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Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology

Overview of attention for article published in PLOS ONE, June 2012
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Title
Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0039052
Pubmed ID
Authors

G. Jack Peterson, Steve Pressé, Kristin S. Peterson, Ken A. Dill

Abstract

We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein's neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution.

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The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 3%
Japan 1 2%
Luxembourg 1 2%
Germany 1 2%
Unknown 56 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 33%
Researcher 13 21%
Professor 6 10%
Student > Master 5 8%
Student > Bachelor 4 7%
Other 8 13%
Unknown 5 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 36%
Computer Science 7 11%
Biochemistry, Genetics and Molecular Biology 5 8%
Physics and Astronomy 5 8%
Medicine and Dentistry 4 7%
Other 10 16%
Unknown 8 13%