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.
翻译:实验和理论上的研究显示,某些蛋白质的相互作用导致细胞极化,而细胞极化在控制细胞运动方面起着关键的作用。我们根据一组生物物理模型来研究细胞极性和运动,这些模型包括不同蛋白的反扩散方程式和移动细胞边界的动态。这种移动的边界往往用一个相向的模型模拟。我们首先应用相匹配的无症状分析,从一个相向模型中严格推断出细胞边界的尖锐界面模型。我们随后开发了一种强大的数字方法,将跟踪移动细胞的尖锐边界和精确的离散技术结合起来,以解决移动细胞区域的反扩散方程式。我们的广泛数字模拟预测了各种刺激下的细胞两极化,并记录了长期移动细胞的线性和圆形轨迹。特别是,我们找出了控制不同细胞轨迹的关键参数,这些参数通过减少的模型可以不太精确地预测。我们的工作还结合了不同的模型和开发的工具,这些模型和工具具有挑战性。