Linking discrete and continuous models of cell birth and migration

TL;DR

This paper develops a method to connect discrete individual-based models with continuous population models in biological systems, especially for cell migration and proliferation, improving interpretability and applicability to real data.

Contribution

It introduces a quantitative approach to link discrete and continuous models by fitting scaling parameters, accounting for low cell numbers and local interactions.

Findings

Continuous models accurately match ensemble averages for single processes.

Parameters differ when migration and proliferation occur simultaneously.

The approach enhances biological interpretability of mathematical models.

Abstract

Self-organisation of individuals within large collectives occurs throughout biology. Mathematical models can help elucidate the individual-level mechanisms behind these dynamics, but analytical tractability often comes at the cost of biological intuition. Discrete models provide straightforward interpretations by tracking each individual yet can be computationally expensive. Alternatively, continuous models supply a large-scale perspective by representing the "effective" dynamics of infinite agents, but their results are often difficult to translate into experimentally relevant insights. We address this challenge by quantitatively linking spatio-temporal dynamics of continuous models and individual-based data in settings with biologically realistic, time-varying cell numbers. Specifically, we introduce and fit scaling parameters in continuous models to account for discrepancies that can arise from low cell numbers and localised interactions. We illustrate our approach on an example motivated by zebrafish-skin pattern formation, in which we create a continuous framework describing the movement and proliferation of a single cell population by upscaling rules from a discrete model. Our resulting continuous models accurately depict ensemble average agent-based solutions when migration or proliferation act alone. Interestingly, the same parameters are not optimal when both processes act simultaneously, highlighting a rich difference in how combining migration and proliferation affects discrete and continuous dynamics.