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Computational Method Speeds Mapping of Cell Signaling Networks

inside the cell. But the molecules that make up individual pathways in signaling networks rarely operate in isolation--they "talk" to each other and work in sync to process information. What researchers have lacked are the experimental and computing tools to model entire signaling networks at once. This new work should help bridge that gap.

Human T-cells--the system Nolan and Lauffenburger's team used to validate their technique--rely on a complex signaling network to protect the body from pathogens such as viruses. When a T-cell encounters a foreign invader, it relays a danger signal throughout the cell, activating it for a quick response. These signals prompt activated T-cells to multiply rapidly and send out molecular "distress signals" that attract other immune system molecules to battle the infection. Components in the pathway are chemically altered during the signaling.

Such alterations occur only at points of active signaling in the network. The researchers measured the modified signaling molecules to determine where along the pathway T-cell components changed in response to signals. An increase in the amount of the molecules indicated spots where they were most active.

Information about the simultaneous activities of several signaling molecules in thousands of individual cells was analyzed using a powerful computational method called Bayesian network modeling. Bayesian analysis uses statistics to determine associations and dependencies between molecules in a network. With sufficient experimental data, for example, a Bayesian network analysis can not only identify an association between signal molecules A and B, it can detect whether B depends on A for transfer of the signal.

The team's analysis predicted all but three of 18 well-studied T-cell network relationships. Although the work was largely a "proof of principle" report designed to demonstrate the usefulness of their method, their studies also identified a previously unk
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Source:NSF


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