Abstract
BackgroundIn this report we re-examine some recent experiments with digital organisms to test some predictions of quasispecies theory. These experiments revealed that under high mutation rates populations of less fit organisms previously adapted to such high mutation rates were able to outcompete organisms with higher average fitness but adapted to low mutation rates.ResultsWe have verified that these results do hold in the original conditions and, by extending the set of initial parameters, we have also detected that the critical mutation rate was independent of population size, a result that we have found to be dependent on a different, contingent factor, the initial fitness vector. Furthermore, in all but one case, the critical mutation rate is higher than the error threshold, a key parameter in quasispecies theory, which prevents its extrapolation to natural viral populations.ConclusionFrom these results we conclude that digital organisms are useful tools for investigating evolutionary patterns and processes including some predictions from the quasispecies theory.
Highlights
In this report we re-examine some recent experiments with digital organisms to test some predictions of quasispecies theory
RNA viruses are among the most infective pathogens affecting plants, animals and humans. Several of their features such as their reduced genomes, high genetic heterogeneity, large population sizes, short generation times and fast evolutionary rates place them among the best models for evolutionary and population genetic studies [1,2]
We considered three factors affecting the critical mutation rate in our digital organisms: genome size, population size and the influence of the initial fitness vector
Summary
In this report we re-examine some recent experiments with digital organisms to test some predictions of quasispecies theory. RNA viruses are among the most infective pathogens affecting plants, animals and humans Several of their features such as their reduced genomes, high genetic heterogeneity, large population sizes, short generation times and fast evolutionary rates place them among the best models for evolutionary and population genetic studies [1,2]. These same features explain why they are so difficult to eradicate. The molecular bases for this genetic variability are three mechanisms differentially used by each kind of virus: mutation, homologous and nonhomologous recombination and genome rearrangement [3]
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