ArticleBMC bioinformatics2019
Estimating network changes from lifespan measurements using a parsimonious gene network model of cellular aging.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- A comprehensive review of artificial intelligence as a catalyst in aging research: insights, gaps and future perspectives.Frontiers in aging · 2026Review
- Computational Systems Biology Approaches to Cellular Aging - Integrating Network Maps and Dynamical Models.Quantitative biology (Beijing, China) · 2025Article
- Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification.Frontiers in genetics · 2023Article
- Modeling aging and its impact on cellular function and organismal behavior.Experimental gerontology · 2021Review
- Protein interaction potential landscapes for yeast replicative aging.Scientific reports · 2021Article
- Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.PloS one · 2021Article
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1 author.
Funding
Abstract
backgroundCellular aging is best studied in the budding yeast Saccharomyces cerevisiae. As an example of a pleiotropic trait, yeast lifespan is influenced by hundreds of interconnected genes. However, no quantitative methods are currently available to infer system-level changes in gene networks during cellular aging.
resultsWe propose a parsimonious mathematical model of cellular aging based on stochastic gene interaction networks. This network model is made of only non-aging components: the strength of gene interactions declines with a constant mortality rate. Death of a cell occurs in the model when an essential node loses all of its interactions with other nodes, and is equivalent to the deletion of an essential gene. Stochasticity of gene interactions is modeled using a binomial distribution. We show that the exponential increase of mortality rate over time can emerge from this gene network model during the early stages of aging.We developed a maximal likelihood approach to estimate three lifespan-influencing network parameters from experimental lifespans: t
conclusionsOur theoretical model provides a parsimonious interpretation of experimental lifespan data from the perspective of gene networks. We hope that our work will stimulate more interest in developing network models to study aging as a pleiotropic trait.
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