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Approaches to the analysis of cell signaling networks and their application in drug discovery.

The ability to predict the safety and efficacy of novel drugs prior to clinical testing is a key goal in pharmaceutical drug discovery. Gaining a mechanistic understanding of the complex cell signaling networks (CSNs) underlying disease processes promises to help reduce the number of clinical failures by identifying points of intervention as well as redundancies and feedback mechanisms that contribute to toxicities, lack of efficacy and unexpected biological activities. Experimental and computational approaches to analyzing and modeling CSNs are currently being validated using simple organisms and cell lines. In vitro cell systems of sufficient complexity to resemble human disease physiology, but which are also amenable to chemical and genetic perturbations on a large scale, are now required for deciphering the signaling networks operating in human disease. In this review, experimental and computational methods for modeling complex CSNs and the applications of these approaches to pharmaceutical drug discovery are discussed.