A Simple Formula for Obtaining Markedly Improved Mutation Rate Estimates
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In previous work by M. E. Jones and colleagues, it was shown that mutation rate estimates can be improved and corresponding confidence intervals tightened by following a very easy modification of the standard fluctuation assay: cultures are grown to a larger-than-usual final density, and mutants are screened for in only a fraction of the culture. Surprisingly, this very promising development has received limited attention, perhaps because there has been no efficient way to generate the predicted mutant distribution to obtain non-moment-based estimates of the mutation rate. Here, the improved fluctuation assay discovered by Jones and colleagues is made amenable to quantile-based, likelihood, and other Bayesian methods by a simple recursion formula that efficiently generates the entire mutant distribution after growth and dilution. This formula makes possible a further protocol improvement: grow cultures as large as is experimentally possible and severely dilute before plating to obtain easily countable numbers of mutants. A preliminary look at likelihood surfaces suggests that this easy protocol adjustment gives markedly improved mutation rate estimates and confidence intervals.
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The simplest way to model phenotypic lag, considered by Armitage and further developed by Angerer, is to assume that a certain amount of time since the mutational event must pass for the cell to express the mutant phenotype.
“The simplest way to model phenotypic lag, which was considered by Armitage and further developed by Angerer, is to assume that a certain amount of time (say, t lag ) since mutational event must pass for the cell to be able to express mutant phenotype [35].”
From Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur) (2023) · cites this paper as [35]
The Abstract discusses when mutations occur and whether mutants form observable colonies; the Discussion lists simplifying assumptions of the model. Neither passage addresses a fixed delay before mutant phenotype expression or attributes such a model to Armitage and Angerer (searched: phenotypic lag, Angerer, delayed expression). (LLM, conf=0.74)
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Sarkar, S., Ma, W. T., & Sandri, G. V. H. (1992) · Genetica · doi:10.1007/bf00120324 · Cited by 181 · see citing works
Analysis of the Luria–Delbrück distribution using discrete convolution powers
Ma, W. T., Sandri, G. V., & Sarkar, S. (1992) · Journal of Applied Probability · doi:10.1017/s0021900200043023 · Cited by 121 · see citing works
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- Bailey, N. T. J., 1964 The Elements of Stochastic Processes With Applica- tions to the Natural Sciences. Wiley, New York.
- Bartlett, M. S., 1978 An Introduction to Stochastic Processes. Cam- bridge University Press, London.
- Kendal, W. S., and P. Frost, 1988 Pitfalls and practice of Luria- Delbru ¨ck fluctuation analysis: a review. Cancer Res. 48: 1060-1065.
- Wolfram Research, 2005 Mathematica Edition, Version 5.
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Estimating mutation rates in low-replication experiments
Couce, A., & Blázquez, J. (2011) · Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis · doi:10.1016/j.mrfmmm.2011.06.005 · Cited by 13 · see citing works
Łazowski, K. (2023) · Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis · doi:10.1016/j.mrfmmm.2023.111816 · Cited by 10 · see citing works
- Authors
- Gerrish, P.
- Affiliations
- University of New Mexico (US)
- Corresponding author
- Gerrish, Philip
- ORCID
- Gerrish, Philip
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-11-01
- Volume
- 180
- Issue
- 3
- Pages
- 1773-1778
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- PMC
- PMC2581975
- OpenAlex
- W2120086627
- Cited by
- 28
- References
- 2
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 1 · 2018: 1 · 2017: 2 · 2016: 1 · 2015: 1 · 2014: 2 · 2013: 3 · 2012: 4
- FWCI
- 0.57
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM079483)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- mutation rate estimation, recursion formula, likelihood estimation, Bayesian estimation, confidence intervals
- MeSH terms
- Animals, Computer Simulation, Humans, Models, Genetic, Mutation, Genetic Variation, Models, Statistical, Likelihood Functions
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