基于加权轨迹集合的化学动力学网络的高效随机模拟
Efficient Stochastic Simulation of Chemical Kinetics Networks using a Weighted Ensemble of Trajectories
摘要：
我们应用“加权系综”（WE）的模拟策略，以前采用的背景下，分子动力学模拟，一系列的系统生物学模型，范围从一维的复杂性与354种和3680个反应的系统。WE相对容易实现，不需要大量的手动调整参数，不依赖于模拟算法的细节，并且可以方便地模拟极其罕见的事件。对于耦合随机反应系统，我们研究，我们能够产生准确和有效的近似的联合概率分布的所有化学物种的所有时间t。我们也能够有效地提取平均首次通过时间的系统，通过建设一个稳态条件与反馈。在这里研究的所有情况下，我们的结果同意独立的计算，但显着提高精度，罕见或缓慢的过程可以表征。通过吉莱斯皮直接随机模拟算法对分布中的稀有状态进行采样时，相对于“蛮力”的加速范围为~10^12 ~10^20，对于平均首次通过时间的计算，加速范围为~10^2 ~10^4。
英文摘要：
We apply the "weighted ensemble" (WE) simulation strategy, previously employed in the context of molecular dynamics simulations, to a series of systems-biology models that range in complexity from one-dimensional to a system with 354 species and 3680 reactions. WE is relatively easy to implement, does not require extensive hand-tuning of parameters, does not depend on the details of the simulation algorithm, and can facilitate the simulation of extremely rare events. For the coupled stochastic reaction systems we study, WE is able to produce accurate and efficient approximations of the joint probability distribution for all chemical species for all time t. WE is also able to efficiently extract mean first passage times for the systems, via the construction of a steady-state condition with feedback. In all cases studied here, WE results agree with independent calculations, but significantly enhance the precision with which rare or slow processes can be characterized. Speedups over "brute-force" in sampling rare events via the Gillespie direct Stochastic Simulation Algorithm range from ~10^12 to ~10^20 for rare states in a distribution, and ~10^2 to ~10^4 for finding mean first passage times.
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