Deep reinforcement learning for optimal experimental design in biology

Neythen J Treloar, Nathan Braniff, Brian Ingalls and Chris P Barnes

PLOS Computational Biology, 2022, vol. 18, issue 11, 1-24

Abstract: The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence—reinforcement learning—to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty.Author summary: Biological systems are often complex and typically exhibit non-linear behaviour, making accurate model parametrisation difficult. Optimal experimental design tools help address this problem by identifying experiments that are predicted to provide maximally informative data for parameter inference. In this work we use reinforcement learning, an artificial intelligence method, to determine such experiments. Our simulation studies show that this approach allows uncertainty in model parameterisation to be directly incorporated into the search for optimal experiments, opening a practical avenue for training an experimental controller. We present this method as complementary to existing optimisation approaches and we anticipate that artificial intelligence has a valuable role to play in the future of optimal experimental design.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1010695

DOI: 10.1371/journal.pcbi.1010695

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