Predicting stochastic cellular dynamics emerging from chemical reaction networks (CRNs) is a long-standing challenge in systems biology. Deep learning was recently used to abstract the CRN dynamics by a mixture density neural network, trained with traces of the original process. Such abstraction is dramatically cheaper to execute, yet it preserves the statistical features of the training data. However, in practice, the modeller has to take care of finding the suitable neural network architecture manually, for each given CRN, through a trial-and-error cycle. In this paper, we propose to further automatise deep abstractions for stochastic CRNs, through learning the neural network architecture along with learning the transition kernel of the stochastic process. The method is applicable to any given CRN, time-saving for deep learning experts and crucial for non-specialists. We demonstrate performance over a number of CRNs with multi-modal phenotypes and a multi-scale scenario where CRNs interact across a spatial grid.

预测来自化学反应网络 (CRN) 的随机细胞动力学是系统生物学中长期存在的挑战。深度学习最近被用于通过混合密度神经网络抽象 CRN 动力学，并使用原始过程的痕迹进行训练。这种抽象的执行成本要低得多，但它保留了训练数据的统计特征。然而，在实践中，建模者必须通过反复试验为每个给定的 CRN 手动找到合适的神经网络架构。在本文中，我们建议通过学习神经网络架构以及学习随机过程的转换核来进一步自动化随机 CRN 的深度抽象。该方法适用于任何给定的 CRN，为深度学习专家节省时间，对非专家至关重要。我们展示了在多个 CRN 上的性能，这些 CRN 具有多模态表型和多尺度场景，其中 CRN 跨空间网格交互。