A Cell Metamodel Uncovers Mechanistic Drivers of Disease Phenotypes Across Molecular, Cellular, and Tissue Scales

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Abstract Understanding how molecular and cellular dynamics regulate tissue function remains a central challenge in biology. Here, we develop a graph-based metamodeling framework for modeling of complex biological systems and apply it to construct a comprehensive metamodel of the β-cell. The metamodel integrates 115 input models that describe various aspects of β-cell physiology across molecular, cellular, and multicellular spatial scales, spanning ten orders of magnitude in timescales. Validated against a broad range of experimental data, including fluorescence imaging, the metamodel uncovers how the interplay between gap junction coupling and K + channel–mediated signaling regulates islet function. Moreover, the metamodel identifies and characterizes two types of hub cells, each deterministically driving islet activity and synchronization via unique ion channel properties. Perturbing channel conductance and hub cell activity recapitulates distinct diabetic phenotypes, highlighting them as mechanistic drivers of diabetes and potential therapeutic targets. Our metamodeling framework is broadly applicable to modeling other complex biological systems.
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A Cell Metamodel Uncovers Mechanistic Drivers of Disease Phenotypes Across Molecular, Cellular, and Tissue Scales | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Biological Sciences - Article A Cell Metamodel Uncovers Mechanistic Drivers of Disease Phenotypes Across Molecular, Cellular, and Tissue Scales Liping Sun, Chenxi Wang, Jingjing Zheng, Weimin Li, Xianni Zhong, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7731109/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Understanding how molecular and cellular dynamics regulate tissue function remains a central challenge in biology. Here, we develop a graph-based metamodeling framework for modeling of complex biological systems and apply it to construct a comprehensive metamodel of the β-cell. The metamodel integrates 115 input models that describe various aspects of β-cell physiology across molecular, cellular, and multicellular spatial scales, spanning ten orders of magnitude in timescales. Validated against a broad range of experimental data, including fluorescence imaging, the metamodel uncovers how the interplay between gap junction coupling and K + channel–mediated signaling regulates islet function. Moreover, the metamodel identifies and characterizes two types of hub cells, each deterministically driving islet activity and synchronization via unique ion channel properties. Perturbing channel conductance and hub cell activity recapitulates distinct diabetic phenotypes, highlighting them as mechanistic drivers of diabetes and potential therapeutic targets. Our metamodeling framework is broadly applicable to modeling other complex biological systems. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Data integration Biological sciences/Systems biology/Bayesian inference Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SI.pdf Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7731109","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":526054973,"identity":"a7423406-aa3c-4594-936f-315ca2463f81","order_by":0,"name":"Liping 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