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A quantitative approach to catabolite repression in Escherichia coli

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Bettenbrock,  K.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Fischer,  Sophia
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Kremling,  A.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Sauter,  T.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Gilles,  E. D.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Citation

Bettenbrock, K., Fischer, S., Kremling, A., Jahreis, K., Sauter, T., & Gilles, E. D. (2006). A quantitative approach to catabolite repression in Escherichia coli. Journal of Biological Chemistry, 281, 2578-2584. doi:10.1074/jbc.M508090200.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-9AA2-A
Abstract
A dynamic mathematical model was developed to describe the uptake of various carbohydrates (glucose, lactose, glycerol, sucrose and galactose) in E.coli. For validation a number of isogenic strains with defined mutations were used. By considering metabolic reactions as well as signal transduction processes influencing the relevant pathways, we were able to describe quantitatively the phenomenon of catabolite repression in E.coli. We verified model predictions by measuring time courses of several extra- and intracellular components such as glycolytic intermediates, EIIA^Crr phosphorylation level, both LacZ and PtsG concentrations and total cAMP concentrations under various growth conditions. The entire database consists of 18 experiments performed with 9 different strains. The model describes the expression of 17 key enzymes, 38 enzymatic reactions and the dynamic behavior of more than 50 metabolites. The different phenomena effecting the EIIA^Crr phosphorylation level, the key regulation molecule for inducer exclusion and catabolite repression in enteric bacteria, can now be explained quantitatively. Copyright © 2015 American Society for Biochemistry and Molecular Biology [accessed 2015 July 8]