Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Bridging the Gap between Mechanistic Biological Models and Machine Learning Surrogates

Version 1 : Received: 28 September 2022 / Approved: 29 September 2022 / Online: 29 September 2022 (07:17:28 CEST)

A peer-reviewed article of this Preprint also exists.

Gherman, I.M.; Abdallah, Z.S.; Pang, W.; Gorochowski, T.E.; Grierson, C.S.; Marucci, L. Bridging the Gap between Mechanistic Biological Models and Machine Learning Surrogates. PLOS Computational Biology 2023, 19, e1010988, doi:10.1371/journal.pcbi.1010988. Gherman, I.M.; Abdallah, Z.S.; Pang, W.; Gorochowski, T.E.; Grierson, C.S.; Marucci, L. Bridging the Gap between Mechanistic Biological Models and Machine Learning Surrogates. PLOS Computational Biology 2023, 19, e1010988, doi:10.1371/journal.pcbi.1010988.

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 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 machine learning models. Application-wise, we show how machine learning 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 machine learning models may hold the key to making the simulation of complex biological models possible using a typical desktop computer.

Keywords

systems biology; machine learning; surrogate model

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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