Treasure Trove of Data Yields Rich Resource for Synthetic Biologists

Achievement date: 
2014
Outcome/accomplishment: 

Researchers have devised a new method to discover elements in the regulation of gene expression--an essential feature of all living organisms and viruses--in random settings. With support by the Synthetic Biology Engineering Research Center (SynBERC), an NSF-funded center headquartered at the University of California at Berkeley, the investigators determined how to characterize common regulatory elements by reviewing a vast library of scientific information. The resulting treasure trove of data provides a resource for other researchers seeking to achieve particular expression levels.

Impact/benefits: 

Using standardized elements or prediction-based design often fails to yield regulatory components that function in the same way under different conditions or in combinations. This is a potential problem for synthetic biologists, where multi-component circuits and pathways at the cellular level are necessary to generate products, many of which are superior to non-biological alternatives. The ease and scale of the SynBERC researchers’ approach indicates that screening synthetic biology data libraries for desired behavior is much better rather than the usual practice of relying on prediction or standardization.

Explanation/Background: 

The researchers took a comprehensive look at the behavior of a library of 12,563 combinations of common regulatory elements and simultaneously measured DNA, RNA, and protein levels from the entire library. They then quantified how often-simple measures of promoter and ribosomal binding site (RBS) strengths can accurately predict gene expression when used in combination. These results bring new meaning to Yogi Berra’s quote, “You can observe a lot just by watching.”