Prediction is the great strength of Bayesian network analysis--particularly in the post-genomic age when researchers are generating vast amounts of data. The statistical approach requires no prior knowledge of signaling pathways to uncover molecular relationships. This is a distinct advantage according to Pe'er, who notes, "Unlike classical biochemical approaches where you need to know all of the intermediate players in a network, this system can detect indirect influences" between molecules.
Pe'er suggests a similar technique could be used to study the differences between normal and abnormal cells. This application would be particularly useful for understanding disease states where signaling malfunction plays a prominent role, including cancer and autoimmunity.
Until now, the large amounts of data the technique requires prevented its application to protein networks. Being able to monitor multiple components in thousands of cells at once was pivotal to the group’s success. Despite technical hurdles, the team believes similar approaches may be used in the future to study systems that involve multiple types of cells and their interactions, such as tissues and organs.
NSF program manager Carter Kimsey thinks this study will open up a wide range of research opportunities. "There is no reason to think that this mathematical tool is for health-related fields only, there are many possible applications in biology," she says.
Dana Pe'er is a recipient of a National Science Foundation postdoctoral research fellowship award in biological informatics. Several organizations supported the work, including the National Institutes of Health, Bristol-Meyer Squibb and the Juvenile Diabetes Foundation.