135351 20251121145034.0 doi 10.1093/intbio/zyad002 sideral 138586 ART-2023-138586 eng Movilla, Nieves (orcid)0000-0002-0163-8378 A novel integrated experimental and computational approach to unravel fibroblast motility in response to chemical gradients in 3D collagen matrices 2023 Abstract Fibroblasts play an essential role in tissue repair and regeneration as they migrate to wounded areas to secrete and remodel the extracellular matrix. Fibroblasts recognize chemical substances such as growth factors, which enhance their motility towards the wounded tissues through chemotaxis. Although several studies have characterized single-cell fibroblast motility before, the migration patterns of fibroblasts in response to external factors have not been fully explored in 3D environments. We present a study that combines experimental and computational efforts to characterize the effect of chemical stimuli on the invasion of 3D collagen matrices by fibroblasts. Experimentally, we used microfluidic devices to create chemical gradients using collagen matrices of distinct densities. We evaluated how cell migration patterns were affected by the presence of growth factors and the mechanical properties of the matrix. Based on these results, we present a discrete-based computational model to simulate cell motility, which we calibrated through the quantitative comparison of experimental and computational data via Bayesian optimization. By combining these approaches, we predict that fibroblasts respond to both the presence of chemical factors and their spatial location. Furthermore, our results show that the presence of these chemical gradients could be reproduced by our computational model through increases in the magnitude of cell-generated forces and enhanced cell directionality. Although these model predictions require further experimental validation, we propose that our framework can be applied as a tool that takes advantage of experimental data to guide the calibration of models and predict which mechanisms at the cellular level may justify the experimental findings. Consequently, these new insights may also guide the design of new experiments, tailored to validate the variables of interest identified by the model.  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 This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21 info:eu-repo/semantics/closedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 1.5 2023 CELL BIOLOGY 190 / 205 = 0.927 2023 Q4 T3 0.568 2023 Biophysics 2023 Q2 Medicine (miscellaneous) 2023 Q2 Biochemistry 2023 Q3 4.9 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Gonçalves, Inês G Universidad de Zaragoza Borau, Carlos Universidad de Zaragoza (orcid)0000-0002-3784-1140 García-Aznar, Jose Manuel Universidad de Zaragoza (orcid)0000-0002-9864-7683 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 14, 8-12 (2023), 212-227 Integr. biol. (Camb.) Integrative biology (Cambridge) 1757-9694 1804613 http://zaguan.unizar.es/record/135351/files/texto_completo.pdf Versión publicada 2903592 http://zaguan.unizar.es/record/135351/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:135351 articulos driver 2025-11-21-14:41:11 ARTICLE