128073 20241125101145.0 doi 10.1016/j.compbiomed.2023.107291 sideral 135244 ART-2023-135244 eng Pérez-Aliacar, Marina Universidad de Zaragoza (orcid)0000-0002-7909-4446 Modelling cell adaptation using internal variables: Accounting for cell plasticity in continuum mathematical biology 2023 Access copy available to the general public Unrestricted Cellular adaptation is the ability of cells to change in response to different stimuli and environmental conditions. It occurs via phenotypic plasticity, that is, changes in gene expression derived from changes in the physiological environment. This phenomenon is important in many biological processes, in particular in cancer evolution and its treatment. Therefore, it is crucial to understand the mechanisms behind it. Specifically, the emergence of the cancer stem cell phenotype, showing enhanced proliferation and invasion rates, is an essential process in tumour progression. We present a mathematical framework to simulate phenotypic heterogeneity in different cell populations as a result of their interaction with chemical species in their microenvironment, through a continuum model using the well-known concept of internal variables to model cell phenotype. The resulting model, derived from conservation laws, incorporates the relationship between the phenotype and the history of the stimuli to which cells have been subjected, together with the inheritance of that phenotype. To illustrate the model capabilities, it is particularised for glioblastoma adaptation to hypoxia. A parametric analysis is carried out to investigate the impact of each model parameter regulating cellular adaptation, showing that it permits reproducing different trends reported in the scientific literature. The framework can be easily adapted to any particular problem of cell plasticity, with the main limitation of having enough cells to allow working with continuum variables. With appropriate calibration and validation, it could be useful for exploring the underlying processes of cellular adaptation, as well as for proposing favourable/unfavourable conditions or treatments. info:eu-repo/grantAgreement/ES/AEI/PID2021-126051OB-C41 info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 7.0 2023 BIOLOGY 7 / 109 = 0.064 2023 Q1 T1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 2 / 66 = 0.03 2023 Q1 T1 ENGINEERING, BIOMEDICAL 16 / 123 = 0.13 2023 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 19 / 170 = 0.112 2023 Q1 T1 1.481 2023 Health Informatics 2023 Q1 Computer Science Applications 2023 Q1 11.7 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Ayensa-Jiménez, Jacobo (orcid)0000-0003-2564-6038 Doblaré, Manuel Universidad de Zaragoza (orcid)0000-0001-8741-6452 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 164 (2023), 107291 [20 pp.] Comput. biol. med. Computers in biology and medicine 0010-4825 6173520 http://zaguan.unizar.es/record/128073/files/texto_completo.pdf Versión publicada 2806173 http://zaguan.unizar.es/record/128073/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:128073 articulos driver 2024-11-22-12:04:12 ARTICLE