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The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks

Overview of attention for article published in PLOS ONE, March 2013
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
9 X users

Citations

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49 Dimensions

Readers on

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89 Mendeley
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2 CiteULike
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Title
The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks
Published in
PLOS ONE, March 2013
DOI 10.1371/journal.pone.0058134
Pubmed ID
Authors

Yuliang Jin, Dmitrij Turaev, Thomas Weinmaier, Thomas Rattei, Hernán A. Makse

Abstract

Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Japan 2 2%
India 1 1%
Brazil 1 1%
Slovenia 1 1%
Canada 1 1%
Spain 1 1%
United States 1 1%
Unknown 79 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 25%
Researcher 14 16%
Student > Master 9 10%
Professor 8 9%
Student > Bachelor 7 8%
Other 18 20%
Unknown 11 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 46%
Biochemistry, Genetics and Molecular Biology 8 9%
Computer Science 8 9%
Physics and Astronomy 3 3%
Engineering 3 3%
Other 12 13%
Unknown 14 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 22 May 2017.
All research outputs
#1,706,753
of 24,825,035 outputs
Outputs from PLOS ONE
#21,201
of 214,954 outputs
Outputs of similar age
#13,278
of 202,024 outputs
Outputs of similar age from PLOS ONE
#497
of 5,435 outputs
Altmetric has tracked 24,825,035 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 214,954 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.7. This one has done particularly well, scoring higher than 90% 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 202,024 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 5,435 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.