Mammalian cell signaling and protein interactions extracted from research literature or identified using high-throughput experiments can be integrated and analyzed by considering such interactions as networks made of nodes and links. Topology analysis of such networks in combination with advanced experimental techniques that measure many cellular components at once can be used to make predictions about gaps in our current knowledge, and to predict undiscovered functional roles for components and pathways. This approach has been successful for quality assessment of pre-synaptic proteomics data, and it enabled the discovery of new pathways and components in cannabinoid induced neurite outgrowth using transcription-factor arrays in combination with pharmacological inhibitors and RNAi. Combining biological networks as background knowledge to analyze multivariate experiments using graph-theory algorithms that place nodes in context of background knowledge can rapidly enhance our understanding of the detailed workings of mammalian cells.
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