Department of Computer Science, University of California Santa Barbara, Santa Barbara, California 93106, USA.
J Chem Phys. 2012 Jul 21;137(3):034106. doi: 10.1063/1.4733563.
Multiple time scales in cellular chemical reaction systems present a challenge for the efficiency of stochastic simulation. Numerous model reductions have been proposed to accelerate the simulation of chemically reacting systems by exploiting time scale separation. However, these are often identified and deployed manually, requiring expert knowledge. This is time-consuming, prone to error, and opportunities for model reduction may be missed, particularly for large models. We propose an automatic model analysis algorithm using an adaptively weighted Petri net to dynamically identify opportunities for model reductions for both the stochastic simulation algorithm and tau-leaping simulation, with no requirement of expert knowledge input. Results are presented to demonstrate the utility and effectiveness of this approach.
细胞化学反应系统中的多个时间尺度给随机模拟的效率带来了挑战。为了利用时间尺度分离来加速化学反应系统的模拟，已经提出了许多模型简化方法。然而，这些通常需要专家知识来手动识别和部署。这既耗时又容易出错，并且可能会错过模型简化的机会，尤其是对于大型模型。我们提出了一种使用自适应加权 Petri 网的自动模型分析算法，用于动态识别随机模拟算法和 tau 跳跃模拟的模型简化机会，而无需输入专家知识。结果表明了该方法的实用性和有效性。
