A hybrid discrete-continuum approach to model Turing pattern formation

TL;DR

This paper introduces a hybrid discrete-continuum model combining stochastic cell behavior with reaction-diffusion systems to simulate Turing pattern formation, demonstrating strong agreement between models on static and growing domains.

Contribution

It develops a novel hybrid modeling framework that integrates individual cell dynamics with morphogen concentration patterns for Turing-based pattern formation.

Findings

Quantitative match between stochastic and continuum models.

Effective modeling on static and growing domains.

Proof of concept for future morphogenetic studies.

Abstract

Since its introduction in 1952, Turing's (pre-)pattern theory ("the chemical basis of morphogenesis") has been widely applied to a number of areas in developmental biology. The related pattern formation models normally comprise a system of reaction-diffusion equations for interacting chemical species ("morphogens"), whose heterogeneous distribution in some spatial domain acts as a template for cells to form some kind of pattern or structure through, for example, differentiation or proliferation induced by the chemical pre-pattern. Here we develop a hybrid discrete-continuum modelling framework for the formation of cellular patterns via the Turing mechanism. In this framework, a stochastic individual-based model of cell movement and proliferation is combined with a reaction-diffusion system for the concentrations of some morphogens. As an illustrative example, we focus on a model in which the dynamics of the morphogens are governed by an activator-inhibitor system that gives rise to Turing pre-patterns. The cells then interact with morphogens in their local area through either of two forms of chemically-dependent cell action: chemotaxis and chemically-controlled proliferation. We begin by considering such a hybrid model posed on static spatial domains, and then turn to the case of growing domains. In both cases, we formally derive the corresponding deterministic continuum limit and show that that there is an excellent quantitative match between the spatial patterns produced by the stochastic individual-based model and its deterministic continuum counterpart, when sufficiently large numbers of cells are considered. This paper is intended to present a proof of concept for the ideas underlying the modelling framework, with the aim to then apply the related methods to the study of specific patterning and morphogenetic processes in the future.