Cell Polarity and Movement with Reaction-Diffusion and Moving Boundary: Rigorous Modeling and Robust Simulations

TL;DR

This paper rigorously derives a sharp-interface model for cell polarity and movement from a phase-field model, and develops a robust numerical approach to simulate cell dynamics, capturing various trajectories and key parameters influencing cell behavior.

Contribution

The work provides a rigorous derivation of the sharp-interface model from a phase-field model and introduces a robust numerical method combining level-set and discretization techniques for cell movement simulations.

Findings

Predicts cell polarization under various stimuli

Captures linear and circular cell trajectories over time

Identifies key parameters controlling cell movement

Abstract

Cell polarity and movement are fundamental to many biological functions. Experimental and theoretically studies have indicated that interactions of certain proteins lead to the cell polarization which plays a key role in controlling the cell movement. We study the cell polarity and movement based on a class of biophysical models that consist of reaction-diffusion equations for different proteins and the dynamics of moving cell boundary. Such a moving boundary is often simulated by a phase-filed model. We first apply the matched asymptotic analysis to give a rigorous derivation of the sharp-interface model of the cell boundary from a phase-field model. We then develop a robust numerical approach that combines the level-set method to track the sharp boundary of a moving cell and accurate discretization techniques for solving the reaction-diffusion equations on the moving cell region. Our extensive numerical simulations predict the cell polarization under various kinds of stimulus, and capture both the linear and circular trajectories of a moving cell for a long period of time. In particular, we have identified some key parameters controlling different cell trajectories that are less accurately predicted by reduced models. Our work has linked different models and also developed tools that can be adapted for the challenging three-dimensional simulations.