  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.
