Title |
SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools
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Published in |
BMC Systems Biology, December 2013
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DOI | 10.1186/1752-0509-7-135 |
Pubmed ID | |
Authors |
Claudine Chaouiya, Duncan Bérenguier, Sarah M Keating, Aurélien Naldi, Martijn P van Iersel, Nicolas Rodriguez, Andreas Dräger, Finja Büchel, Thomas Cokelaer, Bryan Kowal, Benjamin Wicks, Emanuel Gonçalves, Julien Dorier, Michel Page, Pedro T Monteiro, Axel von Kamp, Ioannis Xenarios, Hidde de Jong, Michael Hucka, Steffen Klamt, Denis Thieffry, Nicolas Le Novère, Julio Saez-Rodriguez, Tomáš Helikar |
Abstract |
Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 25% |
United Kingdom | 2 | 13% |
France | 1 | 6% |
Germany | 1 | 6% |
Portugal | 1 | 6% |
Unknown | 7 | 44% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 11 | 69% |
Scientists | 5 | 31% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 6 | 3% |
United Kingdom | 4 | 2% |
United States | 3 | 2% |
France | 1 | <1% |
Latvia | 1 | <1% |
Turkey | 1 | <1% |
Sweden | 1 | <1% |
Portugal | 1 | <1% |
Singapore | 1 | <1% |
Other | 1 | <1% |
Unknown | 170 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 55 | 29% |
Student > Ph. D. Student | 46 | 24% |
Student > Master | 23 | 12% |
Student > Bachelor | 11 | 6% |
Student > Doctoral Student | 8 | 4% |
Other | 27 | 14% |
Unknown | 20 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 54 | 28% |
Computer Science | 39 | 21% |
Biochemistry, Genetics and Molecular Biology | 33 | 17% |
Engineering | 10 | 5% |
Medicine and Dentistry | 9 | 5% |
Other | 17 | 9% |
Unknown | 28 | 15% |