Modelling cell adaptation using internal variables: Accounting for cell plasticity in continuum mathematical biology

Pérez-Aliacar, Marina (Universidad de Zaragoza) ; Ayensa-Jiménez, Jacobo ; Doblaré, Manuel (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1016/j.compbiomed.2023.107291

Año: 2023

Publicado en: Computers in biology and medicine 164 (2023), 107291 [20 pp.]
ISSN: 0010-4825

Factor impacto JCR: 7.0 (2023)

Categ. JCR: BIOLOGY rank: 7 / 109 = 0.064 (2023) - Q1 - T1
Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 2 / 66 = 0.03 (2023) - Q1 - T1
Categ. JCR: ENGINEERING, BIOMEDICAL rank: 16 / 123 = 0.13 (2023) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 19 / 170 = 0.112 (2023) - Q1 - T1

Factor impacto CITESCORE: 11.7 - Health Informatics (Q1) - Computer Science Applications (Q1)

Factor impacto SCIMAGO: 1.481 - Health Informatics (Q1) - Computer Science Applications (Q1)

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2021-126051OB-C41

Tipo y forma: Article (Published version)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)