The change in fitness of an RNA molecule under replication with mutation rate μ is quantified by comparing the secondary structures of the mother i and daughter j sequences.
The maximum likelihood values and standard errors of these parameters were estimated using the mle function in R. Five of the parameters specify the curve (shown in Fig. 2) describing the rate of within-house infection: m,σ, g, β f and β h ; in addition, the mutation rate is μ, and m quantifies any decline in infectivity with household size.
To assess the sensitivity of our assay, we quantified mutation rates for alleles of 33 mutator genes.
We also quantified the influence of mutation rate on the ability to estimate N e.
Next, we quantified mutation rates in these cells.
To quantify the effect of mutation rate variation on synonymous mtDNA diversity we combined the bird and mammal polymorphism and neutral substitution rate datasets and performed a multiple regression of substitution rate and bird/mammal status (class) on synonymous mtDNA diversity.
To determine the extent to which exposure to Mn2+ increases the accumulations of mutations and to quantify the increase in the mutation rate of wild-type yeast cells, we utilized the CAN1 forward mutation assay [ 33], as described in Section 2. As shown in Figure 4, the mutation rate increased 12-fold (from 1.9 × 10−7 to 23.1 × 10−7) when wild-type cells were treated with 1.5 mM Mn2+.
Our studies have quantified the impact of the mutational fitness landscape on the composition of recurrent tumors and underscore the importance of experimental efforts to quantify mutation rates and the distribution of random fitness effects of mutations in cancer.
Significantly mutated genes (SMGs) were defined as the somatic mutations with higher mutated frequency than background mutation rate.
In contrast to cell numbers, the mutation rate parameter in our model is relatively difficult to quantify, as it represents the rate of resistant cells arising from the sensitive cell population.
Techniques from experimental evolution could be useful in the context of tumor biology to systematically quantify mutation rates and evaluate costs of resistance (at least in vitro) and determine the role of the microenvironment in diversification.
