%0 Journal Article
%T Signals and systems
%A Nevan J Krogan
%A Timothy R Hughes
%J Genome Biology
%D 2006
%I BioMed Central
%R 10.1186/gb-2006-7-4-313
%X The boundaries between traditional notions of cellular signaling and more genomic and systematic approaches to biology are becoming increasingly blurred, and the recent Keystone conference on signaling networks reflected an expanded view of signaling. It is becoming increasingly accepted that genes, proteins, cells, and organisms function as components of larger systems, rather than independent activities contributing to a single defined outcome, and many presentations at the conference reflected this. If there was a single theme, it was the heavy reliance on technical approaches in functional genomics, proteomics, and computational biology, such that conceptual and technical discussions often dominated the resulting biology.The impact of technology on the study of signaling networks was most evident in the widespread application of RNA interference (RNAi) screens. Screens to study signaling networks in multicellular organisms using RNAi technology are being performed with success, although several hurdles clearly remain. One of these is the apparently high rate of false positives. In his keynote lecture, Norbert Perrimon (Harvard Medical School, Boston, USA) predicted an uncomfortably high rate of false positives due to off-target effects, at least in Drosophila. Consistent with this, Phil Beachy (Johns Hopkins University School of Medicine, Baltimore, USA) described an ongoing RNAi screen in Drosophila looking for proteins involved in the Wingless signaling pathway. Following efforts to study a previously uncharacterized gene identified by this screen, it was noted that there were 16 bases in the interfering RNA that were identical to armadillo, a known gene in the pathway, suggesting that it is an unanticipated off-target that could completely explain the phenotype conferred by the interfering RNA. Perrimon proposed that incorporating other types of data, such as protein-protein interaction information, can help decipher which hits are physiologically relevant. R
%U http://genomebiology.com/2006/7/4/313