A second look at the final number of cells in a fluctuation experiment
Abstract
In a fluctuation experiment, the number of cells existing in a culture immediately before plating (commonly known as Nt) varies across the parallel cultures. However, most existing mathematical models for fluctuation assay data do not recognize the variation in Nt. Despite repeated attempts in the past to integrate this source of variability in the estimation of microbial mutation rates, several questions of practical importance remain unanswered. The present investigation finds that the variation needs accounting for only when the coefficient of variation for Nt is large, and experimental data suggest that the coefficient of variation is often moderate or small. Moreover, an increase in the inoculum size can reduce the coefficient of variation. Through extensive simulation, several existing methods that accommodate the variation in Nt are compared. It was found that a newly devised likelihood method based on the existing gamma mixture model outperforms other existing methods. The investigation focuses on the estimation of mutation rates using the Lea-Coulson model, under which mutation is selectively neutral; however, the paper also explores the major findings' implications for the comparison of mutation rates using the likelihood ratio test, and for the estimation of mutation rates using the Mandelbrot-Koch model that allows for non-neutral mutations.
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The dynamics of mutant cells can be modelled by a simple birth-and-death process.
“The dynamics of the mutant cells can be modelled by a simple birth-and-death process, as previously suggested [34,41,42,64,65].”
From Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur) (2023) · cites this paper as [41]
Cites
Improving accuracy of cell and chromophore concentration measurements using optical density
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Analysis of Gross‐Chromosomal Rearrangements in Saccharomyces cerevisiae
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Fluctuation analysis of mutations to nalidixic acid resistance in Escherichia coli.
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A new discrete distribution induced by the Luria-Delbrück mutation model.
Zheng, Q. (2010) · Statistics · doi:10.1080/02331880903236868 · Cited by 5 · see citing works
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rSalvador: An R Package for the Fluctuation Experiment
Zheng, Q. (2017) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.117.300120 · Cited by 98 · see citing works
“If the CV for N t in an experiment is C , then, under additional mild assumptions, the unconditional distribution of the number of mutants is a B 0 ð C 2 m 0 ; C ð 2 2 Þ Þ distribution (Zheng 2016b).”
BackgroundMethodSelf-citationŁazowski, K. (2023) · Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis · doi:10.1016/j.mrfmmm.2023.111816 · Cited by 10 · see citing works
- Authors
- Zheng, Q.
- Affiliations
- Texas A&M Health Science Center (US)
- Corresponding author
- Zheng, Qi
- ORCID
- Zheng, Qi
- Venue
- Journal of Theoretical Biology
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 401
- Pages
- 54-63
- ISSN
- 0022-5193 (ISSN-L)
- OpenAlex
- W2337112531
- Cited by
- 10
- References
- 5
- Citations by year
- 2025: 1 · 2023: 2 · 2022: 1 · 2018: 3 · 2017: 3
- FWCI
- 1.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Theoretical Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- gamma mixture model, likelihood ratio test, inoculum size, coefficient of variation, selective neutrality
- MeSH terms
- Microbiological Techniques, Models, Biological, Models, Genetic, Bacterial Load, Mutation Rate
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