Modern Perspectives on Near-Equilibrium Analysis of Turing Systems

TL;DR

This paper reviews recent advances in the mathematical analysis of Turing systems, extending classical reaction-diffusion models to complex settings, highlighting progress, challenges, and open questions in understanding biological pattern formation.

Contribution

It generalizes Turing's reaction-diffusion theory to complex geometries and heterogeneous systems, and discusses open problems in stability analysis and biological applications.

Findings

Progress in mathematical modeling of Turing patterns

Extension to complex manifolds and heterogeneous systems

Identification of open challenges in stability analysis

Abstract

In the nearly seven decades since the publication of Alan Turing's work on morphogenesis, enormous progress has been made in understanding both the mathematical and biological aspects of his proposed reaction-diffusion theory. Some of these developments were nascent in Turing's paper, and others have been due to new insights from modern mathematical techniques, advances in numerical simulations, and extensive biological experiments. Despite such progress, there are still important gaps between theory and experiment, with many examples of biological patterning where the underlying mechanisms are still unclear. Here we review modern developments in the mathematical theory pioneered by Turing, showing how his approach has been generalized to a range of settings beyond the classical two-species reaction-diffusion framework, including evolving and complex manifolds, systems heterogeneous in space and time, and more general reaction-transport equations. While substantial progress has been made in understanding these more complicated models, there are many remaining challenges that we highlight throughout. We focus on the mathematical theory, and in particular linear stability analysis of `trivial' base states. We emphasise important open questions in developing this theory further, and discuss obstacles in using these techniques to understand biological reality.