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LsrR Quorum Sensing “Switch” Is Revealed by a Bottom-Up Approach

Overview of attention for article published in PLoS Computational Biology, September 2011
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Title
LsrR Quorum Sensing “Switch” Is Revealed by a Bottom-Up Approach
Published in
PLoS Computational Biology, September 2011
DOI 10.1371/journal.pcbi.1002172
Pubmed ID
Authors

Sara Hooshangi, William E. Bentley

Abstract

Quorum sensing (QS) enables bacterial multicellularity and selective advantage for communicating populations. While genetic "switching" phenomena are a common feature, their mechanistic underpinnings have remained elusive. The interplay between circuit components and their regulation are intertwined and embedded. Observable phenotypes are complex and context dependent. We employed a combination of experimental work and mathematical models to decipher network connectivity and signal transduction in the autoinducer-2 (AI-2) quorum sensing system of E. coli. Negative and positive feedback mechanisms were examined by separating the network architecture into sub-networks. A new unreported negative feedback interaction was hypothesized and tested via a simple mathematical model. Also, the importance of the LsrR regulator and its determinant role in the E. coli QS "switch", normally masked by interfering regulatory loops, were revealed. Our simple model allowed mechanistic understanding of the interplay among regulatory sub-structures and their contributions to the overall native functioning network. This "bottom up" approach in understanding gene regulation will serve to unravel complex QS network architectures and lead to the directed coordination of emergent behaviors.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 5%
Germany 2 4%
China 1 2%
Spain 1 2%
Japan 1 2%
United States 1 2%
Unknown 48 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 28%
Researcher 13 23%
Student > Bachelor 6 11%
Student > Postgraduate 4 7%
Student > Master 4 7%
Other 7 12%
Unknown 7 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 32%
Biochemistry, Genetics and Molecular Biology 10 18%
Computer Science 5 9%
Engineering 5 9%
Chemistry 3 5%
Other 6 11%
Unknown 10 18%