Phenotype structuring in collective cell migration:a tutorial of mathematical models and methods

TL;DR

This paper reviews how classical PDE models for collective cell migration can be extended to include phenotypic diversity, resulting in complex non-local models called PS-PIDEs, with tutorials on derivation, analysis, and simulation.

Contribution

It introduces phenotype-structured PDE models for cell populations, extending classic models and providing methods for derivation, analysis, and numerical simulation.

Findings

Phenotype-structured models capture population heterogeneity.

Traveling wave analysis reveals phenotypic distribution dynamics.

Numerical schemes enable simulation of complex non-local models.

Abstract

Populations are heterogeneous, deviating in numerous ways. Phenotypic diversity refers to the range of traits or characteristics across a population, where for cells this could be the levels of signalling, movement and growth activity, etc. Clearly, the phenotypic distribution -- and how this changes over time and space -- could be a major determinant of population-level dynamics. For instance, across a cancerous population, variations in movement, growth, and ability to evade death may determine its growth trajectory and response to therapy. In this review, we discuss how classical partial differential equation (PDE) approaches for modelling cellular systems and collective cell migration can be extended to include phenotypic structuring. The resulting non-local models -- which we refer to as phenotype-structured partial integro-differential equations (PS-PIDEs) -- form a sophisticated class of models with rich dynamics. We set the scene through a brief history of structured population modelling, and then review the extension of several classic movement models -- including the Fisher-KPP and Keller-Segel equations -- into a PS-PIDE form. We proceed with a tutorial-style section on derivation, analysis, and simulation techniques. First, we show a method to formally derive these models from underlying agent-based models. Second, we recount travelling waves in PDE models of spatial spread dynamics and concentration phenomena in non-local PDE models of evolutionary dynamics, and combine the two to deduce phenotypic structuring across travelling waves in PS-PIDE models. Third, we discuss numerical methods to simulate PS-PIDEs, illustrating with a simple scheme based on the method of lines and noting the finer points of consideration. We conclude with a discussion of future modelling and mathematical challenges.