Mining and analyzing the topological structure of protein–protein interaction networks

Paper presented at the ACM Symposium on Applied Computing (Bioinformatics Track), April 23-27, 2006, Dijon, Bourgogne, France. Retrieved 6/26/2006 from http://www.ischool.drexel.edu/faculty/thu/My%20Publication/Conference-papers/ACMACWu.pdf.We report a comprehensive evaluation of the topological structure of protein-protein interaction (PPI) networks by mining and analyzing graphs constructed from the popular data sets publicly available to the bioinformatics research community. We compare the topology of these networks across different species, different confidence levels, and different experimental systems used to obtain the interaction data. Our results confirm the well-accepted claim that the degree distribution follows a power law. However, further statistical analysis shows that residues are not independent on the fit values, indicating that the power law model may be inadequate. Our results also show that the dependence of the average clustering coefficient on the vertices degree is far from a power law, contradicting many published results. For the first time, we report that the average vertex density exhibits a strong powder law dependence on the vertices degree for all the networks studied, regardless of species, confidence levels, and experimental systems

The structure and function of biological networks

Biology has been revolutionized in recent years by an explosion in the availability of data. Transforming this new wealth of data into meaningful biological insights and clinical breakthroughs requires a complete overhaul both in the questions being asked and the methodologies used to answer them. A major challenge in organizing and understanding the data is the ability to define the structure in biological systems, especially high level structures. Networks are a powerful and versatile tool useful in bridging the data and the complex biological systems. To address the importance of the higher-level modular and hierarchical structure in biological networks, we have investigated in this thesis the topological structure of protein-protein interaction networks through a comprehensive network analysis using statistical and computational techniques and publicly available protein-protein interaction data sets. Furthermore, we have designed and implemented a novel and efficient computational approach to identify modules from a seed protein. The experiment results demonstrate the efficiency and effectiveness of this approach in finding a module whose members exhibit high functional coherency. In addition, toward quantitative studies of protein translation regulatory networks (PTRN), we have developed a novel approach to reconstruct the PTRN through integration of protein-protein interaction data and Gene Ontology annotations. We have applied computational techniques based on hierarchical random graph model on these reconstructed PTRN to explore their modular and hierarchical and to detect missing and false positive links from these networks. The identification of the high order structures in these networks unveils insights into their functional organization.Ph.D., Information Science and Technology -- Drexel University, 201

A cross-species study of the protein-protein interaction networks via the random graph approach

[[abstract]]We employed the random graph theory approach to analyze the protein-protein interacting database DIP (Oct. 7 and Nov. 25, 2003), for six different species (S. cerevisiae, H. pylori, E. coli, H. sapiens, M. musculus and D. melanogaster). Two global topological parameters (node connectivity, average diameter) were used to characterize these protein-protein interaction networks (PINs). The logarithm of the connectivity distribution vs. the logarithm of connectivity plot indicates that it follows a power law behavior quite well for the six species. We also demonstrated that the interaction networks are quite robust when subject to random perturbation. Node degree correlation study supports the earlier results that nodes of low connectivity are correlated, whereas nodes of high connectivity are not directly linked. These results provided some evidence suggesting such correlation relations might be a general feature of the PINs across different species