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Optimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle model

Overview of attention for article published in BMC Systems Biology, June 2013
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Title
Optimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle model
Published in
BMC Systems Biology, June 2013
DOI 10.1186/1752-0509-7-53
Pubmed ID
Authors

Cihan Oguz, Teeraphan Laomettachit, Katherine C Chen, Layne T Watson, William T Baumann, John J Tyson

Abstract

Parameter estimation from experimental data is critical for mathematical modeling of protein regulatory networks. For realistic networks with dozens of species and reactions, parameter estimation is an especially challenging task. In this study, we present an approach for parameter estimation that is effective in fitting a model of the budding yeast cell cycle (comprising 26 nonlinear ordinary differential equations containing 126 rate constants) to the experimentally observed phenotypes (viable or inviable) of 119 genetic strains carrying mutations of cell cycle genes.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
United Kingdom 2 3%
Portugal 1 2%
Italy 1 2%
Malaysia 1 2%
Canada 1 2%
Netherlands 1 2%
Unknown 49 83%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 31%
Researcher 12 20%
Student > Master 6 10%
Student > Doctoral Student 5 8%
Student > Bachelor 4 7%
Other 9 15%
Unknown 5 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 24%
Biochemistry, Genetics and Molecular Biology 8 14%
Engineering 8 14%
Computer Science 7 12%
Mathematics 3 5%
Other 10 17%
Unknown 9 15%