While calorie restriction has been shown to combat aging in everything from humans to flies, the mechanism behind this has remained mysterious. But in a new study published in the journal Nature, a team of researchers from Tel Aviv University have developed a computer algorithm capable of predicting the genes that can be switched off in order to create the same anti-aging effect as calorie restriction.

"Most algorithms try to find drug targets that kill cells to treat cancer or bacterial infections," co-author and doctoral student Karen Yizhak said. "Our algorithm is the first in our field to look for drug targets not to kill cells, but to transform them from a diseased state into a healthy one."

Yizhak is a student in the lab of of Eytan Ruppin where researchers have been busy combining mathematical equations and computers to create genome-scale metabolic models, or GSMMs. Each model acts as a digital lab where scientists are able to carry out previously labor-intensive tests with the click of a mouse.

The algorithm Yizhak developed takes information about any two metabolic states and predicts what environmental or genetic changes it will take to make it go from one state to another. Applying it to the genetics of aging, she predicted the genes that can be turned off to make old yeast's gene expression look similar to that of young yeast. Some of the genes the algorithm suggested are already known to extend yeast's lifespan when switched off, while seven of the others were sent away to be tested. So far, scientists have found that switching off two of them - GRE3 and ADH2 - greatly extends the lifespan of yeast.

"You would expect about three percent of yeast's genes to be lifespan-extending," Yizhak said. "So achieving a 10-fold increase over this expected frequency, as we did, is very encouraging."

Turning off GRE3 and ADH2, the algorithm showed, increased oxidative stress levels in the yeast, which Yizhak hypothesizes may produce a mild stress much like the one created by calorie restriction. 

Applying the algorithm to human metabolic data, Yizhak found a set of genes capable of transforming between 40-70 percent of the changes between old and young information from four studies. And while it is currently impossible to verify the results in humans, many of the genes identified are already known to extend the lifespan of not only yeast, but worms and mice, too.