doi:10.1007/s11831-025-10291-yengAyensa-Jiménez, JacoboPérez-Aliacar, MarinaDoweidar, Mohamed H.Gaffney, Eamonn A.Doblaré, ManuelAn Overview from Physically-Based to Data-Driven Approaches of the Modelling and Simulation of Glioblastoma Progression in Microfluidic DevicesART-2025-145032In silico models and computational tools are invaluable instruments that complement experiments to improve our understanding of complex phenomena such as cancer evolution. This work offers a perspective on different approaches that can be used for mathematical modeling of glioblastoma, the most common and lethal brain cancer, in microfluidic devices, the most biomimetic in vitro cell culture technique nowadays. These approaches range from purely knowledge-based solutions to data-driven, and hence completely model-free, algorithms. In particular, we focus on hybrid approaches, which combine physically-based and data-driven strategies, demonstrating how this integration can enhance the understanding we get from simulation by revealing the underlying model structure and thus, in turn, the prospective biological mechanism.2025http://zaguan.unizar.es/record/16245710.1007/s11831-025-10291-yhttp://zaguan.unizar.es/record/162457oai:zaguan.unizar.es:162457info:eu-repo/grantAgreement/ES/AEI/PID2021-126051OB-C41info:eu-repo/grantAgreement/ES/DGA/T62-230Rinfo:eu-repo/grantAgreement/ES/MICINN/PID2022-138572OB-C44ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 32 (2025), 5037-5073byhttps://creativecommons.org/licenses/by/4.0/deed.esinfo:eu-repo/semantics/openAccess