Abstract

Coordination of large groups occurs throughout biology, with examples including zebrafish stripe patterning and cell formation of embryonic tissues. Mathematical models can help elucidate the individual-level mechanisms dominating these dynamics and thereby provide experimental insights. We demonstrate this by creating a model for chick cranial neural crest cell (NCC) migration that tracks each individual. This agent-based model examines whether the creation of an extracellular matrix (ECM) scaffold via NCC remodelling of an initially punctate structure allows trailing cells to robustly follow their leaders and form consistent streams. Global sensitivity analyses and simulated gain- and loss-of-function experiments suggest that long-distance migration towards target sites most likely occurs when cells at the front specialize in creating ECM fibres and trailing cells efficiently read these cues though upregulating contact guidance (a process by which cells align along ECM structures). This model therefore provides testable hypotheses about the NCC microenvironment and its role in inducing cell heterogeneity, but is computationally expensive to simulate. This motivates the second part of the talk, which describes a computational pipeline for developing more analytically tractable continuous models that match ensemble average dynamics of a given individual-level framework. Using the illustrative example of zebrafish skin pattern formation,  we use this pipeline to match a continuous and discrete models that each describe the movement and proliferation of a single cell population. By fitting parameters controlling the time scale of each mechanism, we show how this pipeline can provide accurate descriptions of individual-level data and highlight the importance of accounting for possible synergistic effects when multiple mechanisms occur simultaneously.