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A model reduction method for biochemical reaction networks

Overview of attention for article published in BMC Systems Biology, May 2014
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Mentioned by

twitter
4 tweeters

Citations

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57 Dimensions

Readers on

mendeley
84 Mendeley
citeulike
2 CiteULike
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Title
A model reduction method for biochemical reaction networks
Published in
BMC Systems Biology, May 2014
DOI 10.1186/1752-0509-8-52
Pubmed ID
Authors

Shodhan Rao, Arjan van der Schaft, Karen van Eunen, Barbara M Bakker, Bayu Jayawardhana

Abstract

In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network. It does not rely on prior knowledge of the dynamic behaviour of the network and hence can be automated, as we demonstrate. The reduced network has fewer complexes, reactions, variables and parameters as compared to the original network, and yet the behaviour of a preselected set of significant metabolites in the reduced network resembles that of the original network. Moreover the reduced network largely retains the structure and kinetics of the original model.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Netherlands 1 1%
Brazil 1 1%
Germany 1 1%
United Kingdom 1 1%
India 1 1%
Spain 1 1%
Singapore 1 1%
Unknown 75 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 32%
Researcher 17 20%
Student > Master 12 14%
Student > Doctoral Student 5 6%
Student > Bachelor 2 2%
Other 7 8%
Unknown 14 17%
Readers by discipline Count As %
Engineering 16 19%
Agricultural and Biological Sciences 13 15%
Biochemistry, Genetics and Molecular Biology 11 13%
Mathematics 8 10%
Computer Science 6 7%
Other 12 14%
Unknown 18 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 26 November 2015.
All research outputs
#11,451,302
of 18,804,592 outputs
Outputs from BMC Systems Biology
#526
of 1,131 outputs
Outputs of similar age
#99,492
of 200,162 outputs
Outputs of similar age from BMC Systems Biology
#6
of 12 outputs
Altmetric has tracked 18,804,592 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,131 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 48th percentile – i.e., 48% 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 200,162 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.