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Structural bifurcation analysis in chemical reaction networks

Overview of attention for article published in Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, July 2018
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  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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7 X users

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29 Mendeley
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Article details
Title
Structural bifurcation analysis in chemical reaction networks
Published in
Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, July 2018
DOI 10.1103/physreve.98.012417
Pubmed ID
Authors
Abstract

In living cells, chemical reactions form complex networks. Dynamics arising from such networks are the origins of biological functions. We propose a mathematical method to analyze bifurcation behaviors of network systems using their structures alone. Specifically, a whole network is decomposed into subnetworks, and for each of them the bifurcation condition can be studied independently. Further, parameters inducing bifurcations and chemicals exhibiting bifurcations can be determined on the network. We illustrate our theory using hypothetical and real networks.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 6 21%
Student > Ph. D. Student 5 17%
Researcher 5 17%
Student > Doctoral Student 2 7%
Student > Bachelor 2 7%
Other 5 17%
Unknown 4 14%
Readers by discipline
Readers by discipline Count As %
Physics and Astronomy 6 21%
Chemistry 4 14%
Engineering 4 14%
Biochemistry, Genetics and Molecular Biology 3 10%
Computer Science 2 7%
Other 3 10%
Unknown 7 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 31 July 2018.
All research outputs
#11,170,732
of 34,377,743 outputs
Outputs from Physical Review E: Statistical, Nonlinear, and Soft Matter Physics
#3,708
of 24,695 outputs
Outputs of similar age
#136,976
of 371,561 outputs
Outputs of similar age from Physical Review E: Statistical, Nonlinear, and Soft Matter Physics
#52
of 356 outputs
Altmetric has tracked 34,377,743 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 24,695 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done well, scoring higher than 84% 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 371,561 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 356 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.