Coarse-graining molecular dynamics: stochastic models with non-Gaussian force distributions
Coarse-graining molecular dynamics: stochastic models with non-Gaussian force distributions
复制标题
DOI：
10.1007/s00285-019-01433-5
复制
发表时间：
2020-01-01
影响因子：
1.9
通讯作者：
Erban, Radek
中科院分区：
文献类型：
--
作者：
Erban, Radek
将原子和分子信息转化为细胞行为的模型是具有挑战性的，因为在原子和细胞水平上发生的过程之间在空间和时间尺度上存在巨大的分离。多尺度或多分辨率方法通过在细胞的不同部分使用分子动力学（MD）和粗粒度模型来解决这个困难。它们的适用性取决于近似详细MD描述的粗粒度模型的精度和属性。提出了一类随机粗粒化（SCG）模型，该模型可表示为相对低维的非线性随机微分方程组。非线性SCG模型采用了非高斯力分布，这是在MD模拟中观察到的，不能用线性模型来描述。结果表明，非线性可以选择这样一种方式，他们不复杂的参数化的SCG描述详细的MD模拟。SCG模型的解决方案中发现的伽马函数。
Incorporating atomistic and molecular information into models of cellular behaviour is challenging because of a vast separation of spatial and temporal scales between processes happening at the atomic and cellular levels. Multiscale or multi-resolution methodologies address this difficulty by using molecular dynamics (MD) and coarse-grained models in different parts of the cell. Their applicability depends on the accuracy and properties of the coarse-grained model which approximates the detailed MD description. A family of stochastic coarse-grained (SCG) models, written as relatively low-dimensional systems of nonlinear stochastic differential equations, is presented. The nonlinear SCG model incorporates the non-Gaussian force distribution which is observed in MD simulations and which cannot be described by linear models. It is shown that the nonlinearities can be chosen in such a way that they do not complicate parametrization of the SCG description by detailed MD simulations. The solution of the SCG model is found in terms of gamma functions.