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Deep Reinforcement Learning for Optimal Experimental Design in Biology

View ORCID ProfileNeythen J. Treloar, Nathan Braniff, Brian Ingalls, Chris P. Barnes
doi: https://doi.org/10.1101/2022.05.09.491138
Neythen J. Treloar
1Department of Cell and Developmental Biology, University College London, London, WC1E 6BT, UK
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  • For correspondence: neythen.treloar.14@ucl.ac.uk
Nathan Braniff
2Department of Applied Mathematics, University of Waterloo, Canada
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Brian Ingalls
2Department of Applied Mathematics, University of Waterloo, Canada
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Chris P. Barnes
1Department of Cell and Developmental Biology, University College London, London, WC1E 6BT, UK
3UCL Genetics Institute, University College London, United Kingdom
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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 accurate parameter estimates. 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 without confident knowledge of the system’s parameter values. We present this method as complementary to existing optimisation approaches and we anticipate that artificial intelligence has a fundamental role to play in the future of optimal experimental design.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • * christopher.barnes{at}ucl.ac.uk

  • https://github.com/ucl-cssb/RED

  • https://doi.org/10.5281/zenodo.6521194

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 10, 2022.
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Deep Reinforcement Learning for Optimal Experimental Design in Biology
Neythen J. Treloar, Nathan Braniff, Brian Ingalls, Chris P. Barnes
bioRxiv 2022.05.09.491138; doi: https://doi.org/10.1101/2022.05.09.491138
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Deep Reinforcement Learning for Optimal Experimental Design in Biology
Neythen J. Treloar, Nathan Braniff, Brian Ingalls, Chris P. Barnes
bioRxiv 2022.05.09.491138; doi: https://doi.org/10.1101/2022.05.09.491138

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