Dong Guang Qiang, Jakobowski Luke, Iafolla Marco A J, McMillen David R
Institute for Optical Sciences and Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada.
J Biol Phys. 2007 Feb;33(1):67-95. doi: 10.1007/s10867-007-9043-2. Epub 2007 Sep 5.
Biological systems often involve chemical reactions occurring in low-molecule-number regimes, where fluctuations are not negligible and thus stochastic models are required to capture the system behaviour. The resulting models are generally quite large and complex, involving many reactions and species. For clarity and computational tractability, it is important to be able to simplify these systems to equivalent ones involving fewer elements. While many model simplification approaches have been developed for deterministic systems, there has been limited work on applying these approaches to stochastic modelling. Here, we describe a method that reduces the complexity of stochastic biochemical network models, and apply this method to the reduction of a mammalian signalling cascade and a detailed model of the process of bacterial gene expression. Our results indicate that the simplified model gives an accurate representation for not only the average numbers of all species, but also for the associated fluctuations and statistical parameters.
生物系统通常涉及在低分子数状态下发生的化学反应，在这种状态下波动不可忽略，因此需要随机模型来捕捉系统行为。由此产生的模型通常相当大且复杂，涉及许多反应和物种。为了清晰和便于计算，能够将这些系统简化为涉及较少元素的等效系统非常重要。虽然已经为确定性系统开发了许多模型简化方法，但将这些方法应用于随机建模的工作却很有限。在这里，我们描述了一种降低随机生化网络模型复杂性的方法，并将该方法应用于简化哺乳动物信号级联反应以及细菌基因表达过程的详细模型。我们的结果表明，简化后的模型不仅能准确表示所有物种的平均数量，还能准确表示相关的波动和统计参数。
