Three-component contour dynamics model to simulate and analyze amoeboid cell motility in two dimensions

Area of Science:

• Biophysics
• Computational Biology
• Cell Biology

Background:

• Amoeboid cell motility is crucial for wound healing, cancer metastasis, and embryonic development.
• Existing computational models struggle with inferring cell expansion/retraction, classifying cell types, and predicting motility behavior.
• Understanding cell shape dynamics is key to deciphering these complex biological processes.

Purpose of the Study:

• To develop a novel computational model for spatio-temporal evolution of 2D cell contours.
• To accurately infer and classify amoeboid cell motility behaviors.
• To correlate inferred cell motility components with biological biomarkers.

Main Methods:

• A novel model incorporating stochastic protrusions and deterministic retractions (self-exciting Poisson process, area-preserving curve-shortening flow, area adjustment flow).
• Generation of diverse cell track data resembling experimental observations.
• Application to experimental cell tracks from Dictyostelium discoideum for parameter estimation and classification.

Main Results:

• The model successfully generates polarized and non-polarized cell tracks.
• Inference of the cell protrusion component and its correlation with F-actin density and motion.
• Fast and straightforward parameter estimation allows classification into amoeboid and fan-shaped types.

Conclusions:

• The proposed model offers a powerful tool for analyzing and simulating amoeboid cell motility.
• It provides a simple method for classifying cell contour dynamics.
• The open-source implementation (AmoePy) facilitates further research in cell migration.