Scientists have upset gene expression to investigate its randomness and discover how the cell reduces this variability.
 
An organism's genetic code does not simply equate to a certain outcome. Noise in gene expression can result in physical differences in genetically identical populations. Synthetic biologists who want to construct and study gene networks need to understand this noise for their own experiments to be valid, and new work from America explores just that.

Variations in the amounts of different RNA molecules in cells can affect protein production © National Science Foundation

In gene expression, genes are read and translated into protein products using small RNA molecules overseen by a large complex called a ribosome. Because there are so many of these small RNAs in a cell, variations in their relative levels can affect protein production. Andrew Ellington, at the University of Texas at Austin, US, and colleagues decided to investigate this phenomenon further.

The group made small ribosome competing RNA (rcRNA) molecules that were designed to compete with cell RNA for the ribosome and affect gene expression. Their aim was to use the rcRNAs as a tool in gene expression noise studies to introduce noise controllably using different amounts and types of rcRNA.

When the researchers added the rcRNAs to Escherichia coli cells they found that their rcRNAs do generate noise, causing fluctuations in the production of a fluorescent protein by the bacteria. The team used its rcRNA approach to show that DNA sections called operons are highly effective at reducing noise as they eliminate the relative RNA fluctuations between genes.

Jim Collins, co-director of the Centre for BioDynamics at Boston University, US, is very impressed with the new tool. He describes the work as 'an excellent example of how synthetic biology techniques can be used to gain insight into fundamental biological principles.' 

Laura Howes