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Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters

Overview of attention for article published in BMC Systems Biology, January 2014
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1 tweeter

Citations

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Readers on

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Title
Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters
Published in
BMC Systems Biology, January 2014
DOI 10.1186/1752-0509-8-4
Pubmed ID
Authors

Austin WT Chiang, Wei-Chung Liu, Pep Charusanti, Ming-Jing Hwang

Abstract

A major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to systems dynamics. As biological processes are often complex in nature, it is desirable to address this issue using a systematic approach. Here, we propose a simple methodology that first performs an enrichment test to find patterns in the values of globally profiled kinetic parameters with which a model can produce the required system dynamics; this is then followed by a statistical test to elucidate the association between individual parameters and different parts of the system's dynamics.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 7%
India 1 2%
Denmark 1 2%
Singapore 1 2%
Unknown 39 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 29%
Student > Ph. D. Student 13 29%
Student > Master 3 7%
Student > Bachelor 3 7%
Student > Doctoral Student 3 7%
Other 7 16%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 22%
Biochemistry, Genetics and Molecular Biology 10 22%
Physics and Astronomy 6 13%
Computer Science 5 11%
Engineering 3 7%
Other 6 13%
Unknown 5 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 16 January 2014.
All research outputs
#3,063,574
of 4,507,509 outputs
Outputs from BMC Systems Biology
#430
of 670 outputs
Outputs of similar age
#79,369
of 120,785 outputs
Outputs of similar age from BMC Systems Biology
#29
of 52 outputs
Altmetric has tracked 4,507,509 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 670 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
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We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.