Towards AI-enabled mechanistic modeling of plant metabolic pathways

Towards AI-enabled mechanistic modeling of plant metabolic pathways | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 31 March 2026 V1 Latest version Share on Towards AI-enabled mechanistic modeling of plant metabolic pathways Authors : Philipp Wendering 0000-0002-0155-6217 [email protected] and Zoran Nikoloski Authors Info & Affiliations https://doi.org/10.22541/au.177499337.78584296/v1 216 views 142 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Advances in computational power and structured representation of biochemical knowledge have fostered the development and use of models of plant metabolic pathways of increasing complexity. These advances have pointed out knowledge gaps about parameters inherent to these models and the need for large-scale data sets to conduct effective model calibrations. This bottleneck hampers wider application of plant metabolic models in plant biotechnology and crop breeding. Here, we argue that the recent advances in artificial intelligence offer the means for efficient model parameterization while strategically guiding the use of resources for data generation to increase precision of parameter values. We focus on surrogate models that have been applied with both stoichiometric and kinetic metabolic models as well as recent approaches for AI-enabled model parameterization using different data inputs. The review does not only showcase applications with plant metabolic pathways, but also points at recent examples from non-plant and chemical systems that can be readily adopted in the study of plant metabolism. Finally, we point at limitations and future directions that may deepen the synergies between machine / deep learning and mechanistic metabolic models to enable predictions of and mitigating actions for plant responses adapted to future climate scenarios. Supplementary Material File (manuscript_preprint.pdf) Download 1.24 MB File (supplementary_material_preprint.pdf) Download 203.85 KB Information & Authors Information Version history V1 Version 1 31 March 2026 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License Keywords artificial intelligence deep learning kinetic metabolic models machine learning model parameterization plant metabolic pathways surrogate models Authors Affiliations Philipp Wendering 0000-0002-0155-6217 [email protected] Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam View all articles by this author Zoran Nikoloski Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology View all articles by this author Metrics & Citations Metrics Article Usage 216 views 142 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Philipp Wendering, Zoran Nikoloski. Towards AI-enabled mechanistic modeling of plant metabolic pathways. Authorea . 31 March 2026. DOI: https://doi.org/10.22541/au.177499337.78584296/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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