Scientists have now developed a new computer algorithm that may help
predict which genes should be turned off to create an anti-ageing effect
similar to calorie restriction.
Restricting calorie consumption is one of the few proven ways to combat
aging. Though the underlying mechanism is unknown, calorie restriction
has been shown to prolong lifespan in yeast, worms, flies, monkeys, and,
in some studies, humans.
The study by Keren Yizhak from Tel Aviv University's Blavatnik School of Computer Science, and her colleagues, could lead to the development of new drugs to treat aging.
"Most algorithms try to find drug targets that kill cells to treat cancer or bacterial infections," says Yizhak. "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'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.
"Gene expression" is the measurement of the expression level of individual genes in a cell, and genes can be "turned off" in various ways to prevent them from being expressed in the cell.
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.
It was found that silencing 'aging gene' leads to 10-fold increase in lifespan for yeast population.
The study is published in journal Nature Communications.
Restricting calorie consumption is one of the few proven ways to combat
aging. Though the underlying mechanism is unknown, calorie restriction
has been shown to prolong lifespan in yeast, worms, flies, monkeys, and,
in some studies, humans. The study by Keren Yizhak from Tel Aviv University's Blavatnik School of Computer Science, and her colleagues, could lead to the development of new drugs to treat aging.
"Most algorithms try to find drug targets that kill cells to treat cancer or bacterial infections," says Yizhak. "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'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.
"Gene expression" is the measurement of the expression level of individual genes in a cell, and genes can be "turned off" in various ways to prevent them from being expressed in the cell.
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.
It was found that silencing 'aging gene' leads to 10-fold increase in lifespan for yeast population.
The study is published in journal Nature Communications.
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