Social network algorithm helps identify protein 'communities'

Only in biotechnology can a small, flowering plant bring together cell biologists with a mathematical algorithm for studying social networks to develop an approach to improve crops and treat cancer. Here's what they all have in common. First, the flowering plant is Arabidopsis thaliana, or the mouse-ear cress, which has a rather small genome that makes it a popular plant for biologists to study. Then there is Northeastern University's Albert-László Barabási, a bioinformatics pioneer who has come up with a mathematical algorithm to identify communities in complex networks, including major biological networks and large-scale social networks.

Barabási applied his algorithm to Arabidopsis thaliana, combing through about 6,200 highly reliable interactions between about 2,700 proteins, and came up with about two dozen communities of interconnected proteins that share in the same biological function. The findings give researchers a peak inside evolution's machinery within networks of plant proteins. "The communities were not random and each had a dominant function that did not emerge by chance," Barabási said in a news release.

The research more than simply satisfies curiosity about a plant, though. Learning about these protein "communities" also gives us a peek into how to treat human diseases such as cancer, the release said. "Creating this map is a significant building block to understanding plants in general and learning more about the biological similarities between plants and animals," Barabási said in a statement.

Also contributing to the research were three postdocs and researchers at Harvard Medical School, the Dana-Farber Cancer Institute, the Salk Institute for Biological Studies, the United States Department of Agriculture and the Department of Computing at Imperial College in London.

- read the report from Northeastern University
- and the abstract in Science

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