Affordable Access

Collective Cell Behaviour with Neighbour-Dependent Proliferation, Death and Directional Bias.

Authors
  • Binny, Rachelle N1, 2, 3
  • James, Alex1, 2
  • Plank, Michael J4, 5
  • 1 School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand. , (New Zealand)
  • 2 Te Pūnaha Matatini, A New Zealand Centre of Research Excellence, Auckland, New Zealand. , (New Zealand)
  • 3 Landcare Research, Lincoln, New Zealand. , (New Zealand)
  • 4 School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand. [email protected] , (New Zealand)
  • 5 Te Pūnaha Matatini, A New Zealand Centre of Research Excellence, Auckland, New Zealand. [email protected] , (New Zealand)
Type
Published Article
Journal
Bulletin of mathematical biology
Publication Date
Nov 01, 2016
Volume
78
Issue
11
Pages
2277–2301
Identifiers
PMID: 27761698
Source
Medline
Keywords
License
Unknown

Abstract

Collective cell migration and proliferation are integral to tissue repair, embryonic development, the immune response and cancer. Central to collective cell migration and proliferation are interactions among neighbouring cells, such as volume exclusion, contact inhibition and adhesion. These individual-level processes can have important effects on population-level outcomes, such as growth rate and equilibrium density. We develop an individual-based model of cell migration and proliferation that includes these interactions. This is an extension of a previous model with neighbour-dependent directional bias to incorporate neighbour-dependent proliferation and death. A deterministic approximation to this individual-based model is derived using a spatial moment dynamics approach, which retains information about the spatial structure of the cell population. We show that the individual-based model and spatial moment model match well across a range of parameter values. The spatial moment model allows insight into the two-way interaction between spatial structure and population dynamics that cannot be captured by traditional mean-field models.

Report this publication

Statistics

Seen <100 times