In this combined analysis, both data types are integrated with molecular interactions data into a diagram that connects the experimentally identified proteins and genes. While this typically results in an extraordinarily complicated diagram, sometimes jokingly referred to as a "hairball", ResponseNet is designed to whittle the hairball down to the most probable pathways connecting various genes and proteins.
Esti Yeger-Lotem, a postdoctoral researcher in the laboratories of Whitehead Member Susan Lindquist and of Ernest Fraenkel at MIT's Biological Engineering department and co-author of the Nature Genetics article, says that by analyzing those probable pathways, a systems view of the cellular response emerges. "This allows for a more complete understanding of cellular response and can reveal hidden components of the response that may be targeted by drugs," she says.
According to Laura Riva, a postdoctoral researcher in MIT's biological engineering department and one of the designers of the algorithm, ResponseNet is potentially very useful for researchers.
"It is a powerful approach for interpreting experimental data because it can efficiently analyze tens of thousands of nodes and interactions," says Riva, who is also a co-author on the article. "The output of ResponseNet is a sparse network connecting some of the genetic data to some of the transcriptional data via intermediate proteins. Biologists can look at the network and understand which pathways are perturbed, and they can use it to generate testable hypotheses."
To demonstrate ResponseNet's capabilities, Yeger-Lotem entered the data from screens of
|Contact: Nicole Giese|
Whitehead Institute for Biomedical Research