Background: Cellular aging is best studied in the budding yeast Saccharomyces cerevisiae. As an example of a\npleiotropic trait, yeast lifespan is influenced by hundreds of interconnected genes. However, no quantitative methods\nare currently available to infer system-level changes in gene networks during cellular aging.\nResults: We propose a parsimonious mathematical model of cellular aging based on stochastic gene interaction\nnetworks. This network model is made of only non-aging components: the strength of gene interactions declines\nwith a constant mortality rate. Death of a cell occurs in the model when an essential node loses all of its interactions\nwith other nodes, and is equivalent to the deletion of an essential gene. Stochasticity of gene interactions is modeled\nusing a binomial distribution. We show that the exponential increase of mortality rate over time can emerge from this\ngene network model during the early stages of aging.\nWe developed a maximal likelihood approach to estimate three lifespan-influencing network parameters from\nexperimental lifespans: t0, the initial virtual age of the network system; n, the average lifespan-influencing interactions\nper essential node; and R, the initial mortality rate. We applied this model to yeast mutants with known effects on\nreplicative lifespans. We found that deletion of SIR2, FOB1, and HXK2 considerably altered the initial virtual age but not\nthe average lifespan-influencing interactions per essential node, suggesting that these mutations mainly influence the\nreliability of gene interactions but not the overall configurations of gene networks.\nWe applied this model to investigate replicative lifespans of yeast natural isolates. We estimated that the average\nnumber of lifespan-influencing interactions per essential node is 7.0 (6.1-8) and the average estimated initial virtual\nage is 45.4 (30.6-74 ) cell divisions in these isolates. We also found that t0 could potentially mediate the observed\nStrehler-Mildvan correlation in yeast natural isolates.\nConclusions: Our theoretical model provides a parsimonious interpretation of experimental lifespan data from the\nperspective of gene networks. We hope that our work will stimulate more interest in developing network models to\nstudy aging as a pleiotropic trait.
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