Title |
Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach
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Published in |
BMC Systems Biology, February 2014
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DOI | 10.1186/1752-0509-8-13 |
Pubmed ID | |
Authors |
Pablo Meyer, Thomas Cokelaer, Deepak Chandran, Kyung Hyuk Kim, Po-Ru Loh, George Tucker, Mark Lipson, Bonnie Berger, Clemens Kreutz, Andreas Raue, Bernhard Steiert, Jens Timmer, Erhan Bilal, Herbert M Sauro, Gustavo Stolovitzky, Julio Saez-Rodriguez |
Abstract |
Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Poland | 1 | 13% |
United Kingdom | 1 | 13% |
Germany | 1 | 13% |
Unknown | 5 | 63% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 4 | 50% |
Members of the public | 4 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 3% |
United States | 4 | 3% |
Germany | 3 | 2% |
Spain | 2 | 1% |
Portugal | 1 | <1% |
Unknown | 145 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 54 | 34% |
Student > Ph. D. Student | 40 | 25% |
Student > Master | 13 | 8% |
Professor > Associate Professor | 9 | 6% |
Professor | 8 | 5% |
Other | 27 | 17% |
Unknown | 8 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 59 | 37% |
Biochemistry, Genetics and Molecular Biology | 23 | 14% |
Computer Science | 15 | 9% |
Mathematics | 15 | 9% |
Engineering | 11 | 7% |
Other | 25 | 16% |
Unknown | 11 | 7% |