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The effect of spatial randomness on the average fixation time of mutants

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  • Suzan Farhang-Sardroodi
  • Amir H Darooneh
  • Moladad Nikbakht
  • Natalia L Komarova
  • Mohammad Kohandel

Abstract

The mean conditional fixation time of a mutant is an important measure of stochastic population dynamics, widely studied in ecology and evolution. Here, we investigate the effect of spatial randomness on the mean conditional fixation time of mutants in a constant population of cells, N. Specifically, we assume that fitness values of wild type cells and mutants at different locations come from given probability distributions and do not change in time. We study spatial arrangements of cells on regular graphs with different degrees, from the circle to the complete graph, and vary assumptions on the fitness probability distributions. Some examples include: identical probability distributions for wild types and mutants; cases when only one of the cell types has random fitness values while the other has deterministic fitness; and cases where the mutants are advantaged or disadvantaged. Using analytical calculations and stochastic numerical simulations, we find that randomness has a strong impact on fixation time. In the case of complete graphs, randomness accelerates mutant fixation for all population sizes, and in the case of circular graphs, randomness delays mutant fixation for N larger than a threshold value (for small values of N, different behaviors are observed depending on the fitness distribution functions). These results emphasize fundamental differences in population dynamics under different assumptions on cell connectedness. They are explained by the existence of randomly occurring “dead zones” that can significantly delay fixation on networks with low connectivity; and by the existence of randomly occurring “lucky zones” that can facilitate fixation on networks of high connectivity. Results for death-birth and birth-death formulations of the Moran process, as well as for the (haploid) Wright Fisher model are presented.Author summary: We study the influence of randomness on evolutionary dynamics, assuming that a newly arising mutant may experience a different set of environments compared to the wild type. We calculate the mean conditional fixation time of the mutant under different assumptions on spatial interactions, and show that randomness has a strong impact on the fixation time. In particular, it delays the fixation of mutants on 1D circles and accelerates it on complete graphs (the so called mass action, or complete mixing, model). This result holds for advantageous, disadvantageous, and neutral (on average) mutants. The reason for this pattern is quite intuitive: in a rigid, 1D structure, randomness can by chance put a “roadblock” and disrupt mutant spread, causing significant delay. In higher dimensions, there are many ways for a mutant to spread, and it is difficult to block all of them by chance; on the other hand, randomness can enhance fixation by providing an “easier” path. The effects of a random environment are important in biological models such as bacterial growth or cancer initiation/progression.

Suggested Citation

  • Suzan Farhang-Sardroodi & Amir H Darooneh & Moladad Nikbakht & Natalia L Komarova & Mohammad Kohandel, 2017. "The effect of spatial randomness on the average fixation time of mutants," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-22, November.
    • Handle: RePEc:plo:pcbi00:1005864
    DOI: 10.1371/journal.pcbi.1005864
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    References listed on IDEAS

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    1. Natalia L. Komarova, 2015. "A moving target," Nature, Nature, vol. 525(7568), pages 198-199, September.
    2. Erez Lieberman & Christoph Hauert & Martin A. Nowak, 2005. "Evolutionary dynamics on graphs," Nature, Nature, vol. 433(7023), pages 312-316, January.
    3. Wes Maciejewski & Feng Fu & Christoph Hauert, 2014. "Evolutionary Game Dynamics in Populations with Heterogenous Structures," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-16, April.
    4. V S K Manem & K Kaveh & M Kohandel & S Sivaloganathan, 2015. "Modeling Invasion Dynamics with Spatial Random-Fitness Due to Micro-Environment," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-20, October.
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    2. Kamran Kaveh & Alex McAvoy & Krishnendu Chatterjee & Martin A Nowak, 2020. "The Moran process on 2-chromatic graphs," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-18, November.
    3. Zhang, Libin & Yao, Zijun & Wu, Bin, 2021. "Calculating biodiversity under stochastic evolutionary dynamics," Applied Mathematics and Computation, Elsevier, vol. 411(C).

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