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  13C labeling experiments at metabolic nonstationary conditions : an exploratory study

Wahl, S. A., Nöh, K., & Wiechert, W. (2008). 13C labeling experiments at metabolic nonstationary conditions: an exploratory study. BMC Bioinformatics, 9, 152. doi:10.1186/1471-2105-9-152.

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eDoc_322608_2008.pdf (Publisher version), 728KB
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Wahl, S. A.1, Author           
Nöh, K.2, Author
Wiechert, W.3, Author
Affiliations:
1Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738140              
2Research Centre Jülich GmbH, Institute of Biotechnology 2, Department of Fermentation Technology, Jülich, Germany , ou_persistent22              
3University of Siegen, Department of Simulation, Institute of Systems Engineering, Siegen, Germany , ou_persistent22              

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 Abstract: Stimulus Response Experiments to unravel the regulatory properties of metabolic networks are becoming more and more popular. However, their ability to determine enzyme kinetic parameters have proven to be limited with the presently available data. Different approaches are sought to circumvent this lack of information. In this contribution, a novel territory is entered based on the idea of increasing the information content of the dynamic experiment by adding 13C labeling. Labeling techniques have been shown to convey valuable constraints for stationary Flux Analysis. To explore whether 13C labeling will also increase the information content for kinetic parameter identification, an example network is studied. Different scenarios regarding available measurements are analyzed and compared to a non-labeled reference experiment. Sensitivity analysis and statistical methods are applied to assess the information gain of the new experimental approach. It was found that labeling will significantly increase the parameter accuracy. An overall information gain of about a factor 6 was observed for the example network. © 2008 Wahl et al; licensee BioMed Central Ltd. [accessed June 6th, 2008]

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Language(s): eng - English
 Dates: 2008
 Publication Status: Issued
 Pages: -
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 Rev. Type: Peer
 Identifiers: eDoc: 322608
DOI: 10.1186/1471-2105-9-152
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Title: BMC Bioinformatics
Source Genre: Journal
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Publ. Info: BioMed Central
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 152 Identifier: ISSN: 1471-2105
CoNE: https://pure.mpg.de/cone/journals/resource/111000136905000