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Speed, Sensitivity, and Bistability in Auto-activating Signaling Circuits

Overview of attention for article published in PLoS Computational Biology, November 2011
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Title
Speed, Sensitivity, and Bistability in Auto-activating Signaling Circuits
Published in
PLoS Computational Biology, November 2011
DOI 10.1371/journal.pcbi.1002265
Pubmed ID
Authors

Rutger Hermsen, David W. Erickson, Terence Hwa

Abstract

Cells employ a myriad of signaling circuits to detect environmental signals and drive specific gene expression responses. A common motif in these circuits is inducible auto-activation: a transcription factor that activates its own transcription upon activation by a ligand or by post-transcriptional modification. Examples range from the two-component signaling systems in bacteria and plants to the genetic circuits of animal viruses such as HIV. We here present a theoretical study of such circuits, based on analytical calculations, numerical computations, and simulation. Our results reveal several surprising characteristics. They show that auto-activation can drastically enhance the sensitivity of the circuit's response to input signals: even without molecular cooperativity, an ultra-sensitive threshold response can be obtained. However, the increased sensitivity comes at a cost: auto-activation tends to severely slow down the speed of induction, a stochastic effect that was strongly underestimated by earlier deterministic models. This slow-induction effect again requires no molecular cooperativity and is intimately related to the bimodality recently observed in non-cooperative auto-activation circuits. These phenomena pose strong constraints on the use of auto-activation in signaling networks. To achieve both a high sensitivity and a rapid induction, an inducible auto-activation circuit is predicted to acquire low cooperativity and low fold-induction. Examples from Escherichia coli's two-component signaling systems support these predictions.

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Mendeley readers

The data shown below were compiled from readership statistics for 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
France 2 2%
Portugal 1 <1%
Italy 1 <1%
United Kingdom 1 <1%
Netherlands 1 <1%
Argentina 1 <1%
Taiwan 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 97 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 27%
Student > Ph. D. Student 26 23%
Student > Master 11 10%
Student > Bachelor 11 10%
Professor > Associate Professor 7 6%
Other 14 13%
Unknown 13 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 34%
Biochemistry, Genetics and Molecular Biology 22 20%
Physics and Astronomy 8 7%
Mathematics 5 4%
Computer Science 4 4%
Other 17 15%
Unknown 18 16%