A comprehensive statistical study of metabolic and protein-protein interaction network properties

TL;DR

This study conducts a detailed statistical analysis of metabolic and protein-protein interaction networks, revealing that biological network properties are influenced by mechanisms beyond degree distributions, with significant differences observed especially in PPI networks.

Contribution

It provides a comprehensive statistical characterization of biological networks and highlights properties that deviate from random models, suggesting underlying biological mechanisms.

Findings

PPI networks' degree distributions fit power-law only in the tail.

Biological networks exhibit properties beyond random degree-based models.

Significant differences found between real and randomized PPI networks.

Abstract

Understanding the mathematical properties of graphs underling biological systems could give hints on the evolutionary mechanisms behind these structures. In this article we perform a complete statistical analysis over thousands of graphs representing metabolic and protein-protein interaction (PPI) networks. First, we investigate the quality of fits obtained for the nodes degree distributions to power-law functions. This analysis suggests that a power-law distribution poorly describes the data except for the far right tail in the case of PPI networks. Next we obtain descriptive statistics for the main graph parameters and try to identify the properties that deviate from the expected values had the networks been built by randomly linking nodes with the same degree distribution. This survey identifies the properties of biological networks which are not solely the result of their degree distribution, but emerge from yet unidentified mechanisms other than those that drive these distributions. The findings suggest that, while PPI networks have properties that differ from their expected values in their randomized versions with great statistical significance, the differences for metabolic networks have a smaller statistical significance, though it is possible to identify some drift.