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Dynamic simulation of regulatory networks using SQUAD

Overview of attention for article published in BMC Bioinformatics, November 2007
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1 tweeter

Citations

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113 Dimensions

Readers on

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141 Mendeley
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2 CiteULike
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Title
Dynamic simulation of regulatory networks using SQUAD
Published in
BMC Bioinformatics, November 2007
DOI 10.1186/1471-2105-8-462
Pubmed ID
Authors

Alessandro Di Cara, Abhishek Garg, Giovanni De Micheli, Ioannis Xenarios, Luis Mendoza

Abstract

The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology.

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 141 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 4 3%
Luxembourg 2 1%
Netherlands 2 1%
Switzerland 1 <1%
France 1 <1%
Cuba 1 <1%
Spain 1 <1%
Portugal 1 <1%
Unknown 128 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 39 28%
Student > Ph. D. Student 38 27%
Student > Master 18 13%
Professor > Associate Professor 11 8%
Student > Bachelor 10 7%
Other 17 12%
Unknown 8 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 75 53%
Biochemistry, Genetics and Molecular Biology 19 13%
Computer Science 13 9%
Engineering 7 5%
Medicine and Dentistry 5 4%
Other 12 9%
Unknown 10 7%

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 17 September 2014.
All research outputs
#3,051,810
of 4,507,652 outputs
Outputs from BMC Bioinformatics
#2,220
of 2,646 outputs
Outputs of similar age
#74,596
of 118,302 outputs
Outputs of similar age from BMC Bioinformatics
#90
of 110 outputs
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