TY - JOUR
T1 - Tumour growth
T2 - An approach to calibrate parameters of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment
AU - Hervas-Raluy, Silvia
AU - Wirthl, Barbara
AU - Guerrero, Pedro E.
AU - Robalo Rei, Gil
AU - Nitzler, Jonas
AU - Coronado, Esther
AU - Font de Mora Sainz, Jaime
AU - Schrefler, Bernhard A.
AU - Gomez-Benito, Maria Jose
AU - Garcia-Aznar, Jose Manuel
AU - Wall, Wolfgang A.
N1 - Funding Information:
SHR, PG, JFMS, MJGB and JMGA were supported by PRIMAGE (PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, empowered by imaging biomarkers) , a Horizon 2020—RIA project (Topic SC1-DTH-07-2018) , grant agreement No. 826494 . SHR gratefully acknowledges the support of the Government of Aragon (Grant no 2019–23 ), the Deutscher Akademischer Austauschdienst ( DAAD-91819992 ), the Fundación Ibercaja-Cai (No IT 5/21, IT 1/22 ) and the “Iberus+” project, co-funded by the European Union’s Erasmus+ programme and managed by Campus Iberus. MJGB was supported by Grant PID2021-124271OB-I00 founded by MCIN/AEI /10.13039/501100011033 and ERDF A way of making Europe . Authors would like to acknowledge the use of Servicio General de Apoyo a la Investigación-SAI (Universidad de Zaragoza) and the use of Servicios Científico Técnicos del CIBA (IACS-Universidad de Zaragoza). JMGA was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ICoMICS grant agreement No 101018587 ) and by the Spanish National Projects RTI2018-094494-B-C21 /MCI/AEI/FEDER, UE and PID2021-122409OB-C217 / AEI/10.13039/501100011033/ FEDER, UE. BAS was supported by the Visiting Fellowship for Alumni Fellows of the Institute for Advanced Study (IAS) . GRR was supported by the German Federal Ministry of Education and Research (project FestBatt 2, 03XP0435B ). JN and WAW wish to acknowledge funding of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) via project WA 1521/23 . WAW was supported by BREATHE, a Horizon 2020—ERC–2020–ADG project , grant agreement No. 101021526 -BREATHE.
Funding Information:
SHR, PG, JFMS, MJGB and JMGA were supported by PRIMAGE (PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, empowered by imaging biomarkers), a Horizon 2020—RIA project (Topic SC1-DTH-07-2018), grant agreement No. 826494. SHR gratefully acknowledges the support of the Government of Aragon (Grant no 2019–23), the Deutscher Akademischer Austauschdienst (DAAD-91819992), the Fundación Ibercaja-Cai (No IT 5/21, IT 1/22) and the “Iberus+” project, co-funded by the European Union's Erasmus+ programme and managed by Campus Iberus. MJGB was supported by Grant PID2021-124271OB-I00 founded by MCIN/AEI/10.13039/501100011033 and ERDF A way of making Europe. Authors would like to acknowledge the use of Servicio General de Apoyo a la Investigación-SAI (Universidad de Zaragoza) and the use of Servicios Científico Técnicos del CIBA (IACS-Universidad de Zaragoza). JMGA was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ICoMICS grant agreement No 101018587) and by the Spanish National Projects RTI2018-094494-B-C21/MCI/AEI/FEDER, UE and PID2021-122409OB-C217/ AEI/10.13039/501100011033/ FEDER, UE. BAS was supported by the Visiting Fellowship for Alumni Fellows of the Institute for Advanced Study (IAS). GRR was supported by the German Federal Ministry of Education and Research (project FestBatt 2, 03XP0435B). JN and WAW wish to acknowledge funding of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) via project WA 1521/23. WAW was supported by BREATHE, a Horizon 2020—ERC–2020–ADG project, grant agreement No. 101021526-BREATHE.
Publisher Copyright:
© 2023 The Authors
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - Bayesian calibration
KW - Gaussian processes
KW - Global sensitivity analysis
KW - Multiphase model
KW - Neuroblastoma spheroids
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U2 - 10.1016/j.compbiomed.2023.106895
DO - 10.1016/j.compbiomed.2023.106895
M3 - Article
AN - SCOPUS:85152236602
VL - 159
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
SN - 0010-4825
M1 - 106895
ER -