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A rule-based model of insulin signalling pathway

Overview of attention for article published in BMC Systems Biology, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)

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9 tweeters
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1 Facebook page

Citations

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17 Dimensions

Readers on

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66 Mendeley
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3 CiteULike
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Title
A rule-based model of insulin signalling pathway
Published in
BMC Systems Biology, June 2016
DOI 10.1186/s12918-016-0281-4
Pubmed ID
Authors

Barbara Di Camillo, Azzurra Carlon, Federica Eduati, Gianna Maria Toffolo

Abstract

The insulin signalling pathway (ISP) is an important biochemical pathway, which regulates some fundamental biological functions such as glucose and lipid metabolism, protein synthesis, cell proliferation, cell differentiation and apoptosis. In the last years, different mathematical models based on ordinary differential equations have been proposed in the literature to describe specific features of the ISP, thus providing a description of the behaviour of the system and its emerging properties. However, protein-protein interactions potentially generate a multiplicity of distinct chemical species, an issue referred to as "combinatorial complexity", which results in defining a high number of state variables equal to the number of possible protein modifications. This often leads to complex, error prone and difficult to handle model definitions. In this work, we present a comprehensive model of the ISP, which integrates three models previously available in the literature by using the rule-based modelling (RBM) approach. RBM allows for a simple description of a number of signalling pathway characteristics, such as the phosphorylation of signalling proteins at multiple sites with different effects, the simultaneous interaction of many molecules of the signalling pathways with several binding partners, and the information about subcellular localization where reactions take place. Thanks to its modularity, it also allows an easy integration of different pathways. After RBM specification, we simulated the dynamic behaviour of the ISP model and validated it using experimental data. We the examined the predicted profiles of all the active species and clustered them in four clusters according to their dynamic behaviour. Finally, we used parametric sensitivity analysis to show the role of negative feedback loops in controlling the robustness of the system. The presented ISP model is a powerful tool for data simulation and can be used in combination with experimental approaches to guide the experimental design. The model is available at http://sysbiobig.dei.unipd.it/ was submitted to Biomodels Database ( https://www.ebi.ac.uk/biomodels-main/ # MODEL 1604100005).

Twitter Demographics

The data shown below were collected from the profiles of 9 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 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 2%
United States 1 2%
Russia 1 2%
Unknown 63 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 23%
Student > Master 11 17%
Student > Bachelor 8 12%
Researcher 7 11%
Student > Postgraduate 7 11%
Other 10 15%
Unknown 8 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 28 42%
Agricultural and Biological Sciences 7 11%
Engineering 6 9%
Computer Science 2 3%
Mathematics 2 3%
Other 12 18%
Unknown 9 14%

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 14 April 2021.
All research outputs
#4,090,182
of 20,793,985 outputs
Outputs from BMC Systems Biology
#136
of 1,137 outputs
Outputs of similar age
#66,744
of 280,548 outputs
Outputs of similar age from BMC Systems Biology
#1
of 4 outputs
Altmetric has tracked 20,793,985 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,137 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 88% 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 280,548 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 76% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them