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A Quantitative Study of the Division Cycle of Caulobacter crescentus Stalked Cells

Overview of attention for article published in PLoS Computational Biology, January 2008
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Title
A Quantitative Study of the Division Cycle of Caulobacter crescentus Stalked Cells
Published in
PLoS Computational Biology, January 2008
DOI 10.1371/journal.pcbi.0040009
Pubmed ID
Authors

Shenghua Li, Paul Brazhnik, Bruno Sobral, John J Tyson

Abstract

Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genome replication and partitioning to daughter cells. From published experimental evidence, we propose a molecular mechanism for control of the cell division cycle in Caulobacter crescentus. The mechanism, which is based on the synthesis and degradation of three "master regulator" proteins (CtrA, GcrA, and DnaA), is converted into a quantitative model, in order to study the temporal dynamics of these and other cell cycle proteins. The model accounts for important details of the physiology, biochemistry, and genetics of cell cycle control in stalked C. crescentus cell. It reproduces protein time courses in wild-type cells, mimics correctly the phenotypes of many mutant strains, and predicts the phenotypes of currently uncharacterized mutants. Since many of the proteins involved in regulating the cell cycle of C. crescentus are conserved among many genera of alpha-proteobacteria, the proposed mechanism may be applicable to other species of importance in agriculture and medicine.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 9%
United Kingdom 2 3%
Unknown 70 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 24%
Researcher 16 20%
Student > Bachelor 9 11%
Student > Master 8 10%
Professor > Associate Professor 6 8%
Other 18 23%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 58%
Biochemistry, Genetics and Molecular Biology 9 11%
Immunology and Microbiology 5 6%
Computer Science 4 5%
Medicine and Dentistry 2 3%
Other 7 9%
Unknown 6 8%