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SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks

Overview of attention for article published in BMC Systems Biology, October 2015
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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7 X users

Citations

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29 Dimensions

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63 Mendeley
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5 CiteULike
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Title
SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks
Published in
BMC Systems Biology, October 2015
DOI 10.1186/s12918-015-0212-9
Pubmed ID
Authors

Andreas Dräger, Daniel C Zielinski, Roland Keller, Matthias Rall, Johannes Eichner, Bernhard O Palsson, Andreas Zell

Abstract

The size and complexity of published biochemical network reconstructions are steadily increasing, expanding the potential scale of derived computational models. However, the construction of large biochemical network models is a laborious and error-prone task. Automated methods have simplified the network reconstruction process, but building kinetic models for these systems is still a manually intensive task. Appropriate kinetic equations, based upon reaction rate laws, must be constructed and parameterized for each reaction. The complex test-and-evaluation cycles that can be involved during kinetic model construction would thus benefit from automated methods for rate law assignment. We present a high-throughput algorithm to automatically suggest and create suitable rate laws based upon reaction type according to several criteria. The criteria for choices made by the algorithm can be influenced in order to assign the desired type of rate law to each reaction. This algorithm is implemented in the software package SBMLsqueezer 2. In addition, this program contains an integrated connection to the kinetics database SABIO-RK to obtain experimentally-derived rate laws when desired. The described approach fills a heretofore absent niche in workflows for large-scale biochemical kinetic model construction. In several applications the algorithm has already been demonstrated to be useful and scalable. SBMLsqueezer is platform independent and can be used as a stand-alone package, as an integrated plugin, or through a web interface, enabling flexible solutions and use-case scenarios.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Denmark 2 3%
Germany 1 2%
Singapore 1 2%
United Kingdom 1 2%
Russia 1 2%
Canada 1 2%
Unknown 53 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 33%
Student > Ph. D. Student 10 16%
Student > Master 8 13%
Student > Doctoral Student 6 10%
Student > Postgraduate 3 5%
Other 8 13%
Unknown 7 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 22%
Computer Science 11 17%
Biochemistry, Genetics and Molecular Biology 9 14%
Engineering 7 11%
Chemical Engineering 3 5%
Other 9 14%
Unknown 10 16%
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 22 April 2022.
All research outputs
#7,174,980
of 23,881,329 outputs
Outputs from BMC Systems Biology
#254
of 1,126 outputs
Outputs of similar age
#84,847
of 281,273 outputs
Outputs of similar age from BMC Systems Biology
#5
of 36 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,126 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 77% 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 281,273 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 69% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.