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Fluctuation Analysis: Can Estimates Be Trusted?

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  • Bernard Ycart

Abstract

The estimation of mutation rates and relative fitnesses in fluctuation analysis is based on the unrealistic hypothesis that the single-cell times to division are exponentially distributed. Using the classical Luria-Delbrück distribution outside its modelling hypotheses induces an important bias on the estimation of the relative fitness. The model is extended here to any division time distribution. Mutant counts follow a generalization of the Luria-Delbrück distribution, which depends on the mean number of mutations, the relative fitness of normal cells compared to mutants, and the division time distribution of mutant cells. Empirical probability generating function techniques yield precise estimates both of the mean number of mutations and the relative fitness of normal cells compared to mutants. In the case where no information is available on the division time distribution, it is shown that the estimation procedure using constant division times yields more reliable results. Numerical results both on observed and simulated data are reported.

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  • Bernard Ycart, 2013. "Fluctuation Analysis: Can Estimates Be Trusted?," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    • Handle: RePEc:plo:pone00:0080958
    DOI: 10.1371/journal.pone.0080958
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    Cited by:

    1. Hernandez-Suarez, Carlos & Rabinovich, Jorge, 2024. "Exact confidence intervals for population growth rate, longevity and generation time," Theoretical Population Biology, Elsevier, vol. 155(C), pages 1-9.

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