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In this methodology, quantitative fitness metrics are used to assess the quality of a simulation.
Using E. coli as proof of principle, we show that combining large-scale chemical genomics with quantitative fitness measurements provides a high-quality data set rich in discovery.
The quantitative fitness consequences of the cost to cross-feeding by Salmonella and the benefit provided by E. coli were not measured.
The high-dimensionality of the datasets poses also challenging computational problems; for instance, the current version of the SGA genetic interaction database comprises millions of quantitative fitness measurements [32].
Even when we included crop plants, we could not find any publications where single genes reduced quantitative fitness traits in a plant as strongly as in the present case, yet only in the field and not in the glasshouse [47].
Using a quantitative fitness assay, we quantified the growth inhibition for MG and glyoxal.
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In addition to the primary modules that are used to specify demography, mutation, recombination, quantitative traits, fitness and reported output, there are several building block modules that provide basic functionality to the primary modules.
While coevolutionary models have largely focused on qualitative virulence and resistance loci controlling infection [ 12, 14, 17, 18] we demonstrate that host genotype continues to affect quantitative pathogen fitness following infection.
Besides the quantitative reduction of fitness, we observed that some spikes of the two lines Pm3b#2 and #4 also differed in their morphology during flowering time and that the same two lines were also more heavily infected by ergot fungus than the other two GM lines and the four control lines.
Observations also confirm predictions from abstract general models that map quantitative traits to fitness [ 143].
Moreover, we used the fitness quantitative data of Steinmetz et al. (2002) to calculate the essentiality for each gene.
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