Mathematics of neural stem cells: Linking data and processes.
Mechanistic models
Neurogenesis
Population models
Single-cell data
Stem cells
Journal
Cells & development
ISSN: 2667-2901
Titre abrégé: Cells Dev
Pays: Netherlands
ID NLM: 101775611
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
received:
03
02
2023
revised:
29
04
2023
accepted:
05
05
2023
medline:
29
5
2023
pubmed:
14
5
2023
entrez:
13
5
2023
Statut:
ppublish
Résumé
Adult stem cells are described as a discrete population of cells that stand at the top of a hierarchy of progressively differentiating cells. Through their unique ability to self-renew and differentiate, they regulate the number of end-differentiated cells that contribute to tissue physiology. The question of how discrete, continuous, or reversible the transitions through these hierarchies are and the precise parameters that determine the ultimate performance of stem cells in adulthood are the subject of intense research. In this review, we explain how mathematical modelling has improved the mechanistic understanding of stem cell dynamics in the adult brain. We also discuss how single-cell sequencing has influenced the understanding of cell states or cell types. Finally, we discuss how the combination of single-cell sequencing technologies and mathematical modelling provides a unique opportunity to answer some burning questions in the field of stem cell biology.
Identifiants
pubmed: 37179018
pii: S2667-2901(23)00025-6
doi: 10.1016/j.cdev.2023.203849
pii:
doi:
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
203849Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare no competing interests.