Empirical lifespan datasets are often studied with the best-fitted mathematical model for aging. In this study, we focus our attention to the budding yeast S. cerevisiae lifespan and the determination of the best-fitted model of aging. We investigate the influence of model selection in yeast lifespan datasets and the fitting outcomes of the two-parameter Weibull (WE) and Log-logistic (LL) models of aging. Both of these models are commonly studied and implemented in aging research. They show similar tendency as a survival function that they correspond to mortality rates that increase, and then decrease, with time. Studies so far has been usually done with medflies, Drosophila, house flies, flour beetles, and humans with these models. Different than previous research, we focus our attention on the influence of fitting results and calibrations on empirical lifespan data samples. As expected both of the models could be used as a substitute of each other. However, we also find WE model fits the yeast lifespan data significantly better than LL model with an R2 = 0.86. This finding is especially important in yeast aging study because of typically survival models are applied and therefore one can see which model fits the yeast data best. In this article, comparisons are done and developed and the potential of the approach is demonstrated with a model comparison of yeast replicative lifespan datasets of the laboratory BY4741 and BY4742 wildtype reference strains. Our study highlights that interpreting model fitting results of experimental lifespans should take model selection and resulted variation into account.
: 2 Ağustos 2019
|APA||GÜVEN, E . (2020). A comparison between the performance of Weibull and Log-logistic Aging Models on Saccharomyces cerevisiae lifespan data. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi , 7 (100. Yıl Özel Sayı) , 123-132 . DOI: 10.35193/bseufbd.600693|