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Modeling Networks of Coupled Enzymatic Reactions Using the Total Quasi-Steady State Approximation

Overview of attention for article published in PLoS Computational Biology, March 2007
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

blogs
1 blog

Citations

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153 Dimensions

Readers on

mendeley
149 Mendeley
citeulike
8 CiteULike
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1 Connotea
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Title
Modeling Networks of Coupled Enzymatic Reactions Using the Total Quasi-Steady State Approximation
Published in
PLoS Computational Biology, March 2007
DOI 10.1371/journal.pcbi.0030045
Pubmed ID
Authors

Andrea Ciliberto, Fabrizio Capuani, John J Tyson

Abstract

In metabolic networks, metabolites are usually present in great excess over the enzymes that catalyze their interconversion, and describing the rates of these reactions by using the Michaelis-Menten rate law is perfectly valid. This rate law assumes that the concentration of enzyme-substrate complex (C) is much less than the free substrate concentration (S0). However, in protein interaction networks, the enzymes and substrates are all proteins in comparable concentrations, and neglecting C with respect to S0 is not valid. Borghans, DeBoer, and Segel developed an alternative description of enzyme kinetics that is valid when C is comparable to S0. We extend this description, which Borghans et al. call the total quasi-steady state approximation, to networks of coupled enzymatic reactions. First, we analyze an isolated Goldbeter-Koshland switch when enzymes and substrates are present in comparable concentrations. Then, on the basis of a real example of the molecular network governing cell cycle progression, we couple two and three Goldbeter-Koshland switches together to study the effects of feedback in networks of protein kinases and phosphatases. Our analysis shows that the total quasi-steady state approximation provides an excellent kinetic formalism for protein interaction networks, because (1) it unveils the modular structure of the enzymatic reactions, (2) it suggests a simple algorithm to formulate correct kinetic equations, and (3) contrary to classical Michaelis-Menten kinetics, it succeeds in faithfully reproducing the dynamics of the network both qualitatively and quantitatively.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 6%
United Kingdom 4 3%
Germany 3 2%
Turkey 1 <1%
Netherlands 1 <1%
Switzerland 1 <1%
France 1 <1%
Italy 1 <1%
Australia 1 <1%
Other 5 3%
Unknown 122 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 32%
Researcher 38 26%
Professor > Associate Professor 15 10%
Student > Bachelor 9 6%
Professor 8 5%
Other 22 15%
Unknown 10 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 38%
Biochemistry, Genetics and Molecular Biology 16 11%
Physics and Astronomy 14 9%
Mathematics 11 7%
Chemistry 10 7%
Other 27 18%
Unknown 14 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 18 June 2010.
All research outputs
#5,446,359
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#4,149
of 8,960 outputs
Outputs of similar age
#17,456
of 90,726 outputs
Outputs of similar age from PLoS Computational Biology
#12
of 28 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 53% of its peers.
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 90,726 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.