Kyle Serikawa is familiar with the debates on whether transcription measurements accurately reflect protein levels, and he’s weighing in. Serikawa, a research scientist at the University of Washington, contends that studying translation throughout the cell cycle may be a far better way to make predictions.
The process is called translation state array analysis, which uses microarrays to measure mRNA expression levels at various times. Serikawa, who’s collaborating with Ruedi Aebersold’s lab at the Institute for Systems Biology, says there are two main uses for the method. One: “to get a general idea of how well translated all the messages are at any given time point under any given condition,” and two: to compare cells grown under condition A to cells under condition B.
Translation state analysis was originally used on mouse and human genes, but most of Serikawa’s experiments now are on yeast because it’s easier to manipulate. Eventually, he plans to return to mammalian systems.
The drawback of Serikawa’s plan is that he proposes studying RNA at 25 points across the translation cycle, which means 25 times more arrays than are used in basic transcription analyses. But if Serikawa’s right, the improved accuracy that’s expected to come from understanding expression across the cycle will prove its worth in more accurate predictions and understanding of protein expression.
— Marian Moser Jones and Meredith Salisbury