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Temporal Controls of the Asymmetric Cell Division Cycle in Caulobacter crescentus

Overview of attention for article published in PLoS Computational Biology, August 2009
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Title
Temporal Controls of the Asymmetric Cell Division Cycle in Caulobacter crescentus
Published in
PLoS Computational Biology, August 2009
DOI 10.1371/journal.pcbi.1000463
Pubmed ID
Authors

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

Abstract

The asymmetric cell division cycle of Caulobacter crescentus is orchestrated by an elaborate gene-protein regulatory network, centered on three major control proteins, DnaA, GcrA and CtrA. The regulatory network is cast into a quantitative computational model to investigate in a systematic fashion how these three proteins control the relevant genetic, biochemical and physiological properties of proliferating bacteria. Different controls for both swarmer and stalked cell cycles are represented in the mathematical scheme. The model is validated against observed phenotypes of wild-type cells and relevant mutants, and it predicts the phenotypes of novel mutants and of known mutants under novel experimental conditions. Because the cell cycle control proteins of Caulobacter are conserved across many species of alpha-proteobacteria, the model we are proposing here may be applicable to other genera of importance to agriculture and medicine (e.g., Rhizobium, Brucella).

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The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
United Kingdom 1 1%
New Zealand 1 1%
Germany 1 1%
Unknown 74 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 20%
Researcher 16 20%
Student > Master 9 11%
Student > Bachelor 8 10%
Student > Postgraduate 6 8%
Other 15 19%
Unknown 10 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 39%
Biochemistry, Genetics and Molecular Biology 15 19%
Immunology and Microbiology 5 6%
Computer Science 3 4%
Medicine and Dentistry 3 4%
Other 12 15%
Unknown 11 14%