Mathematical model results for: Dynamic fibronectin assembly and remodeling by leader neural crest cells prevents jamming in collective cell migration

Collective cell migration plays an essential role in vertebrate development, yet the extent to which dynamically changing microenvironments influence this phenomenon remains unclear. Observations of the distribution of the extracellular matrix (ECM) component fibronectin during the migration of loosely connected neural crest cells (NCCs) lead us to hypothesize that NCC remodeling of an initially punctate ECM creates a scaffold for trailing cells, enabling them to form robust and coherent stream patterns. We evaluate this idea in a theoretical setting by developing an agent-based model that incorporates reciprocal interactions between NCCs and their ECM. ECM remodeling, haptotaxis, contact guidance, and cell-cell repulsion are sufficient for cells to establish streams in silico, however additional mechanisms, such as chemotaxis, are required to consistently guide cells along the correct target corridor. Further investigations of the model imply that contact guidance and differential cell-cell repulsion between leader and follower cells are key contributors to robust collective cell migration by preventing stream breakage. Global sensitivity analysis and simulated underexpression/overexpression experiments suggest that long-distance migration without jamming is most likely to occur when leading cells specialize in creating ECM fibers, and trailing cells specialize in responding to environmental cues by upregulating mechanisms such as contact guidance. This dataset contains summary statistics, movies, parameter values, and photos obtained from individual realizations of the mathematical model.

Methods
Data was generated by running multiple simulations of the agent-based model described in the article. The model was implemented in C++, using the PhysiCell library (http://physicell.org). Post-processing of the data (e.g., creation of animations and photos of individual simulations) was accomplished by using PhysiCell-supplied Python scripts, as well as user-created Matlab scripts. The corresponding code can be found at the following Github repository: XXXXXX. 

Statistical analysis of the data was also performed in Matlab, using codes that were inspired by older implementations originally developed by Marino et al. (http://malthus.micro.med.umich.edu/lab/usadata/).

Violin plots were generated using open-source code available at the following Github repository: https://github.com/bastibe/Violinplot-Matlab.