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General Information info

Manuscript title Pseudo-transition Analysis Identifies the Key Regulators of Dynamic Metabolic Adaptations from Steady-State Data.
PubMed ID 27136056
Journal Cell Systems
Year 2015
Authors Luca Gerosa, Bart R.B. Haverkorn van Rijsewijk, Dimitris Christodoulou, Karl Kochanowski, Thomas S.B. Schmidt, Elad Noor, Uwe Sauer
Affiliations Institute of Molecular Systems Biology, ETH Zurich, Zurich 8093, Switzerland; Systems Biology Graduate School, Zurich 8057, Switzerland.
Keywords computational biology; metabolism; metabolomics; regulation network; transcription factor
Full text article Downloadarticle Gerosa_2015.pdf
Project name not specified

Experiment Description info

Organism Escherichia coli
Strain BW25113
Data type flux measurements
Data units mmol/gh
Execution date not specified

Experimental Details info

Temperature (0C) 37
pH not specified
Carbon source acetate, fructose, galactose, glucose, glycerol, gluconate, pyruvate, succinate
Culture mode batch
Process condition aerobic
Dilution rate (h-1)
Working volume (L) 0.0035
Biomass concentration (g/L) see worksheet
Medium composition

LB cultures were used to inoculate M9 medium precultures with the indicated carbon sources for overnight cultivation.

General protocol information Flux analysis method: 13C constrained MFA

Platform: LC-MS

Methods description - Notes

Metabolic flux analysis
For steady state analyses, separate 13C-labeling experiments were performed with a mixture of 20% (wt/wt) [U-13C] labeled isotopologue (>99%; Cambridge Isotope Laboratories, Andover, MA) and 80% (wt/wt) of natural abundance carbon sources. Separate 13C-labeling experiments were performed with 100% [1-13C]galactose, [1-13C]glucose, [1-13C]gluconate and [1-13C]fructose (>99%; Cambridge Isotope Laboratories, Andover, MA) and [1,3-13C]glycerol (>99%; CortecNet Voisins-Le-Bretonneux, France). Aliquots of fractionally 13C-labelled biomass were prepared from exponentially growing cultures and analyzed by gas chromatography mass spectrometry (GC-MS) [1].
Estimation of absolute fluxes was done by whole isotopologue balancing [2,3], using cumomer balances and cumomer to isotopologue mapping matrices [4] to calculate isotopologue partitioning of metabolites in a pre-defined stoichiometric network model for a given flux set. The flux set giving the best correspondence between measured and simulated 13C-label partitioning and physiology measurements of growth and extracellular fluxes was determined by non-linear optimization and selected as the final flux distribution. Standard deviations for metabolic fluxes were estimated through Monte Carlo simulations by re-estimating fluxes after adding Gaussian noise to the measured 13C-labeling data [5]. --------------------------------------------References---------------------------------------
[1] Zamboni, N., Fendt, S.-M., Rühl, M., and Sauer, U. (2009). Nat. Protoc. 4, 878–892.
[2] Kleijn, R.J., van Winden, W.A., van Gulik, W.M., and Heijnen, J.J. (2005). FEBS J. 272, 4970–4982.
[3] Van Winden, W.A., van Dam, J.C., Ras, C., Kleijn, R.J., Vinke, J.L., van Gulik, W.M., and Heijnen, J.J. (2005). FEMS Yeast Res. 5, 559–568.
[4] Wiechert, W., Möllney, M., Isermann, N., Wurzel, M., and de Graaf, A.A. (1999). Biotechnol. Bioeng. 66, 69–85.
[5] Schmidt, K., Nielsen, J., and Villadsen, J. (1999). J. Biotechnol. 71, 175–189.

Data file
Downloadfluxes KIMODATAID125_v2.xlsx
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Submission and curation info

Entered by Administrator KiMoSysFirst name: Administrator
Affiliation: INESC-ID/IST
Interests: mathematical modeling, accessible data, use of data

Created 2018-09-25 22:32:33 UTC

Updated 2018-09-25 22:34:51 UTC

Version 2

Status (reviewed) 2018-09-25 22:33:52 UTC

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