Matthew J Simpson, Keeley M Murphy, Scott W McCue, Pascal R Buenzli
{"title":"Discrete and continuous mathematical models of sharp-fronted collective cell migration and invasion","authors":"Matthew J Simpson, Keeley M Murphy, Scott W McCue, Pascal R Buenzli","doi":"arxiv-2310.07938","DOIUrl":null,"url":null,"abstract":"Mathematical models describing the spatial spreading and invasion of\npopulations of biological cells are often developed in a continuum modelling\nframework using reaction-diffusion equations. While continuum models based on\nlinear diffusion are routinely employed and known to capture key experimental\nobservations, linear diffusion fails to predict well-defined sharp fronts that\nare often observed experimentally. This observation has motivated the use of\nnonlinear degenerate diffusion, however these nonlinear models and the\nassociated parameters lack a clear biological motivation and interpretation.\nHere we take a different approach by developing a stochastic discrete\nlattice-based model incorporating biologically-inspired mechanisms and then\nderiving the reaction-diffusion continuum limit. Inspired by experimental\nobservations, agents in the simulation deposit extracellular material, that we\ncall a substrate, locally onto the lattice, and the motility of agents is taken\nto be proportional to the substrate density. Discrete simulations that mimic a\ntwo--dimensional circular barrier assay illustrate how the discrete model\nsupports both smooth and sharp-fronted density profiles depending on the rate\nof substrate deposition. Coarse-graining the discrete model leads to a novel\npartial differential equation (PDE) model whose solution accurately\napproximates averaged data from the discrete model. The new discrete model and\nPDE approximation provides a simple, biologically motivated framework for\nmodelling the spreading, growth and invasion of cell populations with\nwell-defined sharp fronts","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"10 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Cell Behavior","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2310.07938","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Mathematical models describing the spatial spreading and invasion of
populations of biological cells are often developed in a continuum modelling
framework using reaction-diffusion equations. While continuum models based on
linear diffusion are routinely employed and known to capture key experimental
observations, linear diffusion fails to predict well-defined sharp fronts that
are often observed experimentally. This observation has motivated the use of
nonlinear degenerate diffusion, however these nonlinear models and the
associated parameters lack a clear biological motivation and interpretation.
Here we take a different approach by developing a stochastic discrete
lattice-based model incorporating biologically-inspired mechanisms and then
deriving the reaction-diffusion continuum limit. Inspired by experimental
observations, agents in the simulation deposit extracellular material, that we
call a substrate, locally onto the lattice, and the motility of agents is taken
to be proportional to the substrate density. Discrete simulations that mimic a
two--dimensional circular barrier assay illustrate how the discrete model
supports both smooth and sharp-fronted density profiles depending on the rate
of substrate deposition. Coarse-graining the discrete model leads to a novel
partial differential equation (PDE) model whose solution accurately
approximates averaged data from the discrete model. The new discrete model and
PDE approximation provides a simple, biologically motivated framework for
modelling the spreading, growth and invasion of cell populations with
well-defined sharp fronts