Automated deep abstractions for stochastic chemical reaction networks

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.