Carnegie Mellon algorithm charts evolution of genetic networks during fruit fly ...( PITTSBURGHA new algorithm developed ...),Health

PITTSBURGHA new algorithm developed by Carnegie Mellon University computer scientists has revealed for the first time how genetic networks in the fruit fly, Drosophila melanogaster, evolve during the insect's life cycle.

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

"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," said Eric P. Xing, associate professor of computer science, machine learning and language technology in Carnegie Mellon's School of Computer Science. "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.

"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," Xing said. "These findings help us to understand how these networks evolve over time, respond to stimuli and sometimes become dysfunctional."

In a paper to be published online this week in the Early Edition of the Proceedings of the National Academy of Sciences, Xing and Amr Ahmed, a Ph.D. student in the Language Technology Institute, detail how the Tesla algorithm can be used to analyze not only evolving gene networks in fruit flies, but also changes in voting alliances in the U.S. Senate and shifts in the
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