English

Parameter spaces for cross-diffusive-driven instability in a reaction-diffusion system on an annular domain

Dynamical Systems 2024-12-31 v1

Abstract

In this work, the influence of geometry and domain size on spatiotemporal pattern formation is investigated to establish parameter spaces for a cross-diffusive reaction-diffusion model on an annulus. By applying linear stability theory, we derive conditions which can give rise to Turing, Hopf and transcritical types of diffusion-driven instabilities. We explore whether selection of a sufficiently large domain size, together with the appropriate selection of parameters, can give rise to the development of patterns on non-convex geometries e.g. annulus. Hence, the key research methodology and outcomes of our studies include: a complete analytical exploration of the spatiotemporal dynamics in an activator-depleted reaction-diffusion system; a linear stability analysis to characterise the dual roles of cross-diffusion and domain size of pattern formation on an annulus region; the derivation of the instability conditions through lower and upper bounds of the domain size; the full classification of the model parameters, and a demonstration of how cross-diffusion relaxes the general conditions for the reaction-diffusion system to exhibit pattern formation. To validate theoretical findings and predictions, we employ the finite element method to reveal spatial and spatiotemporal patterns in the dynamics of the cross-diffusive reaction-diffusion system within a two-dimensional annular domain. These observed patterns resemble those found in ring-shaped cross-sectional scans of hypoxic tumours. Specifically, the cross-section of an actively invasive region in a hypoxic tumour can be effectively approximated by an annulus.

Keywords

Cite

@article{arxiv.2412.20097,
  title  = {Parameter spaces for cross-diffusive-driven instability in a reaction-diffusion system on an annular domain},
  author = {Gulsemay Yigit and Wakil Sarfaraz and Raquel Barreira and Anotida Madzvamuse},
  journal= {arXiv preprint arXiv:2412.20097},
  year   = {2024}
}
R2 v1 2026-06-28T20:50:34.296Z