Critical Mutation Rate has an Exponential Dependence on Population Size for Eukaryotic-length Genomes with Crossover

Abstract

The critical mutation rate (CMR) determines the shift between survival-of-the-fittest and survival of individuals with greater mutational robustness (“flattest”). We identify an inverse relationship between CMR and sequence length in an in silico system with a two-peak fitness landscape; CMR decreases to no more than five orders of magnitude above estimates of eukaryotic per base mutation rate. We confirm the CMR reduces exponentially at low population sizes, irrespective of peak radius and distance, and increases with the number of genetic crossovers. We also identify an inverse relationship between CMR and the number of genes, confirming that, for a similar number of genes to that for the plant Arabidopsis thaliana (25,000), the CMR is close to its known wild-type mutation rate; mutation rates for additional organisms were also found to be within one order of magnitude of the CMR. This is the first time such a simulation model has been assigned input and produced output within range for a given biological organism. The decrease in CMR with population size previously observed is maintained; there is potential for the model to influence understanding of populations undergoing bottleneck, stress, and conservation strategy for populations near extinction.

The distribution of fitness effects during adaptive walks using a simple genetic network

“…Random mutations occur at a rate of µ = 9.1528 × 10 − 6 per locus per generation. This mutation rate is based on the average mutation rates observed in A. thaliana and adjusted to per-locus rates by multiplying it with the average length of a eukaryotic gene (1346bp) (Aston et al . 2017; Xu et al .…”

“…Mutations were randomly and uniformly sampled across the n l loci at a rate of µ = 9.1528 × 10 −6 per locus per generation. This mutation rate is based on the average mutation rates observed in Arabidopsis thaliana and adjusted to per-locus rates by multiplying it with the average length of a eukaryotic gene (1346bp) (Aston et al 2017;Xu et al 2006). A. thaliana was chosen as it is a model plant species with good estimates of mutation rates.…”

Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

“…If the explorer chromosomes are defined as having a higher mutation probability than the conservative chromosomes, the optimal solution can be obtained [6]. Several researchers have set the probability as 1/l for the mutation operator, where l is the chromosome length [43,45,46]. Accordingly, mutation probabilities of 1/l and 0.75 (default) were used for the conservative and the explorer chromosomes, respectively, in this study.…”

Multiple-Searching Genetic Algorithm for Whole Test Suites

“…The default of the mutation probabilities in EvoSuite is 0.75. Several researchers [18,50] demonstrated that 1/l is an efficient mutation probability, where l is the length of chromosomes. Therefore, the mutation probability of conservative chromosomes was defined as 1/l and that of explorer chromosomes was assigned the default value (0.75) because 1/l has a smaller value than the default value.…”