基于边过滤的代谢网络社团结构分析

Analysis of community structure in metabolic networks based on edge percolated

社团结构分析有助于理解新陈代谢网络的结构和功能关系,是新陈代谢网络研究领域的一个重要研究主题。然而,直接将复杂网络方法应用到新陈代谢网络中时,很难得到具有实际生物学意义的社团。本文首先构建了高质量人类代谢网络模型的巨强连通体,然后采用一种基于边过滤的技术研究了该巨强连通体,得到的8个主要社团均具备较好的生物学意义。研究结果表明:基于边过滤的技术可用于识别新陈代谢网络中的社团。

Community structure could helpful for understanding the structure and function of metabolic networks, and thus being an important subject in metabolic networks study. However, when complex networks based community structure methods are applied to metabolic networks, resulted modules often biological insignificance. In this article, we first obtain all metabolic reactions in giant strong component of human metabolic network fi＇om published high-quality human metabolic network models, and then study community structure in the model with an edge percolated method, the 8 communities obtained all with better biological significance. The results suggest that edge percolated could identify functional communities in metabolic networks.