Generating phenograms using frequent structure mining over metabolic pathways

Phenograms have already been used many years back to depict taxonomic relationships among organisms based on overall similarity among a variety of characteristics available at the time. These recent years, however, have brought phenomenal advances in experimental techniques in biological research. These have resulted in large amounts of biological network data being unearthed. Among these are metabolic networks. Analyzing the network topology of these metabolic networks across taxa can uncover important biological information that is independent of other currently available biological information. This study explores topological similarities between the glycolysis and citrate cycle metabolic networks of different taxa to build phenograms. A novel approach of generating phenograms using Jaccard Similary Indices and Hamming Distances of the graphs bit codes are presented. The resulting phenograms are compared with those generated by NCBI gene sequences using Phyllp branch matchings and maximum consensus trees.