随机反应扩散格气模型上钙动力学的粗粒化模拟

发展了一种基于随机格气模型的粗粒化方法，该方法能有效模拟内质网表面钙动力学信息. 首先将相邻的微观节点合并成粗粒化节点，再根据局域平均场近似推导出粗粒化反应速率，然后执行粗粒化动力学蒙特卡洛模拟. 发现粗粒化动力学蒙特卡洛模拟结果和微观模拟结果非常吻合. 有趣的是，存在一个最佳的粗粒化比m，使得粗粒化模拟与微观模拟的相变点偏差最小. 固定m，发现临界点随体系尺度增加而单调增加，而且相变点的偏差与体系尺度存在一个标度关系.此外，该粗粒化方法大大地加快了蒙特卡洛模拟速率，并且与微观模拟直接相关. 该方法可以广泛用来研究体系尺度效应，而节省大量计算时间.

Coarse-graining Calcium Dynamics on Stochastic Reaction-diffusion Lattice Model

We develop a coarse grained (CG) approach for efficiently simulating calcium dynamics in the endoplasmic reticulum membrane based on a fine stochastic lattice gas model. By grouping neighboring microscopic sites together into CG cells and deriving CG reaction rates using local mean field approximation, we perform CG kinetic Monte Carlo (kMC) simulations and find the results of CG-kMC simulations are in excellent agreement with that of the microscopic ones. Strikingly, there is an appropriate range of coarse proportion m, corresponding to the minimal deviation of the phase transition point compared to the microscopic one. For fixed m, the critical point increases monotonously as the system size increases, especially, there exists scaling law between the deviations of the phase transition point and the system size. Moreover, the CG approach provides significantly faster Monte Carlo simulations which are easy to implement and are directly related to the microscopics, so that one can study the system size effects at the cost of reasonable computational time.