© 2005 AAAS
Schematic of Bayesian network inference using multidimensional flow cytometry data. Bayesian network analysis of the data from flow cytometry of 11 phosphoproteins and phospholipids in individual cells extracts an influence diagram reflecting the causal relationship between signaling network molecules.
Researchers have spent decades determining how proteins interact with each other in complex signaling networks by studying these relationships one at a time in isolation. This approach may have been necessary, but the resulting maps are accordingly suspect. "The average signaling map is a composite of data from everything from nematodes to yeast ... and although we draw an arrow between two molecules, whether it works in any given cell type and the strength, and timing and conditions are very contextual," says Stanford University researcher Garry Nolan.
Using a largely computational approach, Nolan and colleagues at the Massachusetts Institute of Technology quickly and successfully reverse-engineered a T-cell signaling ...
