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Linkers of Cell Polarity and Cell Cycle Regulation in the Fission Yeast Protein Interaction Network

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
Linkers of Cell Polarity and Cell Cycle Regulation in the Fission Yeast Protein Interaction Network
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002732
Pubmed ID
Authors

Federico Vaggi, James Dodgson, Archana Bajpai, Anatole Chessel, Ferenc Jordán, Masamitsu Sato, Rafael Edgardo Carazo-Salas, Attila Csikász-Nagy

Abstract

The study of gene and protein interaction networks has improved our understanding of the multiple, systemic levels of regulation found in eukaryotic and prokaryotic organisms. Here we carry out a large-scale analysis of the protein-protein interaction (PPI) network of fission yeast (Schizosaccharomyces pombe) and establish a method to identify 'linker' proteins that bridge diverse cellular processes - integrating Gene Ontology and PPI data with network theory measures. We test the method on a highly characterized subset of the genome consisting of proteins controlling the cell cycle, cell polarity and cytokinesis and identify proteins likely to play a key role in controlling the temporal changes in the localization of the polarity machinery. Experimental inspection of one such factor, the polarity-regulating RNB protein Sts5, confirms the prediction that it has a cell cycle dependent regulation. Detailed bibliographic inspection of other predicted 'linkers' also confirms the predictive power of the method. As the method is robust to network perturbations and can successfully predict linker proteins, it provides a powerful tool to study the interplay between different cellular processes.

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The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 4%
Spain 1 1%
Netherlands 1 1%
France 1 1%
Unknown 63 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 28%
Researcher 19 28%
Student > Bachelor 7 10%
Student > Master 6 9%
Professor > Associate Professor 5 7%
Other 5 7%
Unknown 8 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 49%
Biochemistry, Genetics and Molecular Biology 12 17%
Computer Science 7 10%
Engineering 3 4%
Physics and Astronomy 2 3%
Other 3 4%
Unknown 8 12%