Literature DB >> 2307353

Fluctuation analysis: the probability distribution of the number of mutants under different conditions.

F M Stewart1, D M Gordon, B R Levin.   

Abstract

In the 47 years since fluctuation analysis was introduced by Luria and Delbrück, it has been widely used to calculate mutation rates. Up to now, in spite of the importance of such calculations, the probability distribution of the number of mutants that will appear in a fluctuation experiment has been known only under the restrictive, and possibly unrealistic, assumptions: (1) that the mutation rate is exactly proportional to the growth rate and (2) that all mutants grow at a rate that is a constant multiple of the growth rate of the original cells. In this paper, we approach the distribution of the number of mutants from a new point of view that will enable researchers to calculate the distribution to be expected using assumptions that they believe to be closer to biological reality. The new idea is to classify mutations according to the number of observable mutants that derive from the mutation when the culture is selectively plated. This approach also simplifies the calculations in situations where two, or many, kinds of mutation may occur in a single culture.

Mesh:

Year:  1990        PMID: 2307353      PMCID: PMC1203904     

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  4 in total

1.  Statistical concepts in the theory of bacterial mutation.

Authors:  P ARMITAGE
Journal:  J Hyg (Lond)       Date:  1953-06

2.  The origin of mutants.

Authors:  J Cairns; J Overbaugh; S Miller
Journal:  Nature       Date:  1988-09-08       Impact factor: 49.962

3.  Mutation and selection in bacterial populations: alternatives to the hypothesis of directed mutation.

Authors:  R E Lenski; M Slatkin; F J Ayala
Journal:  Proc Natl Acad Sci U S A       Date:  1989-04       Impact factor: 11.205

4.  A deterministic approach for the estimation of mutation rates in cultured mammalian cells.

Authors:  I C Li; S C Wu; J Fu; E H Chu
Journal:  Mutat Res       Date:  1985-03       Impact factor: 2.433

  4 in total
  50 in total

Review 1.  Determining mutation rates in bacterial populations.

Authors:  W A Rosche; P L Foster
Journal:  Methods       Date:  2000-01       Impact factor: 3.608

Review 2.  Evolution of drug resistance in Mycobacterium tuberculosis: clinical and molecular perspective.

Authors:  Stephen H Gillespie
Journal:  Antimicrob Agents Chemother       Date:  2002-02       Impact factor: 5.191

Review 3.  Directed mutation: between unicorns and goats.

Authors:  P L Foster
Journal:  J Bacteriol       Date:  1992-03       Impact factor: 3.490

4.  Development of efficient suicide mechanisms for biological containment of bacteria.

Authors:  S M Knudsen; O H Karlström
Journal:  Appl Environ Microbiol       Date:  1991-01       Impact factor: 4.792

5.  Salvador Luria and Max Delbrück on Random Mutation and Fluctuation Tests.

Authors:  Andrew Murray
Journal:  Genetics       Date:  2016-02       Impact factor: 4.562

Review 6.  Evolution of acquired resistance to anti-cancer therapy.

Authors:  Jasmine Foo; Franziska Michor
Journal:  J Theor Biol       Date:  2014-03-25       Impact factor: 2.691

7.  On fluctuation analysis: a new, simple and efficient method for computing the expected number of mutants.

Authors:  S Sarkar; W T Ma; G H Sandri
Journal:  Genetica       Date:  1992       Impact factor: 1.082

8.  Update on estimation of mutation rates using data from fluctuation experiments.

Authors:  Qi Zheng
Journal:  Genetics       Date:  2005-07-14       Impact factor: 4.562

9.  Methods for determining spontaneous mutation rates.

Authors:  Patricia L Foster
Journal:  Methods Enzymol       Date:  2006       Impact factor: 1.600

10.  Estimating the per-base-pair mutation rate in the yeast Saccharomyces cerevisiae.

Authors:  Gregory I Lang; Andrew W Murray
Journal:  Genetics       Date:  2008-01       Impact factor: 4.562

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