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Chromatin instability and mitochondrial decline are conserved processes that contribute to cellular aging. Although both processes have been explored individually in the context of their distinct signaling pathways, the mechanism that determines which process dominates during aging of individual cells is unknown. We show that interactions between the chromatin silencing and mitochondrial pathways lead to an epigenetic landscape of yeast replicative aging with multiple equilibrium states that represent different types of terminal states of aging. The structure of the landscape drives single-cell differentiation toward one of these states during aging, whereby the fate is determined quite early and is insensitive to intracellular noise. Guided by a quantitative model of the aging landscape, we genetically engineered a long-lived equilibrium state characterized by an extended life span.
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Fitmix: An R Package for Mixture Modeling of the Budding Yeast S. cerevisiae Replicative Lifespan (RLS) Distributions
Replicative lifespan (RLS) of the budding yeast is the number of mother cell divisions until senescence and is instrumental to understanding mechanisms of cellular aging. Recent research has shown that replicative aging is heterogeneous, which argues for mixture modeling. The mixture model is a statistical method to infer subpopulations of the heterogeneous population. Mixture modeling is a relatively underdeveloped area in the study of cellular aging. There is no open access software currently available that assists extensive comparison among mixture modeling methods. To address these needs, we developed an R package called fitmix that facilitates the computation of well-known distributions utilized for RLS data and other lifetime datasets. This package can generate a group of functions for the estimation of probability distributions and simulation of random observations from well-known finite mixture models including Gompertz, Log-logistic, Log-normal, and Weibull models. To estimate and compute the maximum likelihood estimates of the model parameters, the Expectation–Maximization (EM) algorithm is employed.
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- PAR ID:
- 10293611
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 11
- Issue:
- 13
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 6114
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app11136114
