New algorithm charts fruit fly genetics

U.S. scientists say they’ve created a new algorithm that reveals how genetic networks in the fruit fly, Drosophila melanogaster, evolve.

Scientists have known the relationships between fruit fly genes change over time, but existing experimental approaches can’t capture the details of those changes as they occur, the researchers said. The new algorithm, developed by Carnegie Mellon University scientists, incorporates machine learning techniques that enable researchers to figure out how the rewiring of the 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 Associate Professor Eric Xing. “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.

The research that included Amr Ahmed appeared online in last week’s early edition of the Proceedings of the National Academy of Sciences.