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A model reduction technique for stochastic biochemical kinetics

机译：随机生化动力学的模型还原技术

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Modeling the intermolecular reactions in a single cell is a critical problem in computational biology. Biochemical reaction systems often involve species in both low and large population numbers, as is the case of genetic regulatory networks. Then, random fluctuations due to small population numbers may be significant. Hence, stochastic mathematical models are needed to accurately capture the dynamics of the system. In addition, biochemical systems are typically quite complex, involving a large number of components and interactions. This complexity posses great challenges for simulation and analysis of the system. Model reduction techniques aim at reducing the complexity of the system by selecting only the important reactions and species, while retaining the essential features of the full system. We analyze in this paper a novel model reduction technique for stochastic models of biochemical reactions based on sensitivity analysis. We apply this approach to a model of transcriptional regulation and a model of the expression and activity of LacZ and LacY proteins in E. coli.
机译：建模单个电池中的分子间反应是计算生物学中的一个关键问题。生物化学反应系统通常涉及低和大人口数的物种，就像遗传监管网络的情况一样。然后，由于人口数小的较小的随机波动可能是显着的。因此，需要随机数学模型来准确捕获系统的动态。此外，生物化学系统通常非常复杂，涉及大量组件和相互作用。这种复杂性对系统的仿真和分析具有巨大挑战。模型减少技术旨在通过仅选择重要的反应和物种来降低系统的复杂性，同时保留完整系统的基本特征。本文分析了一种基于灵敏度分析的生物化学反应随机模型的新型模型还原技术。我们将这种方法应用于转录调节模型和LacZ和Lacy蛋白在大肠杆菌中的表达和活性模型。

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