Zheng Qi
Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX 77843, USA.
Genetica. 2011 Dec;139(11-12):1409-16. doi: 10.1007/s10709-012-9639-8. Epub 2012 Mar 7.
The fluctuation experiment is an essential tool for measuring microbial mutation rates in the laboratory. When inferring the mutation rate from an experiment, one assumes that the number of mutants in each test tube follows a common distribution. This assumption conceptually restricts the scope of applicability of fluctuation assay. We relax this assumption by proposing a Bayesian two-level model, under which an experiment-wide average mutation rate can be defined. The new model suggests a gamma mixture of the Luria-Delbrück distribution, which coincides with a recently discovered discrete distribution. While the mixture model is of considerable independent interest in fluctuation assay, it also offers a practical Markov chain Monte Carlo method for estimating mutation rates. We illustrate the Bayesian approach with a detailed analysis of an actual fluctuation experiment.
波动实验是在实验室中测量微生物突变率的重要工具。从实验推断突变率时，人们假定每个试管中的突变体数量遵循共同分布。这一假设在概念上限制了波动试验的适用范围。我们通过提出一种贝叶斯二级模型来放宽这一假设，在此模型下可以定义全实验范围的平均突变率。新模型提出了卢里亚-德尔布吕克分布的伽马混合，它与最近发现的一种离散分布一致。虽然混合模型在波动试验中具有相当大的独立研究价值，但它也为估计突变率提供了一种实用的马尔可夫链蒙特卡罗方法。我们通过对一个实际波动实验的详细分析来说明贝叶斯方法。
