title: Revisiting the variation of clustering coefficient of biological networks suggests new modular structure.
abstract: BACKGROUND
  A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary.
  
  
  RESULTS
  We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn't show dependence of degree.
  
  
  CONCLUSIONS
  Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to "deterministic model" of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China. dapenghao@hotmail.com
authors.forename: Dapeng
authors.initials: D
authors.lastname: Hao
authors.forename: Cong
authors.initials: C
authors.lastname: Ren
authors.forename: Chuanxing
authors.initials: C
authors.lastname: Li
mesh_terms: D016000:Cluster Analysis; D004926:Escherichia coli / Q000378:metabolism; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D049490:Systems Biology / Q000379:methods*