The rise of scientific machine learning - A perspective on combining mechanistic modelling with machine learning for systems biology

Aug 2, 2026

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Machine learning and mechanistic modelling have both achieved strong results in systems biology, yet each has distinct limitations: data-driven methods excel at extracting statistical relationships and making quantitative predictions, while mechanistic models encode knowledge and support inference of causal mechanisms. Combining them enables substantial performance gains in a field termed Scientific Machine Learning (SciML). This review surveys recent advances in integrating these approaches for systems biology and outlines practical directions for applying SciML across biological research.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Mechanistic modelling and its limitations

Machine learning strengths in systems biology

Scientific Machine Learning (SciML) and integration rationale

Review scope and future application directions

UvA-DARE (Digital Academic Repository) The rise of scientific machine learning a perspective on combining mechanistic modelling with machine learning for systems biology Noordijk, B. ; Garcia Gomez, M. L. ; ten Tusscher, K. H.W.J. ; de Ridder, D. ; van Dijk, A. D.J. ; Smith, R.W. DOI 10.3389/fsysb.2024.1407994 Publication date 2024 Document Version Final published version Published in Frontiers in Systems Biology License CC BY Link to publication Citation for published version (APA): Noordijk, B. , Garcia Gomez, M. L. , ten Tusscher, K. H. W. J. , de Ridder, D. , van Dijk, A. D. J. ,& Smith, R. W. (2024) . The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology. Frontiers in Systems Biology, 4, Article 1407994. [https://doi.org/10.3389/fsysb.2024.1407994](https://doi.org/10.3389/fsysb.2024.1407994) General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) . Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, P.O. Box 19185 , 1000 GD Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl)) Download date:02 Aug 2026 TYPE Review PUBLISHED 02 August 2024 DOI 10.3389/fsysb.2024.1407994 OPEN ACCESS EDITED BY Rahuman S. Malik-Sheriff, European Bioinformatics Institute (EMBL-EBI), United Kingdom REVIEWED BY Subash Balsamy, King Abdullah University of Science and Technology, Saudi Arabia Dilan Pathirana, University of Bonn, Germany *CORRESPONDENCE Robert W. Smith, [robert1.smith@wur.nl](robert1.smith@wur.nl) RECEIVED 27 March 2024 ACCEPTED 03 July 2024 PUBLISHED 02 August 2024 CITATION Noordijk B, Garcia Gomez ML, ten Tusscher KHWJ, de Ridder D, van Dijk ADJ and Smith RW (2024), The rise of scientiﬁc machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology. Front. Syst. Biol. 4:1407994 . doi: 10.3389/fsysb.2024.1407994 COPYRIGHT © 2024 Noordijk, Garcia Gomez, ten Tusscher, de Ridder, van Dijk and Smith. This is an openaccess 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) and the copyright owner(s) 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. The rise of scientiﬁc machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology Ben Noordijk 1,2, Monica L. Garcia Gomez 2,3, Kirsten H. W. J. ten Tusscher 2,3, Dick de Ridder 1,2, Aalt D. J. van Dijk 2,4 and Robert W. Smith 5* 1Bioinformatics Group, Wageningen University and Research, Wageningen, Netherlands, 2CropXR Institute, Utrecht, Netherlands, 3Experimental and Computational Plant Development, Institute of Environmental Biology; Theoretical Biology, Institute of Biodynamics and Biocomplexity, Department of Biology, Utrecht University, Utrecht, Netherlands, 4Biosystems Data Analysis, Swammerdam

FAQs

What is Scientific Machine Learning (SciML) in systems biology?

SciML refers to combining machine learning with mechanistic modelling to leverage the strengths of both approaches for understanding and predicting biological systems.

How do mechanistic modelling and machine learning complement each other?

Machine learning derives statistical relationships and quantitative predictions from data, while mechanistic modelling captures knowledge and infers causal mechanisms; the weaknesses of one approach can be addressed by the other.

What does the review focus on?

The review discusses recent advances in combining these two approaches for systems biology and points to future avenues for their application in biological sciences.

Aug 2, 2026

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