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ON/OFF and Beyond - A Boolean Model of Apoptosis

Overview of attention for article published in PLoS Computational Biology, December 2009
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Title
ON/OFF and Beyond - A Boolean Model of Apoptosis
Published in
PLoS Computational Biology, December 2009
DOI 10.1371/journal.pcbi.1000595
Pubmed ID
Authors

Rebekka Schlatter, Kathrin Schmich, Ima Avalos Vizcarra, Peter Scheurich, Thomas Sauter, Christoph Borner, Michael Ederer, Irmgard Merfort, Oliver Sawodny

Abstract

Apoptosis is regulated by several signaling pathways which are extensively linked by crosstalks. Boolean or logical modeling has become a promising approach to capture the qualitative behavior of such complex networks. Here we built a large-scale literature-based Boolean model of the central intrinsic and extrinsic apoptosis pathways as well as pathways connected with them. The model responds to several external stimuli such as Fas ligand, TNF-alpha, UV-B irradiation, interleukin-1beta and insulin. Timescales and multi-value node logic were used and turned out to be indispensable to reproduce the behavior of the apoptotic network. The coherence of the model was experimentally validated. Thereby an UV-B dose-effect is shown for the first time in mouse hepatocytes. Analysis of the model revealed a tight regulation emerging from high connectivity and spanning crosstalks and a particular importance of feedback loops. An unexpected feedback from Smac release to RIP could further increase complex II formation. The introduced Boolean model provides a comprehensive and coherent description of the apoptosis network behavior. It gives new insights into the complex interplay of pro- and antiapoptotic factors and can be easily expanded to other signaling pathways.

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Mendeley readers

The data shown below were compiled from readership statistics for 180 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 8 4%
United States 6 3%
France 2 1%
United Kingdom 2 1%
Portugal 1 <1%
Norway 1 <1%
Sweden 1 <1%
Romania 1 <1%
Luxembourg 1 <1%
Other 0 0%
Unknown 157 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 51 28%
Researcher 43 24%
Student > Master 25 14%
Professor > Associate Professor 11 6%
Student > Bachelor 10 6%
Other 29 16%
Unknown 11 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 85 47%
Computer Science 21 12%
Biochemistry, Genetics and Molecular Biology 18 10%
Engineering 12 7%
Medicine and Dentistry 7 4%
Other 20 11%
Unknown 17 9%