Amplifiers of selection for the Moran process with both Birth-death and death-Birth updating.

Jakub Svoboda, Soham Joshi, Josef Tkadlec, Krishnendu Chatterjee
Author Information
  1. Jakub Svoboda: IST Austria, Klosterneuburg, Austria. ORCID
  2. Soham Joshi: IST Austria, Klosterneuburg, Austria. ORCID
  3. Josef Tkadlec: Computer Science Institute, Charles University, Prague, Czech Republic. ORCID
  4. Krishnendu Chatterjee: IST Austria, Klosterneuburg, Austria. ORCID

Abstract

Populations evolve by accumulating advantageous mutations. Every population has some spatial structure that can be modeled by an underlying network. The network then influences the probability that new advantageous mutations fixate. Amplifiers of selection are networks that increase the fixation probability of advantageous mutants, as compared to the unstructured fully-connected network. Whether or not a network is an amplifier depends on the choice of the random process that governs the evolutionary dynamics. Two popular choices are Moran process with Birth-death updating and Moran process with death-Birth updating. Interestingly, while some networks are amplifiers under Birth-death updating and other networks are amplifiers under death-Birth updating, so far no spatial structures have been found that function as an amplifier under both types of updating simultaneously. In this work, we identify networks that act as amplifiers of selection under both versions of the Moran process. The amplifiers are robust, modular, and increase fixation probability for any mutant fitness advantage in a range r ∈ (1, 1.2). To complement this positive result, we also prove that for certain quantities closely related to fixation probability, it is impossible to improve them simultaneously for both versions of the Moran process. Together, our results highlight how the two versions of the Moran process differ and what they have in common.

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MeSH Term

Population Dynamics
Models, Biological
Biological Evolution
Mutation
Probability
Selection, Genetic

Word Cloud

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