000126467 001__ 126467 000126467 005__ 20240731103404.0 000126467 0247_ $$2doi$$a10.1016/j.compbiomed.2023.106895 000126467 0248_ $$2sideral$$a133842 000126467 037__ $$aART-2023-133842 000126467 041__ $$aeng 000126467 100__ $$0(orcid)0000-0001-8324-5596$$aHervas-Raluy, Silvia$$uUniversidad de Zaragoza 000126467 245__ $$aTumour growth: An approach to calibrate parameters of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment 000126467 260__ $$c2023 000126467 5060_ $$aAccess copy available to the general public$$fUnrestricted 000126467 5203_ $$aTo unravel processes that lead to the growth of solid tumours, it is necessary to link knowledge of cancer biology with the physical properties of the tumour and its interaction with the surrounding microenvironment. Our understanding of the underlying mechanisms is however still imprecise. We therefore developed computational physics-based models, which incorporate the interaction of the tumour with its surroundings based on the theory of porous media. However, the experimental validation of such models represents a challenge to its clinical use as a prognostic tool. This study combines a physics-based model with in vitro experiments based on microfluidic devices used to mimic a three-dimensional tumour microenvironment. By conducting a global sensitivity analysis, we identify the most influential input parameters and infer their posterior distribution based on Bayesian calibration. The resulting probability density is in agreement with the scattering of the experimental data and thus validates the proposed workflow. This study demonstrates the huge challenges associated with determining precise parameters with usually only limited data for such complex processes and models, but also demonstrates in general how to indirectly characterise the mechanical properties of neuroblastoma spheroids that cannot feasibly be measured experimentally. 000126467 536__ $$9info:eu-repo/grantAgreement/ES/DGA/2019-23$$9info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101018587-ICoMICS$$9info:eu-repo/grantAgreement/EC/H2020/101021526/EU/Unlocking vital mysteries in respiratory biomechanics/BREATHE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101021526-BREATHE$$9info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21$$9info:eu-repo/grantAgreement/ES/MICINN/PID2021-124271OB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/RTI2018-094494-B-C21$$9info:eu-repo/grantAgreement/ES/UZ-IBERCAJA-CAI/IT1-22$$9info:eu-repo/grantAgreement/ES/UZ-IBERCAJA-CAI/IT5-21 000126467 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000126467 590__ $$a7.0$$b2023 000126467 592__ $$a1.481$$b2023 000126467 591__ $$aBIOLOGY$$b7 / 109 = 0.064$$c2023$$dQ1$$eT1 000126467 593__ $$aHealth Informatics$$c2023$$dQ1 000126467 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b2 / 65 = 0.031$$c2023$$dQ1$$eT1 000126467 593__ $$aComputer Science Applications$$c2023$$dQ1 000126467 591__ $$aENGINEERING, BIOMEDICAL$$b16 / 122 = 0.131$$c2023$$dQ1$$eT1 000126467 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b18 / 169 = 0.107$$c2023$$dQ1$$eT1 000126467 594__ $$a11.7$$b2023 000126467 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000126467 700__ $$aWirthl, Barbara 000126467 700__ $$0(orcid)0000-0003-2612-9235$$aGuerrero, Pedro E.$$uUniversidad de Zaragoza 000126467 700__ $$aRobalo Rei, Gil 000126467 700__ $$aNitzler, Jonas 000126467 700__ $$aCoronado, Esther 000126467 700__ $$aFont de Mora Sainz, Jaime 000126467 700__ $$aSchrefler, Bernhard A. 000126467 700__ $$0(orcid)0000-0002-1878-8997$$aGomez-Benito, Maria Jose$$uUniversidad de Zaragoza 000126467 700__ $$0(orcid)0000-0002-9864-7683$$aGarcia-Aznar, Jose Manuel$$uUniversidad de Zaragoza 000126467 700__ $$aWall, Wolfgang A. 000126467 7102_ $$11002$$2060$$aUniversidad de Zaragoza$$bDpto. Bioq.Biolog.Mol. Celular$$cÁrea Bioquímica y Biolog.Mole. 000126467 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est. 000126467 773__ $$g159 (2023), 106895 [18 pp.]$$pComput. biol. med.$$tComputers in biology and medicine$$x0010-4825 000126467 8564_ $$s3084136$$uhttps://zaguan.unizar.es/record/126467/files/texto_completo.pdf$$yVersión publicada 000126467 8564_ $$s2473658$$uhttps://zaguan.unizar.es/record/126467/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000126467 909CO $$ooai:zaguan.unizar.es:126467$$particulos$$pdriver 000126467 951__ $$a2024-07-31-10:00:28 000126467 980__ $$aARTICLE