A major challenge in gene regulatory networks (GRN) of biological systems is to discover when and what interventions should be applied to shift them to healthy phenotypes. A set of gene activity profiles, called basin of attraction (BOA), takes this network to a specific phenotype; therefore, a healthy BOA leads the GRN to a healthy phenotype. However, without the complete observability of the genes, it is not possible to identify whether the current BOA is healthy. In this article we investigate external interventions in GRN with partial observability aiming to bring it to healthy BOAs. We propose a new batch reinforcement learning method (BRL), called mSFQI, to define intervention strategies based on the probabilities of the gene activity profiles being in healthy BOAs, which are calculated from a set of previous observed experiences. BRL uses approximation functions and repeated applications of previous experiences to accelerate learning. Results demonstrate that our proposal can quickly shift a partially observable GRN to healthy BOAs, while reducing the number of interventions. In addition, when observability is poor, mSFQI produces better results when the probabilities for a greater amount of previous observations are available.

生物系统基因调控网络 (GRN) 的一个主要挑战是发现何时以及应该采取何种干预措施来将它们转变为健康的表型。一组称为吸引盆 (BOA) 的基因活动图谱将该网络带入特定的表型；因此，健康的 BOA 会将 GRN 引导至健康的表型。然而，如果没有基因的完全可观察性，就不可能确定当前的 BOA 是否健康。在本文中，我们研究了具有部分可观察性的 GRN 的外部干预，旨在将其引入健康的 BOA。我们提出了一种新的批量强化学习方法 (BRL)，称为 mSFQI，根据基因活动谱在健康 BOA 中的概率来定义干预策略，这些概率是根据一组先前观察到的经验计算得出的。BRL 使用近似函数和先前经验的重复应用来加速学习。结果表明，我们的提议可以快速将部分可观察的 GRN 转变为健康的 BOA，同时减少干预次数。此外，当可观察性较差时，当有大量先前观察的概率可用时，mSFQI 会产生更好的结果。