Researchers develop Computer algorithm which identifies genes that could be turned-off to stop the aging process

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Posted on January 6, 2014

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Researcher Keren Yizhak and her colleagues at Prof. Eytan Ruppin’s lab at Tel Aviv University’s Blavatnik School of Computer Science has developed a computer algorithm that predicts which genes can be “turned off” to create the same anti-aging effect as calorie restriction. Researchers from Bar-Ilan University collaborated on the research. Using mathematical equations and computers, GSMMs describe the metabolism, or life-sustaining, processes of living cells. Once built, the individual models serve as digital laboratories, allowing formerly labor-intensive tests to be conducted with the click of a mouse. Yizhak’s algorithm, which she calls a “metabolic transformation algorithm,” or MTA, can take information about any two metabolic states and predict the environmental or genetic changes required to go from one state to the other.

In the study, Yizhak applied MTA to the genetics of aging. After using her custom-designed MTA to confirm previous laboratory findings, she used it to predict genes that can be turned off to make the gene expression of old yeast look like that of young yeast. Yeast is the most widely used genetic model because much of its DNA is preserved in humans.

Some of the genes that the MTA identified were already known to extend the lifespan of yeast when turned off. While the other genes she found, was sent to be tested at a Bar-Ilan University laboratory. Researchers there found that turning off two of the genes, GRE3 and ADH2, in actual, non-digital yeast significantly extends the yeast’s lifespan.

Since MTA provides a systemic view of cell metabolism, it can also shed light on how the genes it identifies contribute to changes in genetic expression. In the case of GRE3 and ADH2, MTA showed that turning off the genes increased oxidative stress levels in yeast, thus possibly inducing a mild stress similar to that produced by calorie restriction.

As a final test, Yizhak applied MTA to human metabolic information. MTA was able to identify a set of genes that can transform 40-to-70 percent of the differences between the old and young information from four different studies. While currently there is no way to verify the results in humans, many of these genes are known to extend lifespan in yeast, worms, and mice.

Their findings are reported in Nature Communications.

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