Bridging the gap between mechanistic biological models and machine learning surrogates

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)

Abstract

Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning (ML) models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying ML models. Application-wise, we show how ML surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate ML models may hold the key to making the simulation of complex biological systems possible using a typical desktop computer.
Original languageEnglish
Article numbere1010988
Pages (from-to)e1010988
JournalPLoS Computational Biology
Volume19
Issue number4
DOIs
Publication statusPublished - 20 Apr 2023

Bibliographical note

Publisher Copyright:
Copyright: © 2023 Gherman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Structured keywords

  • Mathematics and Computational Biology
  • Engineering Mathematics Research Group
  • Bristol BioDesign Institute
  • BrisEngBio

Keywords

  • engineering biology
  • machine learning
  • systems biology

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