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Production-Passage-Time Approximation: A New Approximation Method to Accelerate the Simulation Process of Enzymatic Reactions

机译:生产通行时间近似:加速酶促反应模拟过程的新近似方法

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Given the substantial computational requirements of stochastic simulation, approximation is essential for efficient analysis of any realistic biochemical system. This paper introduces a new approximation method to reduce the computational cost of stochastic simulations of an enzymatic reaction scheme which in biochemical systems often includes rapidly changing fast reactions with enzyme and enzyme-substrate complex molecules present in very small counts. Our new method removes the substrate dissociation reaction by approximating the passage time of the formation of each enzyme-substrate complex molecule which is destined to a production reaction. This approach skips the firings of unimportant yet expensive reaction events, resulting in a substantialacceleration in the stochastic simulations of enzymatic reactions. Additionally, since all the parameters used in our new approach can be derived by the Michaelis-Menten parameters which can actually be measured from experimental data, applications of this approximation can be practical even without having full knowledge of the underlying enzymatic reaction. Furthermore, since our approach does not require a customized simulation procedure for enzymatic reactions, it allows biochemical systems that include such reactions to still take advantage of standard stochastic simulation tools. Here, we apply this new method to various enzymatic reaction systems, resulting in a speedup of orders of magnitude in temporal behavior analysis without any significantloss in accuracy.
机译:鉴于随机模拟的实质性计算要求,近似对于任何现实生化系统有效分析至关重要。本文介绍了一种新的近似方法,以降低生化系统中的酶反应方案随机模拟的计算成本通常包括在非常小计数中与酶和酶 - 底物复杂分子的快速改变快速反应。我们的新方法通过近似形成注射到生产反应的每个酶底物复合分子的通过时间来除去基材解离反应。这种方法跳过不重要但昂贵的反应事件的烧制,导致酶促反应随机模拟中的实质性。另外,由于我们的新方法中使用的所有参数可以由实际测量的Michaelis-Menten参数来源,因为即使在不具有潜在的酶促反应的情况下,这种近似的应用也是实际的。此外,由于我们的方法不需要用于酶促反应的定制模拟程序,因此它允许生物化学系统包括这种反应仍然利用标准随机仿真工具。在这里,我们将这种新方法应用于各种酶促反应系统,导致时间行为分析中的数量级的加速,没有任何显着的准确性。

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