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Dynamic Modelling under Uncertainty: The Case of Trypanosoma brucei Energy Metabolism

Overview of attention for article published in PLoS Computational Biology, January 2012
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Title
Dynamic Modelling under Uncertainty: The Case of Trypanosoma brucei Energy Metabolism
Published in
PLoS Computational Biology, January 2012
DOI 10.1371/journal.pcbi.1002352
Pubmed ID
Authors

Fiona Achcar, Eduard J. Kerkhoven, Barbara M. Bakker, Michael P. Barrett, Rainer Breitling

Abstract

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies.

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Mendeley readers

The data shown below were compiled from readership statistics for 127 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 2%
Germany 2 2%
France 2 2%
Netherlands 2 2%
Canada 2 2%
United States 2 2%
Czechia 1 <1%
Chile 1 <1%
Thailand 1 <1%
Other 1 <1%
Unknown 110 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 27%
Researcher 32 25%
Student > Master 11 9%
Student > Bachelor 8 6%
Professor 7 6%
Other 23 18%
Unknown 12 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 52 41%
Biochemistry, Genetics and Molecular Biology 21 17%
Computer Science 9 7%
Mathematics 5 4%
Medicine and Dentistry 5 4%
Other 19 15%
Unknown 16 13%