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000057502 0247_ $$2DOI$$a10.1016/j.jbiotec.2006.11.015
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000057502 041__ $$aeng
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000057502 084__ $$2WoS$$aBiotechnology & Applied Microbiology
000057502 1001_ $$0P:(DE-Juel1)129051$$aNöh, K.$$b0$$uFZJ
000057502 245__ $$aMetabolic flux analysis at ultra short time scale: Isotopically non-stationary 13C labeling experiments
000057502 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2007
000057502 300__ $$a249 - 267
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000057502 440_0 $$03109$$aJournal of Biotechnology$$v129$$x0168-1656$$y2
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000057502 520__ $$aA novel approach to (13)C metabolic flux analysis (MFA) is presented using cytosolic metabolite pool sizes and their (13)C labeling data from an isotopically non-stationary (13)C labeling experiment (INST-CLE). The procedure is demonstrated with an E. coli wild type strain grown at fed batch conditions. The intra cellular labeling dynamics are excited by a sudden step increase of the (13)C portion in the substrate feed. Due to unchanged saturation of the substrate uptake system, the metabolic fluxes remain constant during the following sampling time period of only 16s, in which 20 samples are taken by an automated rapid sampling device immediately stopping metabolism by methanol quenching. Subsequent cell disruptive sample preparation and LC-MS/MS enabled simultaneous determination of pool sizes and mass isotopomers of intra cellular metabolites requiring detection limits in the nM range. Based on this data the new computational flux analysis tool 13CFLUX/INST is used to determine the intra cellular fluxes based on a complex carbon labeling network model. The measured data is in good agreement with the model predictions, thus proving the applicability of the new isotopically non-stationary (13)C metabolic flux analysis (INST-(13)C-MFA) concept. Moreover, it is shown that significant new information with respect to flux identifiability, non-measurable pool sizes, data consistency, or large storage pools can be taken from the novel kind of experimental data. This offers new insight into the biological operation of the metabolic network in vivo.
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000057502 650_2 $$2MeSH$$aBioreactors
000057502 650_2 $$2MeSH$$aCarbon: metabolism
000057502 650_2 $$2MeSH$$aCarbon Isotopes: metabolism
000057502 650_2 $$2MeSH$$aChromatography, Liquid
000057502 650_2 $$2MeSH$$aComputational Biology: methods
000057502 650_2 $$2MeSH$$aComputer Simulation
000057502 650_2 $$2MeSH$$aEscherichia coli: metabolism
000057502 650_2 $$2MeSH$$aFermentation: physiology
000057502 650_2 $$2MeSH$$aMetabolic Networks and Pathways: physiology
000057502 650_2 $$2MeSH$$aModels, Biological
000057502 650_2 $$2MeSH$$aTandem Mass Spectrometry
000057502 650_7 $$00$$2NLM Chemicals$$aCarbon Isotopes
000057502 650_7 $$07440-44-0$$2NLM Chemicals$$aCarbon
000057502 650_7 $$2WoSType$$aJ
000057502 65320 $$2Author$$ametabolic flux analysis
000057502 65320 $$2Author$$aisotopically non-stationary C-13 labeling experiments
000057502 65320 $$2Author$$ametabolomics
000057502 65320 $$2Author$$acomputational biology
000057502 65320 $$2Author$$ametabolic engineering
000057502 7001_ $$0P:(DE-Juel1)VDB32585$$aGrönke, K.$$b1$$uFZJ
000057502 7001_ $$0P:(DE-Juel1)VDB8775$$aLuo, B.$$b2$$uFZJ
000057502 7001_ $$0P:(DE-HGF)0$$aTakors, R.$$b3
000057502 7001_ $$0P:(DE-Juel1)129053$$aOldiges, M.$$b4$$uFZJ
000057502 7001_ $$0P:(DE-HGF)0$$aWiechert, W.$$b5
000057502 773__ $$0PERI:(DE-600)2016476-2$$a10.1016/j.jbiotec.2006.11.015$$gVol. 129, p. 249 - 267$$p249 - 267$$q129<249 - 267$$tJournal of biotechnology$$v129$$x0168-1656$$y2007
000057502 8567_ $$uhttp://dx.doi.org/10.1016/j.jbiotec.2006.11.015
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000057502 9141_ $$y2007
000057502 915__ $$0StatID:(DE-HGF)0010$$aJCR/ISI refereed
000057502 9201_ $$0I:(DE-Juel1)VDB56$$gIBT$$kIBT-2$$lBiotechnologie 2$$x0$$zab 31.10.10 weitergeführt IBG-1
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