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Escape times for branching processes with random mutational fitness effects

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  • Foo, Jasmine
  • Leder, Kevin
  • Zhu, Junfeng

Abstract

We consider a large declining population of cells under an external selection pressure, modeled as a subcritical branching process. This population has genetic variation introduced at a low rate which leads to the production of exponentially expanding mutant populations, enabling population escape from extinction. Here we consider two possible settings for the effects of the mutation: Case (I) a deterministic mutational fitness advance and Case (II) a random mutational fitness advance. We first establish a functional central limit theorem for the renormalized and sped up version of the mutant cell process. We establish that in Case (I) the limiting process is a trivial constant stochastic process, while in Case (II) the limit process is a continuous Gaussian process for which we identify the covariance kernel. Lastly we apply the functional central limit theorem and some other auxiliary results to establish a central limit theorem (in the large initial population limit) of the first time at which the mutant cell population dominates the population. We find that the limiting distribution is Gaussian in both Cases (I) and (II), but a logarithmic correction is needed in the scaling for Case (II). This problem is motivated by the question of optimal timing for switching therapies to effectively control drug resistance in biomedical applications.

Suggested Citation

  • Foo, Jasmine & Leder, Kevin & Zhu, Junfeng, 2014. "Escape times for branching processes with random mutational fitness effects," Stochastic Processes and their Applications, Elsevier, vol. 124(11), pages 3661-3697.
    • Handle: RePEc:eee:spapps:v:124:y:2014:i:11:p:3661-3697
    DOI: 10.1016/j.spa.2014.06.003
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    References listed on IDEAS

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    1. Durrett, Rick & Foo, Jasmine & Leder, Kevin & Mayberry, John & Michor, Franziska, 2010. "Evolutionary dynamics of tumor progression with random fitness values," Theoretical Population Biology, Elsevier, vol. 78(1), pages 54-66.
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    Cited by:

    1. Hanagal, Pranav & Leder, Kevin & Wang, Zicheng, 2022. "Large deviations of cancer recurrence timing," Stochastic Processes and their Applications, Elsevier, vol. 147(C), pages 1-50.

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