tailieunhanh - Báo cáo khoa học: Prediction of missing enzyme genes in a bacterial metabolic network Reconstruction of the lysine-degradation pathway ofPseudomonas aeruginosa

The metabolic network is an important biological network which consists of enzymes and chemical compounds. However, a large number of meta-bolic pathways remains unknown, and most organism-specific metabolic pathways contain many missing enzymes. We present a novel method to identify the genes coding for missing enzymes using available genomic and chemical information from bacterial genomes. | ễFEBS Journal Prediction of missing enzyme genes in a bacterial metabolic network Reconstruction of the lysine-degradation pathway of Pseudomonas aeruginosa Yoshihiro Yamanishi1 Hisaaki Mihara2 Motoharu Osaki2 Hisashi Muramatsu3 Nobuyoshi Esaki2 Tetsuya Sato1 Yoshiyuki Hizukuri1 Susumu Goto1 and Minoru Kanehisa1 1 Bioinformatics Center Institute for ChemicalResearch Kyoto University Japan 2 Division of EnvironmentalChemistry Institute for ChemicalResearch Kyoto University Japan 3 Department of Biology Graduate Schoolof Science Osaka University Japan Keywords kernel methods lysine degradation pathway metabolic network missing enzymes network inference Correspondence Y. Yamanishi Bioinformatics Center Institute for Chemical Research Kyoto University Gokasho Uji Kyoto 611-0011 Japan Fax 81 774 38 3269 Tel 81 774 38 3270 E-mail yoshi@ Received 6 December 2006 revised 17 February 2007 accepted 1 March 2007 doi The metabolic network is an important biological network which consists of enzymes and chemical compounds. However a large number of metabolic pathways remains unknown and most organism-specific metabolic pathways contain many missing enzymes. We present a novel method to identify the genes coding for missing enzymes using available genomic and chemical information from bacterial genomes. The proposed method consists of two steps a estimation of the functional association between the genes with respect to chromosomal proximity and evolutionary association using supervised network inference and b selection of gene candidates for missing enzymes based on the original candidate score and the chemical reaction information encoded in the EC number. We applied the proposed methods to infer the metabolic network for the bacteria Pseudomonas aeruginosa from two genomic datasets gene position and phylogenetic profiles. Next we predicted several missing enzyme genes to reconstruct the lysinedegradation pathway in P. .