一类新的高效化学反应网络精确随机模拟算法
A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks
摘要：
我们介绍了另一种配方的精确随机模拟算法（SSA）的采样轨迹的化学主方程的耦合化学反应的搅拌系统。我们的公式是基于因子分解，部分反应倾向。这种新的精确SSA，被称为部分倾向直接法（PDM），是非常有效的，并具有计算成本，规模最多与化学物种的数量线性，无论反应网络的耦合程度。此外，我们提出了一个排序的变种，SPDM，这是特别有效的多尺度反应网络。
英文摘要：
We introduce an alternative formulation of the exact stochastic simulation algorithm (SSA) for sampling trajectories of the chemical master equation for a well-stirred system of coupled chemical reactions. Our formulation is based on factored-out, partial reaction propensities. This novel exact SSA, called the partial propensity direct method (PDM), is highly efficient and has a computational cost that scales at most linearly with the number of chemical species, irrespective of the degree of coupling of the reaction network. In addition, we propose a sorting variant, SPDM, which is especially efficient for multiscale reaction networks.
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