{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T14:20:23Z","timestamp":1788445223100,"version":"build-2803163510"},"reference-count":150,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:p>Tuberculosis (TB) remains a global health challenge, driven by multiscale pathogenesis across molecular, cellular, tissue, and systemic levels. Within-host mathematical modelling provides a powerful way to explore these processes, providing mechanistic insights that are difficult to capture experimentally, including host-pathogen interactions, immune regulation, and treatment efficacy. This review synthesises the evolution of TB models from early ordinary differential equation systems to agent-based and hybrid multiscale approaches, leading to integrated pharmacokinetic\/pharmacodynamic and systems pharmacology frameworks. We highlight advances in omics-driven personalisation, virtual clinical trials for regimen optimisation, multiscale and multicompartment extensions, translational impact, evolutionary dynamics, and efforts to improve reproducibility. We also examine modelling strategies for TB coinfections and comorbidities, including HIV and diabetes, which reshape granuloma biology and drug exposure. Unlike previous reviews, we integrate historical and recent within-host tuberculosis modelling advances and present quantitative visual summaries in the form of theme-by-approach heatmaps. In this work, we identify emerging opportunities and current gaps in the landscape of TB models. In addition, we develop a unique workflow for this review that can be adapted for other modelling topics. Our synthesis provides a roadmap towards computational models that may support future precision therapy and TB control strategies.<\/jats:p>","DOI":"10.3389\/fimmu.2026.1920524","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T14:05:20Z","timestamp":1788444320000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The eight pillars of within-host tuberculosis modelling"],"prefix":"10.3389","volume":"17","author":[{"given":"Roselyn F.","family":"Kaondera-Shava","sequence":"first","affiliation":[{"name":"Centre for Mathematical Biology, Department of Mathematical Sciences, University of Bath","place":["Bath, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian T.","family":"Michael","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Immunology, University of Michigan\u2013Michigan Medicine","place":["Ann Arbor, MI, 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