Edinburgh Napier University

  The aim of our work is to accumulate protein interaction data and to develop computational techniques for analysing the topologies of protein interaction networks to reveal network vulnerabilities. We have developed a variety of network analysis algorithms which aim to discover network vulnerabilities, consisting of small sets of proteins, which may be used to aid the discovery of pharmaceutical drug targets for fighting antimicrobial infections. The developed algorithms include the recognition of highly connected [hub] nodes, [bottleneck] nodes which connect network sub-clusters, and the analysis of nodes which participate in topological structures such as clusters, pathways and loops. A composite database has been constructed which combines the complete sets of protein interaction data available from the Database of Interacting Proteins (DIP), the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Kyoto Encyclopaedia of genes and Genomes (KEGG) Pathway database. Following an analysis of the Bacillus subtilis protein interaction network, it was found that 40% of the protein targets predicted by our techniques are encoded by genes known to be essential for the survival of the organism, therefore supporting the effectiveness of our approach. Further work will involve the analysis of other bacteria and the development of new graph theoretic techniques for network analysis.