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Characterizing the roles of changing population size and selection on the evolution of flux control in metabolic pathways

Overview of attention for article published in BMC Ecology and Evolution, May 2017
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  • Average Attention Score compared to outputs of the same age and source

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Title
Characterizing the roles of changing population size and selection on the evolution of flux control in metabolic pathways
Published in
BMC Ecology and Evolution, May 2017
DOI 10.1186/s12862-017-0962-7
Pubmed ID
Authors

Alena Orlenko, Peter B. Chi, David A. Liberles

Abstract

Understanding the genotype-phenotype map is fundamental to our understanding of genomes. Genes do not function independently, but rather as part of networks or pathways. In the case of metabolic pathways, flux through the pathway is an important next layer of biological organization up from the individual gene or protein. Flux control in metabolic pathways, reflecting the importance of mutation to individual enzyme genes, may be evolutionarily variable due to the role of mutation-selection-drift balance. The evolutionary stability of rate limiting steps and the patterns of inter-molecular co-evolution were evaluated in a simulated pathway with a system out of equilibrium due to fluctuating selection, population size, or positive directional selection, to contrast with those under stabilizing selection. Depending upon the underlying population genetic regime, fluctuating population size was found to increase the evolutionary stability of rate limiting steps in some scenarios. This result was linked to patterns of local adaptation of the population. Further, during positive directional selection, as with more complex mutational scenarios, an increase in the observation of inter-molecular co-evolution was observed. Differences in patterns of evolution when systems are in and out of equilibrium, including during positive directional selection may lead to predictable differences in observed patterns for divergent evolutionary scenarios. In particular, this result might be harnessed to detect differences between compensatory processes and directional processes at the pathway level based upon evolutionary observations in individual proteins. Detecting functional shifts in pathways reflects an important milestone in predicting when changes in genotypes result in changes in phenotypes.

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The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 33%
Researcher 8 22%
Student > Ph. D. Student 8 22%
Student > Doctoral Student 1 3%
Student > Bachelor 1 3%
Other 2 6%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 36%
Biochemistry, Genetics and Molecular Biology 12 33%
Computer Science 2 6%
Neuroscience 2 6%
Medicine and Dentistry 1 3%
Other 0 0%
Unknown 6 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 May 2017.
All research outputs
#6,869,597
of 25,394,764 outputs
Outputs from BMC Ecology and Evolution
#1,533
of 3,714 outputs
Outputs of similar age
#100,696
of 327,165 outputs
Outputs of similar age from BMC Ecology and Evolution
#38
of 73 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 3,714 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 327,165 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 73 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.