Biologists in recent years have identified every individual gene in the genomes of several organisms. While this has been quite an accomplishment in itself, the further goal of figuring out how these genes interact is truly daunting.
The difficulty lies in the fact that two genes can pair up in a gigantic number of ways. If an organism has a genome of 20,000 genes, for example, the total number of pairwise combinations is a staggering total of 200 million possible interactions.
Researchers can indeed perform experiments to see what happens when the two genes interact, but 200 million is an enormous number of experiments, says Weiwei Zhong, a postdoctoral scholar at the California Institute of Technology. “The question is whether we can prioritize which experiments we should do in order to save a lot of time.”
To get at this issue, Zhong and her supervising professor, Paul Sternberg, have derived a method of database-mining to make predictions about genetic interactions. In the current issue of the journal Science, they report on a procedure for computationally integrating several sources of data from several organisms to study the tiny worm C. elegans, or nematode, an animal commonly used in biological experiments.
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