Department of Computer Science, University of California Santa Barbara, Santa Barbara, California 93106, USA.
J Chem Phys. 2011 Dec 21;135(23):234108. doi: 10.1063/1.3668100.
In recent years there has been substantial growth in the development of algorithms for characterizing rare events in stochastic biochemical systems. Two such algorithms, the state-dependent weighted stochastic simulation algorithm (swSSA) and the doubly weighted SSA (dwSSA) are extensions of the weighted SSA (wSSA) by H. Kuwahara and I. Mura [J. Chem. Phys. 129, 165101 (2008)]. The swSSA substantially reduces estimator variance by implementing system state-dependent importance sampling (IS) parameters, but lacks an automatic parameter identification strategy. In contrast, the dwSSA provides for the automatic determination of state-independent IS parameters, thus it is inefficient for systems whose states vary widely in time. We present a novel modification of the dwSSA--the state-dependent doubly weighted SSA (sdwSSA)--that combines the strengths of the swSSA and the dwSSA without inheriting their weaknesses. The sdwSSA automatically computes state-dependent IS parameters via the multilevel cross-entropy method. We apply the method to three examples: a reversible isomerization process, a yeast polarization model, and a lac operon model. Our results demonstrate that the sdwSSA offers substantial improvements over previous methods in terms of both accuracy and efficiency.
近年来，用于刻画随机生化系统中稀有事件的算法发展取得了实质性的增长。其中两种算法，即依赖于状态的加权随机模拟算法（swSSA）和双重加权 SSA（dwSSA），是由 H. Kuwahara 和 I. Mura 对加权 SSA（wSSA）的扩展[J. Chem. Phys. 129, 165101 (2008)]。swSSA 通过实现系统状态相关的重要性抽样（IS）参数，大大降低了估计量的方差，但缺乏自动参数识别策略。相比之下，dwSSA 提供了自动确定与状态无关的 IS 参数的方法，因此对于状态随时间变化很大的系统效率较低。我们提出了一种新颖的 dwSSA 变体——依赖于状态的双重加权 SSA（sdwSSA），它结合了 swSSA 和 dwSSA 的优势，同时避免了它们的弱点。sdwSSA 通过多层次交叉熵方法自动计算依赖于状态的 IS 参数。我们将该方法应用于三个示例：一个可逆异构化过程、酵母极化模型和 lac 操纵子模型。我们的结果表明，sdwSSA 在准确性和效率方面都比以前的方法有了实质性的改进。
