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<dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:invenio="http://invenio-software.org/elements/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:identifier>doi:10.1007/s00366-022-01654-1</dc:identifier><dc:language>eng</dc:language><dc:creator>Camacho-Gómez, Daniel</dc:creator><dc:creator>García-Aznar, José Manuel</dc:creator><dc:creator>Gómez-Benito, María José</dc:creator><dc:title>A 3D multi-agent-based model for lumen morphogenesis: the role of the biophysical properties of the extracellular matrix</dc:title><dc:identifier>ART-2022-128835</dc:identifier><dc:description>The correct function of many organs depends on proper lumen morphogenesis, which requires the orchestration of both biological and mechanical aspects. However, how these factors coordinate is not yet fully understood. Here, we focus on the development of a mechanistic model for computationally simulating lumen morphogenesis. In particular, we consider the hydrostatic pressure generated by the cells'' fluid secretion as the driving force and the density of the extracellular matrix as regulators of the process. For this purpose, we develop a 3D agent-based-model for lumen morphogenesis that includes cells'' fluid secretion and the density of the extracellular matrix. Moreover, this computer-based model considers the variation in the biological behavior of cells in response to the mechanical forces that they sense. Then, we study the formation of the lumen under different-mechanical scenarios and conclude that an increase in the matrix density reduces the lumen volume and hinders lumen morphogenesis. Finally, we show that the model successfully predicts normal lumen morphogenesis when the matrix density is physiological and aberrant multilumen formation when the matrix density is excessive.</dc:description><dc:date>2022</dc:date><dc:source>http://zaguan.unizar.es/record/120904</dc:source><dc:doi>10.1007/s00366-022-01654-1</dc:doi><dc:identifier>http://zaguan.unizar.es/record/120904</dc:identifier><dc:identifier>oai:zaguan.unizar.es:120904</dc:identifier><dc:relation>info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE</dc:relation><dc:relation>This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE</dc:relation><dc:relation>info:eu-repo/grantAgreement/ES/MICINN/RTI2018-094494-B-C21</dc:relation><dc:identifier.citation>ENGINEERING WITH COMPUTERS 34 (2022), 4135–4149</dc:identifier.citation><dc:rights>by</dc:rights><dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights><dc:rights>info:eu-repo/semantics/openAccess</dc:rights></dc:dc>

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