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Our work allows us to estimate the phenotypic mutation rate based on data on the fraction of abnormal proteins.
For each population, we propose an adaptive mutation rate tuning method to increase the mutation rate based on fitness values and remaining generations.
Thus, we formulated the evolutionary dynamics of an asexual self-replicator population with a finite population size and hyper mutation rate, based on the probability density of fitnesses (fitness distribution) for the evolving population.
Dissatisfied with such wobble in the basic gears of the molecular clock, evolutionary biologists Sudhir Kumar and Sankar Subramanian decided to establish a more reliable mammalian mutation rate based on as many gene sequences as possible.
The effective mutation rate (based on evolutionary considerations) has been estimated as 1.52×10−3 per generation for an average autosomal dinucleotide STR locus and as 0.85−0.93×10−3 per generation for tri- and tetranucleotide loci [16]; the mutation rate for an average Y chromosome tri- or tetranucleotide STR locus has been estimated as 6.9×10−4 per 25 years [17].
Our simulation results showed that populations tended to form an optimum mutation rate based on their initial configuration.
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Considerable theoretical literature exists on the evolution of mutation rates based on the evolutionary fate of mutators [ 11, 12, 51].
Values of mutation rates based on mtDNA complete genome variability data (one mutation every 3624 years [19]) and synonymous substitutions (one mutation every 7884 years [19]) were used.
One way to test this possibility is to estimate the mutation rates based on available data.
For the nematode C. elegans, there are direct estimates of the per-generation nuclear and mtDNA mutation rates based on mutation-accumulation line systems [ 5, 6].
TMRCA estimates were calculated using the rho statistic and its standard deviation (SD) [ 55] with three previously described mutation rates based on coding region mutations.
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