The Effect of Gaussian Noise on Maximum Likelihood Fitting of Gompertz and Weibull Mortality Models with Yeast Lifespan Data

Güven, Emine Seda Güvendağ; Akçay, Sevinç; Qin, Hong

doi:10.1080/0361073x.2019.1586105

Cited by 5 publications

(9 citation statements)

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“…This degenerate manifold basically leads to a negative auto-correlation between the two Gompertz parameters along a narrow zone of the iso-average-lifespan curve during numerical fitting of homogeneous populations. We addressed these kinds of potential caveats of numerical fitting in one of our previous studies [11] and in a recent study [40]. Because we are dealing with heterogenous yeast cell populations with diverse genotypes, we think our observed Strehler-Mildvan correlation is not caused by the numerical fitting process.…”

Section: Resultsmentioning

confidence: 99%

Estimating network changes from lifespan measurements using a parsimonious gene network model of cellular aging

Qin

2019

BMC Bioinformatics

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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: t0, the initial virtual age of the network system; n, the average lifespan-influencing interactions per essential node; and R, the initial mortality rate. We applied this model to yeast mutants with known effects on replicative lifespans. We found that deletion of SIR2, FOB1, and HXK2 considerably altered the initial virtual age but not the average lifespan-influencing interactions per essential node, suggesting that these mutations mainly influence the reliability of gene interactions but not the overall configurations of gene networks.We applied this model to investigate replicative lifespans of yeast natural isolates. We estimated that the average number of lifespan-influencing interactions per essential node is 7.0 (6.1–8) and the average estimated initial virtual age is 45.4 (30.6–74) cell divisions in these isolates. We also found that t0 could potentially mediate the observed Strehler-Mildvan correlation in yeast natural isolates.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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Section: Resultsmentioning

confidence: 99%

Estimating network changes from lifespan measurements using a parsimonious gene network model of cellular aging

Qin

2019

BMC Bioinformatics

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“…Of course, there is no known reason why a given dataset in survival analysis should be fit by any certain curve or why a model fitting of a biological population necessarily fit another part of lifespan well. Previous and earlier studies shows that mortality models explores the nature of underlying and extrinsic causes of aging [19,20].…”

Section: Ivdiscussionmentioning

confidence: 99%

A comparison between the performance of Weibull and Log-logistic Aging Models on Saccharomyces cerevisiae lifespan data

Güven

2020

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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Ampirik yaşam veri setleri genellikle yaşlanma için en uygun matematiksel modelle incelenir. Bu çalışmada, dikkatimizi tomurcuklanan maya bakterisi S. cerevisiae ömrüne ve bu bakterilerin en uygun yaşlanma modelinin belirlenmesine verdik. Model seçiminin maya bakterisi ömür veri kümelerindeki etkisini ve iki parametreli Weibull (WE) ve Log-logistic (LL) yaşlanma modellerinin uyum sonuçlarını araştırdık. Bu modellerin her ikisi de yaşlanma araştırmalarında yaygın olarak incelenmekte ve uygulanmaktadır. Bir sağkalım fonksiyonu olarak, zamanla artan ve sonra azalan mortalite oranlarına karşılık gelen benzer bir eğilim gösterirler. Şu ana kadar yapılan çalışmalar genellikle Akdeniz meyve sinekleri, meyve sinekleri, ev sinekleri, un böcekleri ve insanlarin ömür verisi üzerinde bu modellerle çalışmalar yapılmıştır. Önceki araştırmalardan farklı olarak, dikkatimizi sonuçların ve kalibrasyonların ampirik ömür veri örnekleri üzerindeki etkisine odakladik. Beklendiği gibi her iki model de birbirlerinin yerine kullanılabilir. Bununla birlikte, WE modelinin maya ömür verilerine R 2 = 0.86 ile LL modelinden önemli ölçüde daha iyi fit olduğunu gördük. Bu bulgu, tipik olarak hayatta kalma modelleri uygulandığından maya yaşlanma çalışmasında özellikle önemlidir ve bu nedenle hangi modelin maya verilerine daha uygun olduğunu öngörebilir. Bu makalede, karşılaştırmalarla geliştirilen bu yaklaşımın potansiyeli, labaratuvar BY4741 ve BY4742 değişime uğramamış referans suşlarının maya replikatif ömür veri setlerinin model karşılaştırması ile gösterilmiştir. Çalışmamız, deneysel ömürlerin model uyum sonuçlarının yorumlanmasının model seçimini dikkate alması ve sonuçlanan varyasyonu göz önünde bulundurulması gerektiğini vurgulamaktadır.

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“…The Weibull model provides a close approximation of the distribution of the lifetime for an object consisting of many parts in which death occurs when any of its parts fail (Collett, 2015;Rinne, 2009;Sharif & Islam, 1980). This model is widely used in life phenomena, from unicellular organisms (Saccharomyces cerevisiae, Liu & Acar, 2018;Guven et al, 2019) to fungi (Penicillium bilaiae, Friesen et al, 2006), plants (Lemna gibba, Chmilar & Laird, 2019), and F I G U R E 1 A hypothetical mortality rate plotted against age showing a change in mortality rate at different stages (a); the Weibull model of the aging process in mortality plotted against age (b), probability density of life span (c), and survivorship plotted against age (d). In (b), (c), and (d), location parameter "a" is 0; scale parameter "b" is 1; and shape parameter "c" is 1.5, 1, and 0.5 for the blue, red, and green lines, respectively animals (Rhodnius neglectus, Rabinovich et al, 2010;Tribolium confusum, Tanaka et al, 2016;Lepidoptera, Carroll & Sherratt, 2017;tyrannosaurs, Ricklefs, 2007;birds and mammals, Pinder et al, 1978;Ricklefs & Scheuerlein, 2002;Homo sapiens, Gurven & Fenelon, 2009;Hawkes et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…The Weibull model provides a close approximation of the distribution of the lifetime for an object consisting of many parts in which death occurs when any of its parts fail (Collett, 2015; Rinne, 2009; Sharif & Islam, 1980). This model is widely used in life phenomena, from unicellular organisms ( Saccharomyces cerevisiae , Liu & Acar, 2018; Guven et al., 2019) to fungi ( Penicillium bilaiae , Friesen et al., 2006), plants ( Lemna gibba , Chmilar & Laird, 2019), and animals ( Rhodnius neglectus , Rabinovich et al., 2010; Tribolium confusum , Tanaka et al., 2016; Lepidoptera, Carroll & Sherratt, 2017; tyrannosaurs, Ricklefs, 2007; birds and mammals, Pinder et al., 1978; Ricklefs & Scheuerlein, 2002; Homo sapiens , Gurven & Fenelon, 2009; Hawkes et al., 2012). In the model, the change in mortality rate ( m ) is a function of age ( x ):

m (x) = m_{0} + \frac{c}{b} * {()(, \frac{x ‐ a}{b})}^{c ‐ 1}

…”

Section: Introductionmentioning

confidence: 99%

Linking the maximum reported life span to the aging rate in wild birds

Xia

Møller

2021

Ecology and Evolution

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The Effect of Gaussian Noise on Maximum Likelihood Fitting of Gompertz and Weibull Mortality Models with Yeast Lifespan Data

Cited by 5 publications

References 39 publications

Estimating network changes from lifespan measurements using a parsimonious gene network model of cellular aging

Estimating network changes from lifespan measurements using a parsimonious gene network model of cellular aging

A comparison between the performance of Weibull and Log-logistic Aging Models on Saccharomyces cerevisiae lifespan data

Linking the maximum reported life span to the aging rate in wild birds

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