Department of Chemical and Biomolecular Engineering, Rice University, 6100 Main Street, MS-362, Houston, TX 77005, USA.
J Theor Biol. 2010 Apr 21;263(4):499-509. doi: 10.1016/j.jtbi.2009.12.007. Epub 2009 Dec 22.
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.
调控网络的复杂性源于网络组件（如 DNA、RNA、蛋白质和代谢物）之间高度的相互作用。我们开发了一种建模工具，即基本网络重建（ENR），用于描述这些网络。ENR 是一种基于线性摄动理论的知识驱动的稳态、确定性、定量建模方法。在 ENR 中，我们通过输入和输出变量之间的无量纲稳态增益来表达控制机制，这完全是基于物种丰度（广延变量）的新方法。由于在 nxn 矩阵中系统地枚举了网络物种，线性摄动的两个性质在图形表示中表现出来：传递性质在特殊的 L 形结构中显而易见，而加性性质在到达同一矩阵单元的多个 L 形结构中显而易见。在施加机制（最低水平）增益后，通过传递和加性性质进行网络自组装，阐明了固有拓扑结构，并明确列出了更高水平的增益，这些增益反过来又可用于预测摄动结果。本文介绍了 ENR 在大肠杆菌碳分解物阻遏调控网络中的应用。通过纳入控制瞬时和永久阻遏的已知分子机制，ENR 模型正确预测了该调控网络的几个关键特征，包括在暴露于葡萄糖时细胞内 cAMP 水平下降 50%。由于功能基因组学研究主要关注扰动系统中物种丰度的再分配，因此 ENR 可用于生物系统的系统级分析。
