Stochastic simulation of large biochemical systems by approximate Gillespie algorithm

V. Purutçuoğlu
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引用次数: 0

Abstract

In recent years the fast innovations for the development of new methods and algorithms in system biology enable the biologists to analyze and interpret the complex biochemical structures. One of the speedy development has been seen in mathematical methods for generating these complex systems on the computer. These techniques help the researcher to ask biologically interesting questions and test their expectations before starting their biological experiments. There are a number of methods which can approximately simulate the biochemical systems in a computationally efficient way. In this study we present two applications of a recently developed simulation technique, called the approximate Gillespie, for approximately producing large systems with realistic complexity. We evaluate the performance of the new algorithm by comparing its simulation results with the ones generated from the well-known exact simulation technique, namely the Gillespie method.
基于近似Gillespie算法的大型生化系统随机模拟
近年来,系统生物学中新方法和算法的快速发展使生物学家能够分析和解释复杂的生化结构。在计算机上生成这些复杂系统的数学方法得到了迅速的发展。这些技术帮助研究人员提出生物学上有趣的问题,并在开始生物学实验之前测试他们的期望。有许多方法可以以计算效率高的方式近似模拟生化系统。在这项研究中,我们提出了最近开发的模拟技术的两种应用,称为近似Gillespie,用于近似地产生具有现实复杂性的大型系统。我们通过将新算法的仿真结果与著名的精确仿真技术(即Gillespie方法)的仿真结果进行比较来评估新算法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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