[Protein-protein interacting networks, their structures and disease-related mutations]

In recent years, the comparison of protein interactomes has identified conserved modules, that could represent functional nuclei with a common ancestry. Within this context, recent analyses of protein-protein interacting networks have led to a debate on the influence of the experimental method on the quality and biological pertinence of these data. It is crucial to understand the measure in which divergence between networks of different species reflect sampling biases in respective experimental methods, as opposed to topological features dictated by biological functionality. This aspect requires novel, precise and practical mathematical tools, to quantify and compare high resolution networks. To this end, we have studied the relationship between pools of aleatory graphs and real biological signalization networks, while stressing the number of graph cycles in the networks, which represent complexes in experimental protein interactomes. By combining methods for graph and algorithm dynamics to count the loops, we evaluate the relative importance of the loops in biological networks in comparison with network analyses.