Deep Reinforcement Learning for Controlled Traversing of the Attractor Landscape of Boolean Models in the Context of Cellular Reprogramming
<|reference_start|>Deep Reinforcement Learning for Controlled Traversing of the Attractor Landscape of Boolean Models in the Context of Cellular Reprogramming: 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.<|reference_end|>
@article{mizera2024deep,
    title={Deep Reinforcement Learning for Controlled Traversing of the Attractor
  Landscape of Boolean Models in the Context of Cellular Reprogramming},
    author={Andrzej Mizera, Jakub Zarzycki},
    journal={arXiv preprint arXiv:2402.08491},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08491},
    primaryClass={cs.LG cs.AI q-bio.MN q-bio.QM}
}