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昂贵的成本。本研究提出了一种基于深度强化学习的创新计算框架，旨在辅助识别重编程策略。为此，我们在异步更新模式下，针对布尔网络和概率布尔网络框架下的细胞重编程控制问题进行了形式化建模。进一步，我们引入了伪吸引子的概念，并建立了训练过程中识别伪吸引子状态的方法。最终，我们设计了一套用于解决该控制问题的计算框架，并在多个不同模型上进行了测试验证。