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首页> 外文期刊>Journal of Theoretical Biology >Elementary network reconstruction: a framework for the analysis of regulatory networks in biological systems.
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Elementary network reconstruction: a framework for the analysis of regulatory networks in biological systems.

机译：基本网络重建：用于分析生物系统中监管网络的框架。

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Complexity of regulatory networks arises from the high degree of interaction between network components such as DNA, RNA, proteins, and metabolites. We have developed a modeling tool, elementary network reconstruction (ENR), to characterize these networks. ENR is a knowledge-driven, steady state, deterministic, quantitative modeling approach based on linear perturbation theory. In ENR we demonstrate a novel means of expressing control mechanisms by way of dimensionless steady state gains relating input and output variables, which are purely in terms of species abundances (extensive variables). As a result of systematic enumeration of network species in nxn matrix, the two properties of linear perturbation are manifested in graphical representations: transitive property is evident in a special L-shape structure, and additive property is evident in multiple L-shape structures arriving at the same matrix cell. Upon imposing mechanistic (lowest-level) gains, network self-assembly through transitive and additive properties results in elucidation of inherent topology and explicit cataloging of higher level gains, which in turn can be used to predict perturbation results. Application of ENR to the regulatory network behind carbon catabolite repression in Escherichia coli is presented. Through incorporation of known molecular mechanisms governing transient and permanent repressions, the ENR model correctly predicts several key features of this regulatory network, including a 50% downshift in intracellular cAMP level upon exposure to glucose. Since functional genomics studies are mainly concerned with redistribution of species abundances in perturbed systems, ENR could be exploited in the system-level analysis of biological systems.
机译：调节网络的复杂性来自网络组件（例如DNA，RNA，蛋白质和代谢产物）之间的高度相互作用。我们开发了一种建模工具，即基本网络重构（ENR），以表征这些网络。 ENR是一种基于线性扰动理论的知识驱动的稳态确定性定量建模方法。在ENR中，我们展示了一种通过与输入和输出变量相关的无量纲稳态增益来表达控制机制的新颖方法，这些变量纯粹是根据物种的丰度（广泛的变量）来表示的。由于系统地枚举了nxn矩阵中的网络物种，因此线性扰动的两个特性在图形表示中得以体现：传递特性在特殊的L形结构中很明显，加性在到达的多个L形结构中很明显相同的矩阵单元。施加机械（最低级别）增益后，通过传递和加性的网络自组装会导致对固有拓扑的阐明和对更高级别增益的显式分类，进而可以用来预测扰动结果。介绍了ENR在大肠杆菌中碳分解代谢物阻抑背后的调控网络中的应用。通过整合已知的控制瞬时和永久抑制的分子机制，ENR模型可以正确预测该调节网络的几个关键特征，包括暴露于葡萄糖后细胞内cAMP水平降低50％。由于功能基因组学研究主要涉及扰动系统中物种丰度的重新分布，因此可以在生物系统的系统级分析中利用ENR。

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