Spatial structure arising from neighbour-dependent bias in collective cell movement

Area of Science:

• Mathematical Biology
• Cellular Dynamics
• Biophysics

Background:

• Collective cell movement models often use mean-field assumptions, neglecting spatial structure and local interactions.
• In vitro studies indicate spatial correlations significantly influence collective cell behavior.

Purpose of the Study:

• To investigate how individual cell interactions generate spatial structure in moving cell populations.
• To develop and validate a model that incorporates spatial correlations for improved population dynamics.

Main Methods:

• Quantified spatial structure using in vitro imaging data of 3T3 fibroblast cells.
• Developed a lattice-free individual-based model (IBM) simulating cell movement in 2D.
• Derived a continuum description using spatial moments, including the second moment for cell-pair correlations.

Main Results:

• The individual-based model demonstrates how local directional bias generates spatial structure.
• The moment dynamics description approximates averaged IBM simulation results.
• Model parameters, estimated from experimental data, enable reproduction of observed spatial structure in 3T3 fibroblast populations.

Conclusions:

• Local interactions and spatial correlations are crucial for accurate modeling of collective cell movement.
• The derived moment dynamics offer a viable continuum approach to capture population-level spatial structure.
• The combined experimental and modeling approach provides a robust framework for studying cell population dynamics.