Reduced stochastic models for complex molecular systems
Reduced stochastic models for complex molecular systems
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DOI：
10.1007/s00791-006-0021-1
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发表时间：
2006-06-01
影响因子：
--
通讯作者：
Schuette, Christof
中科院分区：
文献类型：
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作者：
Horenko, Illia;Dittmer, Evelyn;Schuette, Christof
我们提出了一种新的数值方法，用于从时间序列信息中识别具有复杂动力学行为的系统的最重要的亚稳态。该方法基于亚稳态之间的马尔可夫跳跃过程来表示整个系统的有效动力学，并用相当简单的随机微分方程组(SDE)来表示每个亚稳态内的动力学。它的算法实现利用了隐马尔可夫模型(HMM)的概念，该模型的输出行为由SDE给出。Horenko等人已经提出了第一个完整的算法，其中包括一个显式的基于Euler-Maruyama的似然估计。(彩信，2006a)。在这里，我们提出了一种半隐式指数估计量，与基于Euler-Maruyama的估计量相比，它还允许对单次观测之间的时间步长较大的时间序列进行可靠的参数优化。与基于Euler-Maruyambase的估计器进行了详细的比较，最后将其应用于100 ns B-DNA分子动力学模拟的时间序列。
We present a new numerical method for the identification of the most important metastable states of a system with complicated dynamical behavior from time series information. The approach is based on the representation of the effective dynamics of the full system by a Markov jump process between metastable states, and the dynamics within each of these metastable states by rather simple stochastic differential equations (SDEs). Its algorithmic realization exploits the concept of hidden Markov models (HMMs) with output behavior given by SDEs. A first complete algorithm including an explicit Euler-Maruyama-based likelihood estimator has already been presented in Horenko et al. (MMS, 2006a). Herein, we present a semi-implicit exponential estimator that, in contrast to the Euler-Maruyama- based estimator, also allows for reliable parameter optimization for time series where the time steps between single observations are large. The performance of the resulting method is demonstrated for some generic examples, in detail compared to the Euler-Maruyamabased estimator, and finally applied to time series originating from a 100 ns B-DNA molecular dynamics simulation.