Hierarchical structure of the protein-protein interaction networks

We employed the random graph theory approach to analyze data for seven species in the protein-protein interaction database DIP. Several global topological parameters were used to characterize the protein-protein interaction networks (PINs) for each species. The plots of the logarithm of the node degree cumulative distribution P-cum(k) vs. the logarithm of node degree k indicates that PINs follow the power law (P-cum(k) similar to k(-alpha)). Good evidence by correlation analysis supports the fact that the seven PINs are well approximated by scale-free networks. We found that the logarithm of C-ave(k) scales with k (i.e. C-ave(k) similar to k(-beta)) for E. coli and yeast. In particular, we determine that the E. coli and the yeast PINs are well represented by the stochastic and deterministic hierarchical network models, respectively. These results suggest that the hierarchical network model is a good description for certain species' PINs, but this may not be a universal feature across different species.