3D numerical modeling of glioblastoma cells progression in microfluidic devices
Mathematical 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 commonl...
| Autores: | , , , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2026 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:dnet:upcommonspor::0be86ec787bab7d7fdf748150302aa46 |
| Acesso em linha: | https://hdl.handle.net/2117/460938 https://dx.doi.org/10.1016/j.finel.2026.104551 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Glioblastoma (GBM) High-order continuous Galerkin High-order diagonally implicit Runge–Kutta Newton method Àrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica::Mètodes numèrics |
| Resumo: | Mathematical 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 design of novel devices aimed at testing new hypotheses and guiding experimental research. |
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