Sequential mutations in exponentially growing populations

Michael d. Nicholson, David Cheek, Tibor Antal, Jasmine Foo (Editor)

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)e1011289
JournalPLoS Computational Biology
Issue number7
Publication statusPublished - 10 Jul 2023


Dive into the research topics of 'Sequential mutations in exponentially growing populations'. Together they form a unique fingerprint.

Cite this