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Stem Cell Populations as Self-Renewing Many-Particle Systems
- David J. Jörg1,2, Yu Kitadate3,4, Shosei Yoshida3,4, and Benjamin D. Simons2,5,6
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View Affiliations Hide AffiliationsAffiliations: 1Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge CB3 0HE, United Kingdom 2The Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge CB2 1QN, United Kingdom; email: [email protected] 3Division of Germ Cell Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, Myodaiji, Okazaki 444-8787, Japan 4Department of Basic Biology, School of Life Science, Graduate University for Advanced Studies (Sokendai), Myodaiji, Okazaki 444-8787, Japan 5Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge CB3 0WA, United Kingdom 6The Wellcome Trust/Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge CB2 1QR, United Kingdom
- Vol. 12:135-153 (Volume publication date March 2021) https://doi.org/10.1146/annurev-conmatphys-041720-125707
- First published as a Review in Advance on November 23, 2020
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Copyright © 2021 by Annual Reviews. All rights reserved
Abstract
This article reviews the physical principles of stem cell populations as active many-particle systems that are able to self-renew, control their density, and recover from depletion. We illustrate the dynamical and statistical hallmarks of homeostatic mechanisms, from stem cell density fluctuations and transient large-scale oscillation dynamics during recovery to the scaling behavior of clonal dynamics and front-like boundary propagation during regeneration.
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Data & Media loading...
Supplemental Material
Supplementary Data
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Supplemental Video 1: Simulation of the individual cell-based model in the active phase with nonoscillatory recovery, showing equilibration toward a homeostatic steady state from a subhomeostatic initial state. Parameters and initial conditions are as in Figure 2e (subpanel iii).
Supplemental Video 2: Simulation of the individual cell-based model in the active phase with oscillatory recovery, showing equilibration toward a homeostatic steady state from a subhomeostatic initial state with large-amplitude density oscillations. Parameters and initial conditions are as in Figure 2e (subpanel ii).
Supplemental Video 3: Simulation of the individual cell-based model in the dead phase, showing equilibration toward the dead state from an initially populated state. Parameters and initial conditions are as in Figure 2e (subpanel i).
Supplemental Video 4: Simulation of the individual cell-based model showing neutral competition of single-cell derived clones when the system is in equilibrium. The initial population of cells is marked in different colors, which are inherited by their progeny. Parameters are as in Figure 2e (subpanel ii).
Supplemental Video 5: Simulation of the individual cell-based model in the nonoscillatory recovery regime, showing colonization of the system by an initially localized population of cells through a propagating density front. Parameters and initial conditions are as in Figure 5a.
Supplemental Video 6: Simulation of the individual cell-based model in the oscillatory recovery regime, showing colonization of the system by an initially localized population of cells through a propagating density front with elevated density in the front region. Parameters and initial conditions are as in Figure 5b.
Supplemental Video 7: Simulation of the individual cell-based model with a subpopulation of stem cells with a competitive advantage (black), showing the gradual takeover of the system and extinction of the "wildtype" population (white). Parameters and initial conditions are as in Figure 6.
Supplemental Video 8: Simulation of the individual cell-based model with localized sources of the fate determinant. Parameters and initial conditions are as in Figure 7.
- Article Type: Review Article
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