Topology-Driven Systems Modelling of M.Tuberculosis Host–Pathogen Dynamics Reveals Network Control Nodes Governing Infection Outcome

Topology-Driven Systems Modelling of M.Tuberculosis Host–Pathogen Dynamics Reveals Network Control Nodes Governing Infection Outcome | 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 Research Article Topology-Driven Systems Modelling of M.Tuberculosis Host–Pathogen Dynamics Reveals Network Control Nodes Governing Infection Outcome Enos Jadlin G This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8990051/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 Understanding the multiscale dynamics of host–pathogen interactions remains a central challenge in systems biology. Here, we develop an integrated computational framework that unifies mechanistic pathway modeling with global network topology to study infection dynamics in Mycobacterium tuberculosis (MTB). We first construct a four-variable ordinary differential equation (ODE) model grounded in curated immune signaling architecture, capturing the population dynamics of extracellular bacilli, macrophages, activated immune cells, and infected host compartments. While this mechanistic formulation preserves biological detail, its dimensional complexity limits analytical tractability. To address this, we incorporate global interactome topology derived from large-scale pathway integration and centrality analysis. Network metrics reveal a structural bottleneck characterized by two dominant opposing regulatory forces: pathogen proliferation driven by metabolic and energetic hubs, and host immune activation governed by highly connected sensing and signaling modules. Guided by this topological structure, we perform a coarse-grained dynamical reduction that aggregates immune and pathogen compartments into effective variables representing collective host defense and pathogen load. The resulting reduced system preserves the emergent interaction laws of the full mechanistic model while yielding a mathematically transparent two-variable dynamical framework. This topology-guided reduction establishes a principled bridge between pathway-level mechanistic detail and systems-level abstraction. More broadly, the study introduces a scalable strategy for translating complex biological networks into analytically tractable dynamical models, providing a generalizable foundation for multiscale modeling of host–pathogen systems. Systems Biology Computational Biology Infectious Diseases General Microbiology Bioinformatics Multiscale modeling Host–pathogen interactions Systems biology Network topology Molecular networks Dynamical systems Ordinary differential equations Coarse-grained modeling Interactome analysis Mycobacterium tuberculosis Full Text Additional Declarations The authors declare no competing interests. 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-8990051","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598268706,"identity":"72619533-c34d-4e61-986a-2b328cd9e75c","order_by":0,"name":"Enos Jadlin G","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie3PMUvDQBTA8Xcc3HSa9YUW/QopQih08IM4lUBccnuGWK4U4lKcM5T2K+iSucfBuQRcAy4Fv0BAkA4iXkTolKaj4P2H4z14v+EAXK6/GgIdMiBSf6Z2o1SeRDgDqna8agk5gdgzDsCi4Cxvlx7iDRa6GWcTfj5Yhuiv7268e0v2adlJ/JWJEc0tZ8MqxFH5LApNJFlWr50kqJMQkGnOMAlxWhohLaEk7ybXdXLV4NcvUSsjNn0kwCRAP29JHI3mMhOPfQTrOEb/wf4FI/UGZiueLFHH/uIVkX7Hj8nFZTGVGrKZWL9otdun3aSN4mHWP+/26L2NNId51nfscrlc/7BvZMpWI2yuYfsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0004-1716-6522","institution":"Unaffiliated","correspondingAuthor":true,"prefix":"","firstName":"Enos","middleName":"Jadlin","lastName":"G","suffix":""}],"badges":[],"createdAt":"2026-02-27 16:13:47","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8990051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8990051/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103722017,"identity":"d721ddb0-63b7-4eb9-b7b8-556593c2e8b1","added_by":"auto","created_at":"2026-03-02 07:28:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":695070,"visible":true,"origin":"","legend":"","description":"","filename":"Preprint.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8990051/v1_covered_f7a20f73-67ff-4875-a2a3-a1b368ab43fe.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eTopology-Driven Systems Modelling of \u003cem\u003eM.Tuberculosis\u003c/em\u003e Host–Pathogen Dynamics Reveals Network Control Nodes Governing Infection Outcome\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"unaffiliated","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":"Multiscale modeling, Host–pathogen interactions, Systems biology, Network topology, Molecular networks, Dynamical systems, Ordinary differential equations, Coarse-grained modeling, Interactome analysis, Mycobacterium tuberculosis","lastPublishedDoi":"10.21203/rs.3.rs-8990051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8990051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the multiscale dynamics of host\u0026ndash;pathogen interactions remains a central challenge in systems biology. Here, we develop an integrated computational framework that unifies mechanistic pathway modeling with global network topology to study infection dynamics in \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB). We first construct a four-variable ordinary differential equation (ODE) model grounded in curated immune signaling architecture, capturing the population dynamics of extracellular bacilli, macrophages, activated immune cells, and infected host compartments. While this mechanistic formulation preserves biological detail, its dimensional complexity limits analytical tractability.\u003c/p\u003e \u003cp\u003eTo address this, we incorporate global interactome topology derived from large-scale pathway integration and centrality analysis. Network metrics reveal a structural bottleneck characterized by two dominant opposing regulatory forces: pathogen proliferation driven by metabolic and energetic hubs, and host immune activation governed by highly connected sensing and signaling modules. Guided by this topological structure, we perform a coarse-grained dynamical reduction that aggregates immune and pathogen compartments into effective variables representing collective host defense and pathogen load.\u003c/p\u003e \u003cp\u003eThe resulting reduced system preserves the emergent interaction laws of the full mechanistic model while yielding a mathematically transparent two-variable dynamical framework. This topology-guided reduction establishes a principled bridge between pathway-level mechanistic detail and systems-level abstraction. 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