DataEntryID 80
    General information
      Manuscript title: Metabolic regulation analysis of wild-type and arcA mutant Escherichia coli under nitrate conditions using different levels of omics data.
      PubMed ID: http://www.ncbi.nlm.nih.gov/pubmed/22790675
      Journal: Molecular BioSystems
      Year: 2012
      Authors: Yoshihiro Toya, Kenji Nakahigashi, Masaru Tomita and Kazuyuki Shimizu
      Affiliations: Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0017, Japan
      Keywords: Escherichia coli, metabolic flux distributions
      Full text article: no file uploaded
      Project name: not specified

    Experiment description
      Organism: Escherichia coli
      Strain: BW25113 and arcA mutant
      Data type: flux measurements
      Data units: (mmol/gDW.h)
      Execution date: not specified

    Experimental details
      Temperature (°C): 37.0
      pH: 7.0
      Carbon source: glucose
      Culture mode: chemostat
      Process condition: aerobic and anaerobic
      Dilution rate (h⁻¹): 0.2
      Working volume: 1.0 L
      Biomass concentration (g/L): not specified
      Medium composition: Synthetic medium for batch and continuous: 48 mM Na2HPO4, 22 mM KH2PO4, 10 mM NaCl, 45 mM (NH4)2SO4, 4 g/L glucose was supplemented with 1 mM MgSO4, 1 mg mL 1 thiamine, 0.056 mg/L  CaCl2, 0.08 mg/L FeCl3, 0.01 mg/L MnCl2.4H2O, 0.017 mg/L ZnCl2, 0.0043 mg/L CuCl2.2H2O, 0.006 mg/L CoCl2.2H2O, and 0.06 mg/L Na2MoO4.2H2O. In addition, 40 mM of potassium nitrate was added to the medium for the cultures under nitrate conditions.
      General protocol information: Type analysis list: 13C constrained MFA; Platform list: MALDI-TOF;
      Methods description: 13C-Metabolic flux analysis - The stoichiometric reaction model for glycolysis, the pentose phosphate (PP) pathway, the TCA cycle, the glyoxylate shunt and the anaplerotic reaction was constructed for the flux analysis.
The fluxes for biosynthesis were calculated based on the specific growth rate and the precursor requirements for biomass production [1].  The flux distributions were optimized by the labeling pattern computed from assumed fluxes as the best fits to the measured labeling pattern of the metabolites [2]. A genetic algorithm was combined with a sequential quadratic programming method to perform this optimization. The mass isotopomer distributions of intermediates such as FDP, DHAP, 3PG, PEP, Ru5P, R5P, CIT, and MAL were used to compute the fluxes. These mass isotopomers were quantitated using Analyst QS software (Applied Biosystems), and appropriate corrections were made for natural isotope abundances [3]. A statistical analysis was employed to evaluate the sensitivity of the flux estimations for inaccuracies in measurement. Furthermore, 20 simulated measurement data sets were generated through the addition of artificial noise. Flux estimations were independently performed using these generated data sets to calculate the 99% confidence intervals for the fluxes. All calculations were performed using MATLAB R2010a with the Global Optimization Toolbox 3.0 software (Mathworks).

----------------References-----------------
[1] M. Li, P. Y. Ho, S. Yao and K. Shimizu, J. Biotechnol., 2006, 122, 254–266. http://doi.org/cr7qpd
[2] Y. Toya, N. Ishii, K. Nakahigashi, T. Hirasawa, T. Soga, M. Tomita and K. Shimizu, Biotechnol Prog, 2010, 26, 975–992. http://doi.org/b7nbgf
[3] W. A. van Winden, C. Wittmann, E. Heinzle and J. J. Heijnen, Biotechnol. Bioeng., 2002, 80, 477–479. http://doi.org/dp966z 
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    Submission and curation
      Entered by: Administrator KiMoSys
      Created: 2013-07-08 15:29:28 UTC
      Updated: 2020-04-24 16:10:34 UTC
      Version: 0
      Status: (reviewed) 2013-12-06 17:17:38 UTC
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