Mathematics of neural stem cells: Linking data and processes
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 q...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
June 2023
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| In: |
Cells & development
Year: 2023, Volume: 174, Pages: 1-17 |
| ISSN: | 2667-2901 |
| DOI: | 10.1016/j.cdev.2023.203849 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.cdev.2023.203849 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S2667290123000256 
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| Author Notes: | Diana-Patricia Danciu, Jooa Hooli, Ana Martin-Villalba, Anna Marciniak-Czochra |
| Summary: | 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. |
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| Item Description: | Online veröffentlicht am 11. Mai 2023 Gesehen am 07.12.2023 |
| Physical Description: | Online Resource |
| ISSN: | 2667-2901 |
| DOI: | 10.1016/j.cdev.2023.203849 |