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Machine learning enables statistical relationship discovery and quantitative prediction from data, whereas mechanistic modelling captures knowledge and supports inference of causal mechanisms. This review explains why their complementary strengths create clear opportunities when combined as Scientific Machine Learning (SciML). It summarizes recent progress in integrating the two approaches and outlines future avenues for applications across the biological sciences.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/the-rise-of-scientific-machine-learning-a-perspective-on-combining-mechanistic-modelling-with-machine-learning-for-systems-biology-290482/290482/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/the-rise-of-scientific-machine-learning-a-perspective-on-combining-mechanistic-modelling-with-machine-learning-for-systems-biology-290482/290482.png","ImageObject",300,407,{"name":92,"@type":93},"Maeve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main rationale for combining machine learning with mechanistic modelling in systems biology?","Question",{"text":113,"@type":114},"Machine learning provides statistical relationships and quantitative predictions from data, while mechanistic modelling captures knowledge and infers causal mechanisms. Their strengths are complementary, so combining them can yield substantial gains.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What does SciML refer to in the review?",{"text":118,"@type":114},"SciML stands for Scientific Machine Learning, the field focused on combining machine learning with mechanistic modelling.",{"name":120,"@type":111,"acceptedAnswer":121},"Which topics does the review cover beyond describing SciML?",{"text":122,"@type":114},"It discusses recent advances in combining these approaches and points out future avenues for applying SciML in biological sciences.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290482,1789641793,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":105,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},5909877438554,"https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272","UvA-DARE (Digital Academic Repository)  \nThe rise of scientific machine learning  \na perspective on combining mechanistic modelling with machine learning for systems biology  \nNoordijk, B. ; Garcia Gomez, M. L. ; ten Tusscher, K. H.W.J. ; de Ridder, D. ; van Dijk, A. D.J. ; Smith, R.W.  \nDOI  \n10.3389/fsysb.2024.1407994  \nPublication date  \n2024  \nDocument Version  \nFinal published version  \nPublished in  \nFrontiers in Systems Biology  \nLicense  \nCC BY  \nLink to publication  \nCitation for published version (APA):  \nNoordijk, 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)  \nGeneral rights  \nIt 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) .  \nDisclaimer/Complaints regulations  \nIf 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, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-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))  \nDownload date:28 May 2025  \nTYPE Review  \nPUBLISHED 02 August 2024  \nDOI 10.3389/fsysb.2024.1407994  \nOPEN ACCESS  \nEDITED BY  \nRahuman S. Malik-Sheriff,  \nEuropean Bioinformatics Institute (EMBL-EBI), United Kingdom  \nREVIEWED BY  \nSubash Balsamy,  \nKing Abdullah University of Science and Technology, Saudi Arabia  \nDilan Pathirana,  \nUniversity of Bonn, Germany  \n*CORRESPONDENCE  \nRobert W. Smith,  \n [robert1.smith@wur.nl](robert1.smith@wur.nl)  \nRECEIVED 27 March 2024  \nACCEPTED 03 July 2024  \nPUBLISHED 02 August 2024  \nCITATION  \nNoordijk B, Garcia Gomez ML,  \nten 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.  \nFront. Syst. Biol. 4:1407994 .  \ndoi: 10.3389/fsysb.2024.1407994  \nCOPYRIGHT  \n© 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.  \nThe rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology  \nBen 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*  \n1Bioinformatics 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 Insti","cbCaifzAG3E43yqO","https://ap.wps.com/l/cbCaifzAG3E43yqO","pdf",1762053,"English","# Introduction\n## Machine learning and mechanistic modelling in systems biology\n## Scientific Machine Learning (SciML)\n## Future directions and applications","[{\"question\":\"What is the main rationale for combining machine learning with mechanistic modelling in systems biology?\",\"answer\":\"Machine learning provides statistical relationships and quantitative predictions from data, while mechanistic modelling captures knowledge and infers causal mechanisms. Their strengths are complementary, so combining them can yield substantial gains.\"},{\"question\":\"What does SciML refer to in the review?\",\"answer\":\"SciML stands for Scientific Machine Learning, the field focused on combining machine learning with mechanistic modelling.\"},{\"question\":\"Which topics does the review cover beyond describing SciML?\",\"answer\":\"It discusses recent advances in combining these approaches and points out future avenues for applying SciML in biological sciences.\"}]","The rise of scientific machine learning - a perspective on combining mechanistic modelling with machine learning for systems biology | PDF",35]