首页> 外文会议>Research in Computational Molecular Biology; Lecture Notes in Bioinformatics; 4453 >Production-Passage-Time Approximation: A New Approximation Method to Accelerate the Simulation Process of Enzymatic Reactions
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Production-Passage-Time Approximation: A New Approximation Method to Accelerate the Simulation Process of Enzymatic Reactions

机译:生产时间近似:一种新的近似方法,可加速酶促反应的模拟过程

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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 substantial acceleration 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 significant loss in accuracy.
机译:考虑到随机模拟的大量计算需求,近似值对于有效分析任何现实的生化系统至关重要。本文介绍了一种新的近似方法,可减少酶促反应方案随机模拟的计算成本,该方案在生化系统中通常包括与数量很少的酶和酶-底物复杂分子发生快速变化的快速反应。我们的新方法通过近似每个目标是生产反应的酶-底物复合物分子的形成过程的时间来消除底物解离反应。这种方法跳过了不重要但昂贵的反应事件的触发,从而大大促进了酶促反应的随机模拟。另外,由于我们新方法中使用的所有参数都可以通过Michaelis-Menten参数推导出,而Michaelis-Menten参数实际上可以从实验数据中进行测量,因此即使不完全了解基本的酶促反应,这种近似方法的应用也是可行的。此外,由于我们的方法不需要针对酶促反应的定制模拟程序,因此它使包括此类反应的生化系统仍然可以利用标准的随机模拟工具。在这里,我们将此新方法应用于各种酶反应系统,从而在时间行为分析中加快了数量级的速度,而准确性没有任何重大损失。

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