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General Information info

Manuscript title Transcriptional regulation is insufficient to explain substrate-induced flux changes in Bacillus subtilis.
PubMed ID 24281055
Journal Molecular Systems Biology
Year 2013
Authors Victor Chubukov, Markus Uhr, Ludovic Le Chat, Roelco J Kleijn, Matthieu Jules, Hannes Link, Stephane Aymerich, Joerg Stelling and Uwe Sauer
Affiliations Institute of Molecular System Biology, ETH Zurich, Zurich, Switzerland.
Keywords central carbon metabolism, metabolic flux, transcriptional regulation
Full text article Downloadarticle Chubukov_2013.pdf
Project name not specified

Experiment Description info

Organism Bacillus subtilis
Strain BSB168
Data type flux measurements
Data units mmol/g/h
Execution date not specified

Experimental Details info

Temperature (0C) 37.0
pH
Carbon source glucose, fructose, gluconate, succinate+glutamate, glycerol, malate, malate, malate+glucose, pyruvate
Culture mode batch
Process condition aerobic
Dilution rate (h-1)
Working volume (L) 0.03
Biomass concentration (g/L) 3.0 g cells/OD600
Medium composition

M9 minimal medium (per liter): 8.5 g Na2HPO4·2H2O, 3 g KH2PO4, 1 g NH4Cl, 0.5 g NaCl. The following components were sterilized separately and then added (per liter of final medium): 1 ml 0.1 M CaCl2, 1 ml 1 M MgSO4, 1 ml 50 mM FeCl3 and 10 ml trace salts solution. The trace salts solution contained (per liter): 170 mg ZnCl2, 100 mg MnCl2·4H2O, 60.0 mg CoCl2·6H2O, 60.0 mg Na2MoO4·2H2O and 43.0 mg CuCl2·2H2O. Filter‐sterilized carbon sources were added separately to the medium, pH neutralized with 4 M NaOH where necessary.

General protocol information Flux analysis method: 13C constrained MFA

Platform: GC-MS

Methods description - Notes

Extracellular substrate and byproduct concentrations were measured by HPLC analysis using an Agilent 1100 series HPLC stack (Agilent Technologies, Waldbronn, Germany) in combination with an Aminex HPX‐87H polymer column (Bio‐Rad, Hercules, CA, USA). Sugars were detected with a refractive index detector and organic acids with an UV/Vis detector. Substrate or product yields were calculated by linear regression of external concentration against biomass, and specific rates were calculated as yield multiplied by the growth rate. At least five time points during the exponential growth phase were used for the regression analysis. Cell growth was monitored photometrically at 600 nm and cell dry weight was inferred from a predetermined conversion factor of 0.48 g cells/OD600 [1]. All measurement errors for physiological parameters are reported as the standard deviation of 2–3 biological replicates.
Metabolic flux analysis

Biomass sample processing and GC‐MS analysis to determine isotopomer fractions of proteinogenic amino acids was performed as previously described [2]. Stoichiometric network models were based on a core model containing the reactions of central carbon metabolism [3]. When unconstrained by labeling information, futile cycle fluxes were set to zero. The growth rate‐dependent biomass requirements of B. subtilis were previously established [4] and added to the network as unidirectional biomass precursor withdrawing reactions. Metabolic fluxes were derived using the whole isotopomer modeling approach [5]. The procedure uses the cumomer balances and cumomer to isotopomer mapping matrices [6] to calculate the isotopomer distributions of metabolites in a predefined stoichiometric network model for a given flux set. The flux set that gives the best correspondence between the measured and simulated 13C‐label distribution is determined by non‐linear optimization and denoted as the optimal flux fit. All calculations were performed in Matlab 7.6.0 (The Mathworks Inc, Natick, MA, USA). --------------------------------------------References---------------------------------------
[1] Tännler S, Decasper S, Sauer U (2008). Microb Cell Fact 7: 19. http://doi.org/cjbk3x
[2] Zamboni N, Fendt S‐M, Rühl M, Sauer U (2009). Nat Protoc 4: 878–892. http://doi.org/b8ck9w
[3] Oh Y‐K, Palsson BO, Park SM, Schilling CH, Mahadevan R (2007). J Biol Chem 282: 28791–28799. http://doi.org/bvbtsq
[4] Dauner M, Storni T, Sauer U (2001). J Bacteriol 183: 7308–7317. http://doi.org/cs9gqv
[5] Van Winden WA, van Dam JC, Ras C, Kleijn RJ, Vinke JL, van Gulik WM, Heijnen JJ (2005). FEMS Yeast Res 5: 559–568. http://doi.org/cgg3tw
[6] Wiechert W, Möllney M, Isermann N, Wurzel M, de Graaf AA (1999). Biotechnol Bioeng 66: 69–85. http://doi.org/ctxdtx

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Entered by Administrator KiMoSysFirst name: Administrator
Affiliation: INESC-ID/IST
Homepage: http://kdbio.inesc-id.pt/kimosys
Interests: mathematical modeling, accessible data, use of data

Created 2015-05-12 15:24:47 UTC

Updated 2015-05-12 15:38:58 UTC

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Status (reviewed) 2015-05-12 15:46:59 UTC




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