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Inferring Dynamic Properties of Biochemical Reaction Networks from Structural Knowledge

机译:从结构知识推断生化反应网络的动态特性

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Functional properties of biochemical networks depend on both the network structure and the kinetic parameters. Extensive data on metabolic network topologies have been collected in databases, but much less information is available about the kinetic constants or metabolite concentrations. Depending on the values of these parameters, metabolic fluxes and control coefficients may vary within a wide range. Nevertheless, some of the parameters may have little influence on the observ-ables of interest. We address the question whether, despite uncertainty about kinetic parameters, probabilistic statements can be made about dynamic network features. To this end, we perform a variability analysis of the parameters: assuming that the parameters follow statistical distributions, we compute the resulting distributions of the network properties like metabolic fluxes, concentrations, or control coefficients by Monte Carlo simulation. In this manner, we study systematically the possible distributions arising from typical topologies of biochemical networks such as linear chains, branched networks, and signaling and gene expression cascades. This analysis reveals how much information about dynamic behavior can be drawn from structural knowledge.
机译:生物化学网络的功能特性取决于网络结构和动力学参数。在数据库中收集了关于代谢网络拓扑的广泛数据,但信息较少的信息仍然可以达到动力学常数或代谢物浓度。根据这些参数的值,代谢助熔剂和控制系数可以在宽范围内变化。然而,一些参数可能对观察到的感兴趣的影响几乎没有影响。我们解决问题,尽管有关动力学参数的不确定性,但可以对动态网络功能进行概率陈述。为此,我们执行参数的可变性分析:假设参数遵循统计分布,我们通过Monte Carlo仿真计算等代谢助熔剂,浓度或控制系数的网络属性的产生的分布。以这种方式,我们系统地研究了生物化学网络等生物化网络等典型拓扑的可能分布,例如线性链,分支网络和信号传导和基因表达级联。该分析显示可以从结构知识中汲取有关动态行为的信息。

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