AI Article Synopsis

  • - The text discusses the limitations of traditional continuum models using linear diffusion in accurately predicting sharp fronts in biological cell population spreading, motivating a shift towards nonlinear models.
  • - A new approach is introduced that uses a stochastic discrete lattice-based model, where agents deposit extracellular material, influencing their mobility based on the density of this substrate.
  • - The study develops a new partial differential equation (PDE) model from the discrete model that effectively replicates the behavior of cell populations, providing a biologically grounded method for understanding their growth and invasion patterns.

Article 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, which we call a , 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 provide a simple, biologically motivated framework for modelling the spreading, growth and invasion of cell populations with well-defined sharp fronts. Open-source Julia code to replicate all results in this work is available on GitHub.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11286127PMC
http://dx.doi.org/10.1098/rsos.240126DOI Listing

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