A phylogeny of biological patterns formed by nonlocal advection

TL;DR

This paper provides a comprehensive numerical analysis of two-dimensional patterns formed by nonlocal advection PDEs, revealing diverse natural-like structures and linking them to biological self-organization mechanisms.

Contribution

It offers the first detailed 2D numerical classification of patterns from nonlocal advection PDEs, connecting them to biological phenomena.

Findings

Diverse patterns like clusters, stripes, and mosaics emerge in 2D models.

Pattern types are systematically linked to underlying movement mechanisms.

The framework helps interpret biological self-organization in ecology, biology, and cancer.

Abstract

From tumour invasion to cell sorting and animal territoriality, many biological systems rely on nonlocal interactions that drive complex spatial organisation. Partial differential equations (PDEs) with nonlocal advection are increasingly recognised as powerful tools for capturing such phenomena. However, most research has focused on one-dimensional domains, leaving their two-dimensional behaviour largely unexplored. Here, we present a detailed numerical study of the patterns formed by these systems on 2D domains. Depending on the underlying mechanisms, a wide variety of spatial patterns can emerge - including segregated clusters, stripes, volcanos, and polygonal mosaics - many of which have been observed in natural systems. By systematically varying model parameters, we classify the links between emergent patterns and their underlying movement mechanisms. In comparing these patterns with empirical observations, we show how this modelling framework can help reveal possible mechanisms of self-organisation in various situations within the life sciences, from ecology and developmental biology to cancer research.