Carnegie Mellon University

Genetic Networking

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From Fruit Flies to Cancer Drugs

Eric P. Xing

A new algorithm developed by Carnegie Mellon University computer scientists has revealed for the first time how genetic networks in the fruit fly evolve during the insect's lifecycle. The discovery could lead to new cancer drug therapy, among other innovations.

Scientists have known that the relationships between fruit fly genes change over time — but existing experimental approaches cannot capture the details of those changes as they occur. The new algorithm, called Tesla, incorporates machine learning techniques. Now researchers are able to figure out how the rewiring of those networks takes place as the insect develops.

"Once we understand the dynamics of a network, we can build models that predict how it will respond to stimuli and identify its vulnerabilities," said Eric P. Xing, associate professor of computer science, machine learning and language technology in Carnegie Mellon's School of Computer Science. "In the context of cancer genetics, for instance, this dynamic network analysis could help us identify new targets for drug therapy."

Explaining the past limitations, Xing noted, "Many problems in biological, social and engineering systems require us to understand the interconnections between genes, people or other entities, but directly observing the evolution of these interconnections has often been impossible because of experimental or computational limitations."

"Researchers typically could identify only a static 'average' network within each system over a period of time, but had no way to capture time-specific 'snapshots' of the actual rewiring network topology at consecutive clock-ticks within the period," added Xing, a member of the Ray and Stephanie Lane Center for Computational Biology at Carnegie Mellon. "Our new method exploits the information sharing between the evolving networks, and makes it possible to uncover interconnections that exist for a short moment in time. These findings help us to understand how these networks evolve over time, respond to stimuli and sometimes become dysfunctional."

For details of the work, read the news release and the researcher's paper in the Early Edition of the Proceedings of the National Academy of Sciences — to be published the week of June 22.

The work was supported by grants from the National Science Foundation, the Defense Advanced Research Projects Agency and the Alfred P. Sloan Foundation.

Related Links: Machine Learning  |  Language Technologies Institute  |  National Science Foundation  |  DARPA  |  Alfred P. Sloan Foundation