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Identification of Information Flow‐Modulating Drug Targets: A Novel Bridging Paradigm for Drug Discovery
Author(s) -
Hwang WC,
Zhang A,
Ramanathan M
Publication year - 2008
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1038/clpt.2008.129
Subject(s) - bridging (networking) , betweenness centrality , centrality , computational biology , biological network , drug discovery , computer science , identification (biology) , biology , bioinformatics , mathematics , computer network , botany , combinatorics
Our objective in this study was to identify novel metrics for efficient identification of drug targets using biological network topology data. We developed a novel paradigm and metric, namely, bridging centrality, capable of identifying nodes critically involved in connecting or bridging modular subregions of a network. The topological and biological characteristics of bridging nodes were delineated in a diverse group of published yeast networks and in three human networks: those involved in cardiac arrest, C21‐steroid hormone biosynthesis, and steroid biosynthesis. The bridging centrality metric was highly selective for bridging nodes. Bridging nodes differed distinctively from nodes with high degree and betweenness centrality. Bridging nodes had lower lethality, and their gene expression was consistent with independent regulation. Analysis of biological correlates indicated that bridging nodes are promising drug targets from the standpoints of efficacy and side effects. The bridging centrality method is a promising computational systems biology tool to aid target identification in drug discovery. Clinical Pharmacology & Therapeutics (2008); 84 , 5, 563–572 doi: 10.1038/clpt.2008.129