A Proteomics-Informed Geometric Framework for Identifiability and Panel Design in Genome-Scale Metabolic Networks

A Proteomics-Informed Geometric Framework for Identifiability and Panel Design in Genome-Scale Metabolic Networks | 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 Article A Proteomics-Informed Geometric Framework for Identifiability and Panel Design in Genome-Scale Metabolic Networks Anas Enoch This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8929972/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Genome-scale metabolic models provide a stoichiometric description of biochemical networks, yet most analytical frameworks remain data- or flux-centric, implicitly assuming full observability or well-defined flux states. Under realistic experimental conditions, metabolomics measurements capture only a sparse and condition-dependent subset of metabolites, raising a more fundamental question: which aspects of metabolic mechanism remain identifiable under partial observation, and how should measurements be designed to preserve them? Here, we introduce an operator-centric, metabolite-focused geometric framework for metabolism that treats each biological condition as a mechanistic operator rather than a collection of measured variables or inferred fluxes. Starting from stoichiometry, we construct a Dirac-operator–based formulation whose induced metabolite Laplacian encodes reaction-mediated coupling. Condition-specific gene-level proteomics enter exclusively as modulators of reaction coupling through gene–protein–reaction rules, yielding a family of condition-dependent mechanistic operators that define a spectral geometry on the space of metabolites. Within this framework, partial metabolomic observability is formalized as an operator restriction problem, and identifiability is defined as the stability of low-frequency operator geometry under metabolite masking. This definition is agnostic to steady-state assumptions and avoids imputing unobserved quantities. Building on this criterion, we derive a geometry-aware active measurement strategy that selects metabolite panels which optimally preserve mechanistic structure across conditions. Applying the framework to the human genome-scale metabolic model Human1, we show that proteomics- informed operator geometry is substantially more stable under partial observation than topology-based or unweighted baselines, and that compact, condition-aware metabolite panels can be identified without relying on flux optimization or heuristic centrality measures. Together, this work reframes metabolic analysis around mechanistic operator geometry, providing a principled approach to identifiability and experimental design under sparse, noisy, and condition-specific molecular measurements. Biological sciences/Biophysics Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Biological sciences/Systems biology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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. 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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-8929972","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":596813106,"identity":"6a71eea7-7e92-4398-a88d-7c7fc5607ab5","order_by":0,"name":"Anas Enoch","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYFAD9gYG5h+kaeE5wMBMoi0SCURq4Z/d/PgD4w6bxO0z35huLmCwk2cQO/wAv9l3jplJMJ5JS5xzO8fs9gyGZMMG6TQD/NbcSDBjYGw7nDhDOsfsBg8DcwKDdAJ+LfI30j9/AGuRPAPSUg/Ukv4BrxaDGzkGEmAtEjxmt3kYDgO15OC3xfBGThlQS5rxDJ60spszDI4btknnFODVIncjfTPQYTayM9gPb7vxoaJanl86fQNeLSDA/AfhTgYGNoLqR8EoGAWjYBQQBACmL0EJOQGrawAAAABJRU5ErkJggg==","orcid":"","institution":"Université Mohammed VI des Sciences de la Santé","correspondingAuthor":true,"prefix":"","firstName":"Anas","middleName":"","lastName":"Enoch","suffix":""}],"badges":[],"createdAt":"2026-02-21 02:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8929972/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8929972/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106415084,"identity":"cdeb68e2-75b2-45d8-bc16-e65f92f7b9fd","added_by":"auto","created_at":"2026-04-08 10:32:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":444027,"visible":true,"origin":"","legend":"","description":"","filename":"ProteomicsInformedGeometric2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8929972/v1_covered_fc68c0f0-c704-4cbc-b350-96f38a659016.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Proteomics-Informed Geometric Framework for Identifiability and Panel Design in Genome-Scale Metabolic Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8929972/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8929972/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenome-scale metabolic models provide a stoichiometric description of biochemical networks, yet most analytical frameworks remain data- or flux-centric, implicitly assuming full observability or well-defined flux states. Under realistic experimental conditions, metabolomics measurements capture only a sparse and condition-dependent subset of metabolites, raising a more fundamental question: which aspects of metabolic mechanism remain identifiable under partial observation, and how should measurements be designed to preserve them?\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we introduce an operator-centric, metabolite-focused geometric framework for metabolism that treats each biological condition as a mechanistic operator rather than a collection of measured variables or inferred fluxes. Starting from stoichiometry, we construct a Dirac-operator–based formulation whose induced metabolite Laplacian encodes reaction-mediated coupling. Condition-specific gene-level proteomics enter exclusively as modulators of reaction coupling through gene–protein–reaction rules, yielding a family of condition-dependent mechanistic operators that define a spectral geometry on the space of metabolites.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWithin this framework, partial metabolomic observability is formalized as an operator restriction problem, and identifiability is defined as the stability of low-frequency operator geometry under metabolite masking. This definition is agnostic to steady-state assumptions and avoids imputing unobserved quantities. Building on this criterion, we derive a geometry-aware active measurement strategy that selects metabolite panels which optimally preserve mechanistic structure across conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApplying the framework to the human genome-scale metabolic model Human1, we show that proteomics- informed operator geometry is substantially more stable under partial observation than topology-based or unweighted baselines, and that compact, condition-aware metabolite panels can be identified without relying on flux optimization or heuristic centrality measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTogether, this work reframes metabolic analysis around mechanistic operator geometry, providing a principled approach to identifiability and experimental design under sparse, noisy, and condition-specific molecular measurements.\u003c/p\u003e","manuscriptTitle":"A Proteomics-Informed Geometric Framework for Identifiability and Panel Design in Genome-Scale Metabolic Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 04:22:47","doi":"10.21203/rs.3.rs-8929972/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95f09f5a-b6db-415b-93f4-1e82f570a0a8","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63501563,"name":"Biological sciences/Biophysics"},{"id":63501564,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":63501565,"name":"Physical sciences/Mathematics and computing"},{"id":63501566,"name":"Biological sciences/Systems biology"}],"tags":[],"updatedAt":"2026-04-08T14:12:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 04:22:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8929972","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8929972","identity":"rs-8929972","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}