ArticlePLoS computational biology2021
Large-scale simulations of biological cell sorting driven by differential adhesion follow diffusion-limited domain coalescence regime.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of aptamers in regenerative medicine.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Cell-Level Modelling of Homeostasis in Confined Epithelial Monolayers.Journal Of Elasticity · 2025Article
- Biophysical models of early mammalian embryogenesis.Stem cell reports · 2023Review
- Is cell segregation like oil and water: Asymptotic versus transitory regime.PLoS computational biology · 2022Article
- Role of senescent cells in the motile behavior of active, non-senescent cells in confluent populations.Scientific reports · 2022Article
- Cluster size distribution of cells disseminating from a primary tumor.PLoS computational biology · 2021Article
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Abstract
Cell sorting, whereby a heterogeneous cell mixture segregates and forms distinct homogeneous tissues, is one of the main collective cell behaviors at work during development. Although differences in interfacial energies are recognized to be a possible driving source for cell sorting, no clear consensus has emerged on the kinetic law of cell sorting driven by differential adhesion. Using a modified Cellular Potts Model algorithm that allows for efficient simulations while preserving the connectivity of cells, we numerically explore cell-sorting dynamics over very large scales in space and time. For a binary mixture of cells surrounded by a medium, increase of domain size follows a power-law with exponent n = 1/4 independently of the mixture ratio, revealing that the kinetics is dominated by the diffusion and coalescence of rounded domains. We compare these results with recent numerical studies on cell sorting, and discuss the importance of algorithmic differences as well as boundary conditions on the observed scaling.
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