarzycki
- Abstract summary: We develop a novel computational framework based on deep reinforcement learning that facilitates the identification of reprogramming strategies.
We formulate a control problem in the context of cellular reprogramming for the frameworks of BNs and PBNs under the asynchronous update mode.
We also introduce the notion of a pseudo-attractor and a procedure for identification of pseudo-attractor state during training.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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