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Emergence of Switch-Like Behavior in a Large Family of Simple Biochemical Networks

Overview of attention for article published in PLoS Computational Biology, May 2011
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2 Wikipedia pages

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48 Dimensions

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104 Mendeley
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3 CiteULike
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Title
Emergence of Switch-Like Behavior in a Large Family of Simple Biochemical Networks
Published in
PLoS Computational Biology, May 2011
DOI 10.1371/journal.pcbi.1002039
Pubmed ID
Authors

Dan Siegal-Gaskins, Maria Katherine Mejia-Guerra, Gregory D. Smith, Erich Grotewold

Abstract

Bistability plays a central role in the gene regulatory networks (GRNs) controlling many essential biological functions, including cellular differentiation and cell cycle control. However, establishing the network topologies that can exhibit bistability remains a challenge, in part due to the exceedingly large variety of GRNs that exist for even a small number of components. We begin to address this problem by employing chemical reaction network theory in a comprehensive in silico survey to determine the capacity for bistability of more than 40,000 simple networks that can be formed by two transcription factor-coding genes and their associated proteins (assuming only the most elementary biochemical processes). We find that there exist reaction rate constants leading to bistability in ∼90% of these GRN models, including several circuits that do not contain any of the TF cooperativity commonly associated with bistable systems, and the majority of which could only be identified as bistable through an original subnetwork-based analysis. A topological sorting of the two-gene family of networks based on the presence or absence of biochemical reactions reveals eleven minimal bistable networks (i.e., bistable networks that do not contain within them a smaller bistable subnetwork). The large number of previously unknown bistable network topologies suggests that the capacity for switch-like behavior in GRNs arises with relative ease and is not easily lost through network evolution. To highlight the relevance of the systematic application of CRNT to bistable network identification in real biological systems, we integrated publicly available protein-protein interaction, protein-DNA interaction, and gene expression data from Saccharomyces cerevisiae, and identified several GRNs predicted to behave in a bistable fashion.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 6%
Japan 2 2%
Germany 2 2%
France 2 2%
United Kingdom 2 2%
Sweden 1 <1%
Hungary 1 <1%
Portugal 1 <1%
India 1 <1%
Other 1 <1%
Unknown 85 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 34%
Researcher 34 33%
Student > Bachelor 7 7%
Professor > Associate Professor 6 6%
Professor 5 5%
Other 13 13%
Unknown 4 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 44%
Computer Science 10 10%
Biochemistry, Genetics and Molecular Biology 8 8%
Engineering 8 8%
Physics and Astronomy 7 7%
Other 14 13%
Unknown 11 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 October 2014.
All research outputs
#8,540,769
of 25,385,509 outputs
Outputs from PLoS Computational Biology
#5,638
of 8,961 outputs
Outputs of similar age
#45,412
of 121,332 outputs
Outputs of similar age from PLoS Computational Biology
#30
of 56 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,961 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 121,332 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.