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Cited by 15Evolution and Genetic DynamicsCarcinogens and Genotoxicity AssessmentProbability and Statistical Research

A note on the evaluation of fluctuation experiments.

Angerer, Wolfgang P.·2001·Mutation research. Fundamental and molecular mechanisms of mutagenesis
DOI 10.1016/s0027-5107(01)00203-2
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Abstract

Fluctuation analysis has emerged as a valuable tool for the measurement of mutation rates in single-cell populations. In this paper, we show how to make fuller use of the information supplied by the outcome of a fluctuation experiment. We shall extend Lea and Coulson's theory of the Luria-Delbrück distribution so that it accounts for residual mutation, reduced plating efficiency of mutants, and phenotypic lag, and establish a unifying method for the evaluation of fluctuation experiments in these cases and discuss its limitations. It will be proved that not all factors that might influence the distribution of mutant colonies in a fluctuation experiment can, in effect, be determined simultaneously. Nevertheless, it will be shown that the fluctuation-analytic approach to the measurement of mutation rates may retain its value in comparison with (or may even be superior to) alternative methods. Finally, we give some numerical examples to illustrate our results.

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Crossref · 10.1016/s0027-5107(01)00203-2

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38 of 38 references verified0 citation errors · 0 not checkable

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  • Angerer (2001) was perhaps the first to document computational difficulties that were due to practical nonidentifiability.

    “Angerer (2001) was perhaps the first to document computational difficulties that were due to practical nonidentifiability.”

    From rSalvador: An R Package for the Fluctuation Experiment (2017) · cites this paper as Angerer (2001)

  • The simulation algorithm that is a part of mlemur combines previously used approaches with the novel addition of phenotypic lag.

    “Simulation algorithm that is a part of mlemur combines previously used approaches with the novel addition of phenotypic lag [38,39,46].”

    From Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur) (2023) · cites this paper as [38]

  • Even d as big as 0.95 gives CV = 0.197, within the 20% safety threshold, indicating that the variability might be controlled so long as inoculum is sufficiently big.

    “Even d as big as 0.95 gives CV = 0.197, within the 20% safety threshold [38], indicating that the variability might be controlled so long as inoculum is sufficiently big.”

    From Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur) (2023) · cites this paper as [38]

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Authors
Angerer, W. P.
Affiliations
Ludwig Boltzmann Institute for Cancer Research (AT)
Corresponding author
Angerer, Wolfgang P.
Venue
Mutation research. Fundamental and molecular mechanisms of mutagenesis
Publisher
Elsevier BV
Published
2001-08
Volume
479
Issue
1-2
Pages
207-224
ISSN
  • 0027-5107 (ISSN-L)
  • 1386-1964
DOI
10.1016/s0027-5107(01)00203-2
OpenAlex
W2085617239
Cited by
15
References
37
Citations by year
2025: 2 · 2023: 2 · 2022: 1 · 2017: 2 · 2013: 1
FWCI
0.18
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Mutation research. Fundamental and molecular mechanisms of mutagenesis (publishedVersion)
  • PubMed (publishedVersion)
Funders
Austrian Science Fund (P14682-MAT, P8530-MOB)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Carcinogens and Genotoxicity Assessment
  • Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
Keywords
fluctuation analysis, mutation rate estimation, plating efficiency
MeSH terms
Models, Theoretical, Mutagenicity Tests, Mutation, Phenotype, Research Design, Models, Statistical, Likelihood Functions

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