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Automated Deep Abstractions for Stochastic Chemical Reaction Networks
[article]
2020
arXiv
pre-print
Predicting stochastic cellular dynamics as emerging from the mechanistic models of molecular interactions is a long-standing challenge in systems biology: low-level chemical reaction network (CRN) models give raise to a highly-dimensional continuous-time Markov chain (CTMC) which is computationally demanding and often prohibitive to analyse in practice. A recently proposed abstraction method uses deep learning to replace this CTMC with a discrete-time continuous-space process, by training a
arXiv:2002.01889v1
fatcat:623bkhtxgfbdzhoe77ochltoma