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REVIEW article

Front. Syst. Biol.
Sec. Data and Model Integration
Volume 4 - 2024 | doi: 10.3389/fsysb.2024.1407994
This article is part of the Research Topic Insights in Data and Model Integration: 2023 View all 3 articles

The rise of Scientific Machine Learning: A perspective on combining mechanistic modelling with machine learning for systems biology

Provisionally accepted
Ben Noordijk Ben Noordijk 1,2Monica L. Garcia Gomez Monica L. Garcia Gomez 2,3Kirsten H. Ten Tusscher Kirsten H. Ten Tusscher 2,3Dick de Ridder Dick de Ridder 1,2Aalt D. Van Dijk Aalt D. Van Dijk 2,4Robert W. Smith Robert W. Smith 5*
  • 1 Bioinformatics Group, Wageningen University and Research, Wageningen, Netherlands
  • 2 CropXR Institute, Utrecht, Netherlands, Netherlands
  • 3 Experimental and Computational Plant Development, Institute of Environmental Biology; Theoretical Biology, Institute of Biodynamics and Biocomplexity, Department of Biology, Utrecht University, Utrecht, Netherlands
  • 4 Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
  • 5 Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands

The final, formatted version of the article will be published soon.

    Both machine learning and mechanistic modelling approaches have been used independently with great success in systems biology. Machine learning excels in deriving statistical relationships and quantitative prediction from data, while mechanistic modelling is a powerful approach to capture knowledge and infer causal mechanisms underpinning biological phenomena. Importantly, the strengths of one are the weaknesses of the other, which suggests that substantial gains can be made by combining machine learning with mechanistic modelling, a field referred to as Scientific Machine Learning (SciML). In this review we discuss recent advances in combining these two approaches for systems biology, and point out future avenues for its application in the biological sciences.

    Keywords: machine learning, mechanistic models, Scientific machine learning (SciML), ordinary differential equations, system identification, parameter estimation, Biology-informed neural network (BINN)

    Received: 27 Mar 2024; Accepted: 03 Jul 2024.

    Copyright: © 2024 Noordijk, Garcia Gomez, Ten Tusscher, de Ridder, Van Dijk and Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Robert W. Smith, Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, 6708, Netherlands

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.