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Trade-Offs and Constraints in Allosteric Sensing

Overview of attention for article published in PLoS Computational Biology, November 2011
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Title
Trade-Offs and Constraints in Allosteric Sensing
Published in
PLoS Computational Biology, November 2011
DOI 10.1371/journal.pcbi.1002261
Pubmed ID
Authors

Bruno M.C. Martins, Peter S. Swain

Abstract

Sensing extracellular changes initiates signal transduction and is the first stage of cellular decision-making. Yet relatively little is known about why one form of sensing biochemistry has been selected over another. To gain insight into this question, we studied the sensing characteristics of one of the biochemically simplest of sensors: the allosteric transcription factor. Such proteins, common in microbes, directly transduce the detection of a sensed molecule to changes in gene regulation. Using the Monod-Wyman-Changeux model, we determined six sensing characteristics--the dynamic range, the Hill number, the intrinsic noise, the information transfer capacity, the static gain, and the mean response time--as a function of the biochemical parameters of individual sensors and of the number of sensors. We found that specifying one characteristic strongly constrains others. For example, a high dynamic range implies a high Hill number and a high capacity, and vice versa. Perhaps surprisingly, these constraints are so strong that most of the space of characteristics is inaccessible given biophysically plausible ranges of parameter values. Within our approximations, we can calculate the probability distribution of the numbers of input molecules that maximizes information transfer and show that a population of one hundred allosteric transcription factors can in principle distinguish between more than four bands of input concentrations. Our results imply that allosteric sensors are unlikely to have been selected for high performance in one sensing characteristic but for a compromise in the performance of many.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Netherlands 2 2%
United Kingdom 2 2%
Germany 1 1%
Czechia 1 1%
Argentina 1 1%
Portugal 1 1%
Japan 1 1%
Spain 1 1%
Other 0 0%
Unknown 82 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 30%
Student > Ph. D. Student 26 27%
Student > Bachelor 8 8%
Professor > Associate Professor 7 7%
Professor 5 5%
Other 17 18%
Unknown 4 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 38%
Biochemistry, Genetics and Molecular Biology 20 21%
Physics and Astronomy 9 9%
Chemistry 5 5%
Mathematics 4 4%
Other 13 14%
Unknown 9 9%