论文标题

呈指数增长人群的顺序突变

Sequential mutations in exponentially growing populations

论文作者

Nicholson, Michael D., Cheek, David, Antal, Tibor

论文摘要

顺序突变采集的随机模型被广泛用于量化癌症和细菌进化。在多种情景中,经常性的研究问题是:有多少个具有$ n $变化的单元格,以及这些细胞出现需要多长时间。对于成倍增长的人群,这些问题仅在到目前为止的特殊情况下才解决。在这里,在Multinpe分支过程框架内,我们考虑了一种一般的突变路径,突变可能是有利的,中性或有害的。在大型时期和较小的突变率的生物学相关限制方案中,我们得出了具有N突变的细胞数量和到达时间的概率分布。令人惊讶的是,这两个数量分别遵循Mittag-Leffler和Logistic分布,无论$ n $或突变的选择性效果如何。我们的结果提供了一种快速方法来评估改变基本分裂,死亡和突变率如何影响突变细胞的到达时间和数量。我们强调了波动测定中突变率推断的后果。

Stochastic models of sequential mutation acquisition are widely used to quantify cancer and bacterial evolution. Across manifold scenarios, recurrent research questions are: how many cells are there with $n$ alterations, and how long will it take for these cells to appear. For exponentially growing populations, these questions have been tackled only in special cases so far. Here, within a multitype branching process framework, we consider a general mutational path where mutations may be advantageous, neutral or deleterious. In the biologically relevant limiting regimes of large times and small mutation rates, we derive probability distributions for the number, and arrival time, of cells with n mutations. Surprisingly, the two quantities respectively follow Mittag-Leffler and logistic distributions regardless of $n$ or the mutations' selective effects. Our results provide a rapid method to assess how altering the fundamental division, death, and mutation rates impacts the arrival time, and number, of mutant cells. We highlight consequences for mutation rate inference in fluctuation assays.

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