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Identification of longevity genes with systems biology approaches

Authors Tan Y, Bush JM, Liu W, Tang F

Published 27 February 2009 Volume 2009:2 Pages 49—56


Review by Single anonymous peer review

Peer reviewer comments 4

Yuanyou Tan1,3, John M Bush1, Weijiu Liu2, Fusheng Tang1

1Department of Biology, University of Arkansas, Little Rock, AR, USA; 2Department of Mathematics, University of Central Arkansas, Conway, AR, USA; 3Department of Bioengineering, Wuhan University of Science and Engineering, Hubei, China

Abstract: Identification of genes involved in the aging process is critical for understanding the mechanisms of age-dependent diseases such as cancer and diabetes. Measuring the mutant gene lifespan, each missing one gene, is traditionally employed to identify longevity genes. While such screen is impractical for the whole genome due to the time-consuming nature of lifespan assays, it can be achieved by in silico genetic manipulations with systems biology approaches. In this review, we will introduce pilot explorations applying two approaches of systems biology in aging studies. One approach is to predict the role of a specific gene in the aging process by comparing its expression profile and protein–protein interaction pattern with those of known longevity genes (top-down systems biology). The other approach is to construct mathematical models from previous kinetics data and predict how a specific protein contributes to aging and antiaging processes (bottom-up systems biology). These approaches allow researchers to simulate the effect of each gene’s product in aging by in silico genetic manipulations such as deletion or over-expression. Since simulation-based approaches are not as widely used as the other approaches, we will focus our review on this effort in more detail. A combination of hypothesis from data-mining, in silico experimentation from simulations, and wet laboratory validation will make the systematic identification of all longevity genes possible.

Keywords: systems biology, yeast, aging, in silico genetic manipulation, modeling

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