Signaling Networks in Biology

Abstract

Signal transduction in cells has been an area of intense study for many years. However, until recently, the focus was on interactions between individual components rather than on the global behavior of the cell signaling networks. New experimental technologies, such as protein and DNA microarrays, high‐throughput screening instrumentation, and diverse compound libraries, allow for many interactions to be examined simultaneously. Along with new experimental methods, quantitative models with mathematical methods, such as deterministic, stochastic, and statistical analyses and graph theory, have been useful in the mapping and analysis of intracellular signaling networks. Here we describe current experimental and computational approaches that are used to develop a global understanding of the complex behavior of intracellular signaling networks. We also discuss important applications of signaling network analysis, including the discovery of new drug targets, the identification of the signaling components responsible for the side (off‐target) effects of drugs, and the development of combination therapies.