Patterning of nonlocal transport models in biology: The impact of spatial dimension

Jewell, Thomas Jun; Krause, Andrew L.; Maini, Philip K.; Gaffney, Eamonn A.

Abstract

Throughout developmental biology and ecology, transport can be driven by nonlocal interactions. Examples include cells that migrate based on contact with pseudopodia extended from other cells, and animals that move based on their awareness of other animals. Nonlocal integro-PDE models have been used to investigate contact attraction and repulsion in cell populations in 1D. In this paper, we generalise the analysis of pattern formation in such a model from 1D to higher spatial dimensions. Numerical simulations in 2D demonstrate complex behaviour in the model, including spatio-temporal patterns, multi-stability, and patterns with wavelength and shape that differ significantly depending on whether interactions are attractive or repulsive. Through linear stability analysis in N dimensions, we demonstrate how, unlike in local Turing reaction-diffusion models, the capacity for pattern formation fundamentally changes with dimensionality for this nonlocal model. Most notably, pattern formation is possible only in higher than one spatial dimension for both the single species system with repulsive interactions, and the two species system with 'run-and-chase' interactions. The latter case may be relevant to zebrafish stripe formation, which has been shown to be driven by run-and-chase dynamics between melanophore and xanthophore pigment cells.

Citation

Jewell, T. J., Krause, A. L., Maini, P. K., & Gaffney, E. A. (2023). Patterning of nonlocal transport models in biology: The impact of spatial dimension. Mathematical Biosciences, 366, Article 109093. https://doi.org/10.1016/j.mbs.2023.109093

• Journal Article Type: Article
• Acceptance Date: Oct 23, 2023
• Online Publication Date: Oct 29, 2023
• Publication Date: 2023-12
• Deposit Date: Oct 27, 2023
• Publicly Available Date: Dec 4, 2023
• Journal: Mathematical Biosciences
• Print ISSN: 0025-5564
• Electronic ISSN: 1879-3134
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 366
• Article Number: 109093
• DOI: https://doi.org/10.1016/j.mbs.2023.109093
• Public URL: https://durham-repository.worktribe.com/output/1819177