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.

翻译：细胞重编程可用于多种疾病的预防与治疗。然而，通过传统湿实验发现重编程策略的效率受限于漫长的周期和高昂的成本。本研究提出了一种基于深度强化学习的新型计算框架，可有效识别重编程策略。为此，我们针对异步更新模式下的布尔网络和概率布尔网络框架，构建了细胞重编程场景下的控制问题。进一步地，我们引入了伪吸引子的概念及其训练过程中的状态识别方法。最终，我们设计了解该控制问题的计算框架，并在多个不同模型上进行了验证测试。