000170922 001__ 170922
000170922 005__ 20260430151735.0
000170922 0247_ $$2doi$$a10.1016/j.finel.2026.104551
000170922 0248_ $$2sideral$$a148987
000170922 037__ $$aART-2026-148987
000170922 041__ $$aeng
000170922 100__ $$aSala-Lardies, E.
000170922 245__ $$a3D numerical modeling of glioblastoma cells progression in microfluidic devices
000170922 260__ $$c2026
000170922 5060_ $$aAccess copy available to the general public$$fUnrestricted
000170922 5203_ $$aMathematical and computational models provide a powerful framework for understanding complex biological processes such as cancer cell progression. Here, we present a numerical formulation for the simulation of the evolution of glioblastoma (GBM) cancer cells in microfluidic devices which are commonly used to replicate the dynamic changes of the tumor cells in a biomimetic microenvironment. We model this physicochemical and biological complexity
with a coupled nonlinear system of transient partial differential equations involving different chemical species and cell phenotypes. In particular, we consider oxygen as the main chemical driver and the concentration of two cell phenotypes: living and dead cells. The system is solved combining a high-order continuous Galerkin finite element formulation in space with a high-order diagonally implicit Runge–Kutta (DIRK) scheme in time. This leads to a coupled nonlinear system for oxygen and living cells at each stage of the DIRK scheme that we solve using the Newton method. The same integration method is used to solve for dead cells at each mesh node. Finally, we present several examples to assess and illustrate the capabilities of the proposed formulation. The results demonstrate that this model is a valuable tool for advancing our understanding of cancer cell progression and for supporting the industrial cesign of novel devices aimed at testing new hypotheses and guiding experimental research.
000170922 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2021-126051OB-C41$$9info:eu-repo/grantAgreement/ES/AEI/PID2021-126051OB-C42$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-129512B-I00
000170922 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000170922 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000170922 700__ $$aSarrate, J.
000170922 700__ $$0(orcid)0000-0002-7909-4446$$aPérez-Aliacar, M.
000170922 700__ $$0(orcid)0000-0003-2564-6038$$aAyensa-Jiménez, J.
000170922 700__ $$0(orcid)0000-0001-8741-6452$$aDoblaré, M.$$uUniversidad de Zaragoza
000170922 700__ $$aParés, N.
000170922 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000170922 773__ $$g258 (2026), 104551 [27 pp.]$$pFinite elem. anal. des.$$tFINITE ELEMENTS IN ANALYSIS AND DESIGN$$x0168-874X
000170922 8564_ $$s6747782$$uhttps://zaguan.unizar.es/record/170922/files/texto_completo.pdf$$yVersión publicada
000170922 8564_ $$s2001115$$uhttps://zaguan.unizar.es/record/170922/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000170922 909CO $$ooai:zaguan.unizar.es:170922$$particulos$$pdriver
000170922 951__ $$a2026-04-30-13:57:08
000170922 980__ $$aARTICLE