Identification of information flow-modulating drug targets: a novel bridging paradigm for drug discovery.
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.
Hwang
W-C
WC
Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, New York, USA.
Zhang
A
A
Ramanathan
M
M
D019295:Computational Biology; D020869:Gene Expression Profiling; D006801:Humans; D008954:Models, Biological; D008967:Molecular Biology