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In fibrotic diseases, fibroblasts are a primary driver of disease progression through excess collagen secretion and enhanced contractility. Replacing native tissue with a collagen rich fibrotic scar leads to a decline in tissue function. Recent research into idiopathic pulmonary fibrosis (IPF) has identified fibroblast subpopulations that may be primed for the hyper-activation that leads to increased progression of fibrotic disease. Understanding the contribution of these subpopulations to disease progression requires integrating experimental and computational techniques to understand their dynamic contributions to tissue phenotype. Herein, we introduce a framework for modeling subpopulations using a multiscale mechanistic computational model to understand differences within subpopulations, at the intracellular level and how these differences contribute to cell-and tissue-level pathology. We build and validate this framework using two well-defined subpopulations of fibroblasts in IPF. The subpopulations are defined by the presence or absence of Thy-1, a cell-surface protein that regulates fibroblast mechanosensing. We first developed a logic-based network model of a fibroblast. We then applied this model to identify sub-networks that regulate myofibroblast marker expression in the two subpopulations. Coupling this with an agent-based model (ABM) of the lung microenvironment, we observed how different rules regulating cell fate decisions in each subpopulation affected collagen content. Computational image outputs were analyzed with the open-source biological image analysis software QuPath to quantify how changes in subpopulation dynamics change model-predicted foci characteristics such as size and collagen density. We find that the ability for Thy-1\n                  <jats:sup>+<\/jats:sup>\n                  fibroblasts to transition to Thy-1\n                  <jats:sup>-<\/jats:sup>\n                  fibroblasts significantly increases total collagen content, as well as influences fibrotic foci characteristics. Overall, we present a combined experimental and computational framework for studying how dynamic changes in fibroblast subpopulations lead to tissue-level disease phenotypes.\n                <\/jats:p>\n                <jats:sec>\n                  <jats:title>Author Summary<\/jats:title>\n                  <jats:p>\n                    In wound healing fibroblasts are responsible for rebuilding the scaffolding, called the extracellular matrix (ECM), of the damaged tissue to aid in regeneration. In fibrosis, the normal wound healing processes are hijacked leading to overproduction of ECM proteins, such as collagen, by fibroblasts leading to fibrosis. In fibrotic diseases with no known cause, such as idiopathic pulmonary fibrosis (IPF), subpopulations of fibroblasts have been identified as possible drivers of disease. Herein, we use a multiscale computational model that represents intracellular, cellular, and tissue level signaling to study how the presence or absence of a single protein on a fibroblast\u2019s surface, Thy-1, affects the formation of fibrotic scar in IPF. Thy-1 regulates how a fibroblast senses the stiffness of the microenvironment. The absence of Thy-1 impacted intracellular signaling and when combined across many cells led to fibrosis at the tissue level. Additionally, if normal Thy-1\n                    <jats:sup>+<\/jats:sup>\n                    fibroblasts could dynamically lose Thy-1 expression the amount of collagen they produced correlated with that in end stage IPF lungs. This model presents a framework for studying different fibroblast subpopulations using multiscale computational modeling to understand how changes in a single protein in one cell can, over an entire population, affect the dynamics of disease progression.\n                  <\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.08.03.742640","type":"posted-content","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T16:56:49Z","timestamp":1785862609000},"source":"Crossref","is-referenced-by-count":0,"title":["Computational Analysis of Fibroblast Subpopulation Dynamics as a Driver of Fibrotic Foci Formation"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6278-7190","authenticated-orcid":false,"given":"Julie","family":"Leonard-Duke","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA"},{"name":"Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David J.","family":"Csordas","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA"},{"name":"Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riley T.","family":"Hannan","sequence":"additional","affiliation":[{"name":"Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA"},{"name":"Division of Pulmonary and Critical Care, Department of Medicine, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine","family":"Sano","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darius","family":"Hossainian","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariska","family":"Batavia","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Andrews","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariapaola","family":"Ambrosone","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taylor G.","family":"Eggertsen","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA"},{"name":"Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tania E.","family":"Velez","sequence":"additional","affiliation":[{"name":"Division of Pulmonary and Critical Care, Department of Medicine, University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2731-3299","authenticated-orcid":false,"given":"Jeffrey M.","family":"Sturek","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA"},{"name":"Robert M. 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