Comparing individual-based models of collective cell motion in a benchmark flow geometry.

Area of Science:

• Computational Biology
• Biophysics
• Mathematical Modeling

Background:

• Collective cell migration is crucial for tissue development, cancer progression, and healing.
• Various computational models exist, from simple particle-based to complex cell-shape models.
• Comparing these models is essential for accurate simulation of biological processes.

Purpose of the Study:

• To compare five distinct collective cell migration models with increasing levels of detail.
• To evaluate model performance on a benchmark problem: flow around a circular obstacle.
• To provide guidance on selecting the most suitable model for specific research questions.

Main Methods:

• Implemented and analyzed five models: Vicsek-Grégoire particles, Szabó-like particles, self-propelled Voronoi, cellular Potts, and multiparticle cells.
• Used a channel flow around a circular obstacle as a benchmark, inspired by Stokes' experiment.
• Varied parameters like cell density, attraction, and alignment to observe behavioral differences.

Main Results:

• Generated velocity, density, and deformation fields for each model under identical conditions.
• Identified how different model assumptions and abstractions lead to distinct collective behaviors.
• Assessed the advantages and limitations of each model for simulating collective cell migration.

Conclusions:

• The study provides a framework for selecting appropriate computational models for collective cell migration research.
• It highlights similarities and differences between particle-based and cell-based models in capturing collective effects.
• Recommendations are offered for choosing models based on the specific biological question and desired level of detail.