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Prediction by Promoter Logic in Bacterial Quorum Sensing

Overview of attention for article published in PLoS Computational Biology, January 2012
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Title
Prediction by Promoter Logic in Bacterial Quorum Sensing
Published in
PLoS Computational Biology, January 2012
DOI 10.1371/journal.pcbi.1002361
Pubmed ID
Authors

Navneet Rai, Rajat Anand, Krishna Ramkumar, Varun Sreenivasan, Sugat Dabholkar, K. V. Venkatesh, Mukund Thattai

Abstract

Quorum-sensing systems mediate chemical communication between bacterial cells, coordinating cell-density-dependent processes like biofilm formation and virulence-factor expression. In the proteobacterial LuxI/LuxR quorum sensing paradigm, a signaling molecule generated by an enzyme (LuxI) diffuses between cells and allosterically stimulates a transcriptional regulator (LuxR) to activate its cognate promoter (pR). By expressing either LuxI or LuxR in positive feedback from pR, these versatile systems can generate smooth (monostable) or abrupt (bistable) density-dependent responses to suit the ecological context. Here we combine theory and experiment to demonstrate that the promoter logic of pR - its measured activity as a function of LuxI and LuxR levels - contains all the biochemical information required to quantitatively predict the responses of such feedback loops. The interplay of promoter logic with feedback topology underlies the versatility of the LuxI/LuxR paradigm: LuxR and LuxI positive-feedback systems show dramatically different responses, while a dual positive/negative-feedback system displays synchronized oscillations. These results highlight the dual utility of promoter logic: to probe microscopic parameters and predict macroscopic phenotype.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 5%
Spain 2 2%
India 1 <1%
Germany 1 <1%
Canada 1 <1%
Unknown 93 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 32%
Researcher 19 18%
Student > Master 10 10%
Student > Bachelor 9 9%
Professor > Associate Professor 8 8%
Other 13 13%
Unknown 11 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 42%
Biochemistry, Genetics and Molecular Biology 12 12%
Engineering 9 9%
Computer Science 6 6%
Physics and Astronomy 5 5%
Other 16 16%
Unknown 12 12%