Tumour 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

Hervas-Raluy, Silvia (Universidad de Zaragoza) ; Wirthl, Barbara ; Guerrero, Pedro E. (Universidad de Zaragoza) ; Robalo Rei, Gil ; Nitzler, Jonas ; Coronado, Esther ; Font de Mora Sainz, Jaime ; Schrefler, Bernhard A. ; Gomez-Benito, Maria Jose (Universidad de Zaragoza) ; Garcia-Aznar, Jose Manuel (Universidad de Zaragoza) ; Wall, Wolfgang A.

Resumen: To 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.

Idioma: Inglés

DOI: 10.1016/j.compbiomed.2023.106895

Año: 2023

Publicado en: Computers in biology and medicine 159 (2023), 106895 [18 pp.]
ISSN: 0010-4825

Factor impacto JCR: 7.0 (2023)

Categ. JCR: BIOLOGY rank: 7 / 109 = 0.064 (2023) - Q1 - T1
Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 2 / 66 = 0.03 (2023) - Q1 - T1
Categ. JCR: ENGINEERING, BIOMEDICAL rank: 16 / 123 = 0.13 (2023) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 19 / 170 = 0.112 (2023) - Q1 - T1

Factor impacto CITESCORE: 11.7 - Health Informatics (Q1) - Computer Science Applications (Q1)

Factor impacto SCIMAGO: 1.481 - Health Informatics (Q1) - Computer Science Applications (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/2019-23
Financiación: info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS
Financiación: info:eu-repo/grantAgreement/EC/H2020/101021526/EU/Unlocking vital mysteries in respiratory biomechanics/BREATHE
Financiación: 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
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2021-124271OB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/RTI2018-094494-B-C21
Financiación: info:eu-repo/grantAgreement/ES/UZ-IBERCAJA-CAI/IT1-22
Financiación: info:eu-repo/grantAgreement/ES/UZ-IBERCAJA-CAI/IT5-21

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Bioquímica y Biolog.Mole. (Dpto. Bioq.Biolog.Mol. Celular)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)