Linking continuous and discrete models of cell birth and migration

Self-organization of individuals within large collectives occurs throughout biology, with examples including locust swarming and cell formation of embryonic tissues. 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 discrete and continuous models in settings with biologically realistic, time-varying cell numbers. Motivated by zebrafish-skin pattern formation, we create a continuous framework describing the movement and proliferation of a single cell population by upscaling rules from a discrete model. We introduce and fit scaling parameters to account for discrepancies between these two frameworks in terms of cell numbers, considering movement and birth separately. 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.

Methods
Individual-based data were produced by simulating the ABM listed in the paper, using Matlab (version 2022a). The PDEs were simulated using either Matlab programmes (for the cell birth-only model) or using custom codes in Julia (version 1.7.3). Codes used to simulate the models may be found at the following Github repository: https://github.com/wdmartinson/Self-Organization-One-Species .