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The Underlying Molecular and Network Level Mechanisms in the Evolution of Robustness in Gene Regulatory Networks

Overview of attention for article published in PLoS Computational Biology, January 2013
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  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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9 X users

Citations

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17 Dimensions

Readers on

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133 Mendeley
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12 CiteULike
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Title
The Underlying Molecular and Network Level Mechanisms in the Evolution of Robustness in Gene Regulatory Networks
Published in
PLoS Computational Biology, January 2013
DOI 10.1371/journal.pcbi.1002865
Pubmed ID
Authors

Mario Pujato, Thomas MacCarthy, Andras Fiser, Aviv Bergman

Abstract

Gene regulatory networks show robustness to perturbations. Previous works identified robustness as an emergent property of gene network evolution but the underlying molecular mechanisms are poorly understood. We used a multi-tier modeling approach that integrates molecular sequence and structure information with network architecture and population dynamics. Structural models of transcription factor-DNA complexes are used to estimate relative binding specificities. In this model, mutations in the DNA cause changes on two levels: (a) at the sequence level in individual binding sites (modulating binding specificity), and (b) at the network level (creating and destroying binding sites). We used this model to dissect the underlying mechanisms responsible for the evolution of robustness in gene regulatory networks. Results suggest that in sparse architectures (represented by short promoters), a mixture of local-sequence and network-architecture level changes are exploited. At the local-sequence level, robustness evolves by decreasing the probabilities of both the destruction of existent and generation of new binding sites. Meanwhile, in highly interconnected architectures (represented by long promoters), robustness evolves almost entirely via network level changes, deleting and creating binding sites that modify the network architecture.

X Demographics

X Demographics

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 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 5%
France 2 2%
Portugal 2 2%
United Kingdom 2 2%
Brazil 2 2%
Chile 1 <1%
Netherlands 1 <1%
Switzerland 1 <1%
Germany 1 <1%
Other 6 5%
Unknown 108 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 42 32%
Student > Ph. D. Student 34 26%
Professor 9 7%
Student > Postgraduate 9 7%
Professor > Associate Professor 9 7%
Other 22 17%
Unknown 8 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 74 56%
Biochemistry, Genetics and Molecular Biology 22 17%
Engineering 7 5%
Computer Science 6 5%
Physics and Astronomy 4 3%
Other 9 7%
Unknown 11 8%
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 26 September 2023.
All research outputs
#7,280,570
of 25,593,129 outputs
Outputs from PLoS Computational Biology
#4,923
of 9,006 outputs
Outputs of similar age
#71,579
of 290,062 outputs
Outputs of similar age from PLoS Computational Biology
#48
of 114 outputs
Altmetric has tracked 25,593,129 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 9,006 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 290,062 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 74% of its contemporaries.
We're also able to compare this research output to 114 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.