# Reactions, Diffusion and Volume Exclusion in a Heterogeneous System of Interacting Particles

## TL;DR

This paper introduces a new reaction-diffusion modeling framework that incorporates volume exclusion effects for heterogeneous particles, bridging microscopic interactions and macroscopic descriptions, with applications to chemotaxis.

## Contribution

The authors develop a novel macroscopic PDE system derived from microscopic models that explicitly accounts for volume exclusion and reactions among heterogeneous particles.

## Key findings

- Reaction terms are of lower order than nonlinear diffusion in the asymptotic expansion.
- Including the next reaction term improves agreement with microscopic simulations.
- The macroscopic model facilitates direct parameterization from experimental data.

## Abstract

Complex biological and physical transport processes are often described through systems of interacting particles. Excluded-volume effects on these transport processes are well studied, however the interplay between volume exclusion and reactions between heterogenous particles is less well known. In this paper we develop a novel framework for modeling reaction-diffusion processes which directly incorporates volume exclusion. From an off-lattice microscopic individual based model we use the Fokker--Planck equation and the method of matched asymptotic expansions to derive a low-dimensional macroscopic system of nonlinear partial differential equations describing the evolution of the particles. A biologically motivated, hybrid model of chemotaxis with volume exclusion is explored, where reactions occur at rates dependent upon the chemotactic environment. Further, we show that for reactions due to contact interactions the appropriate reaction term in the macroscopic model is of lower order in the asymptotic expansion than the nonlinear diffusion term. However, we find that the next reaction term in the expansion is needed to ensure good agreement with simulations of the microscopic model. Our macroscopic model allows for more direct parameterization to experimental data than the models available to date.