Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph
Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph
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DOI：
10.1093/bioinformatics/bth167
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发表时间：
2004-08-12
期刊：
影响因子：
5.8
通讯作者：
Zeng, AP
中科院分区：
文献类型：
--
作者：
Ma, HW;Zhao, XM;Zeng, AP
动机：代谢网络是以模块化、层次化的方式组织起来的。将代谢网络合理分解为相对独立的功能子集的方法对于更好地理解大规模全基因组网络的模块化和组织原理至关重要。代谢网络的复杂性使得代谢途径分析方法常常受到组合爆炸问题的阻碍，网络分解对于代谢功能分析也是必要的。文献中提出的分解方法主要是基于代谢产物的连接度。为了得到一个更合理的分解，代谢网络的全局连接结构应考虑到consider.Results：在这项工作中，我们使用的反应图表示的代谢网络的全局连接结构的识别和分解。一个蝴蝶结连接结构类似于以前发现的代谢物图中发现的反应图中也存在。基于这种蝴蝶结结构，提出了一种新的分解方法，它使用的距离定义来自两个反应之间的路径长度。首先，从蝴蝶结结构的巨强组分中的反应之间的距离矩阵构造层次分类树。然后，这些反应被分组到不同的子集的基础上的层次树。蝴蝶结结构的IN和OUT子集中的反应随后根据“多数规则”被放置在相应的子集中。与文献中提出的分解方法相比，我们的方法是基于全局网络结构和局部反应连接性的组合性质，而不是主要基于代谢物的连接度。将该方法应用于大肠杆菌代谢网络的分解。得到11个子集。更详细的研究表明，在同一子集的反应是真正的功能相关。代谢网络的合理分解以及随后对子集的研究，使得在模块化水平上更容易理解代谢网络的内在组织和功能。
Motivation: Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account.Results: In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level.