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
Flux analysis method:
13C constrained MFA
Platform:
MALDI-TOF
Methods description - Notes
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
Metabolic regulation analysis of wild-type and arcA mutant Escherichia coli under nitrate conditions using different levels of omics data.
PubMed ID
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
Project name
not specified
Experiment Description
Organism
Escherichia coli
Strain
BW25113 and arcA mutant
Data type
flux measurements
Data units
(mmol/gDCW.h)
Execution date
not specified
Experimental Details
Temperature (0C)
37
pH
7.0
Carbon source
glucose
Culture mode
chemostat
Process condition
aerobic and anaerobic
Dilution rate (h-1)
0.2
Working volume (L)
1.0
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
Flux analysis method: 13C constrained MFA
Platform: MADLI-TOF
Methods description - Notes
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.
[2] Y. Toya, N. Ishii, K. Nakahigashi, T. Hirasawa, T. Soga, M. Tomita and K. Shimizu, Biotechnol Prog, 2010, 26, 975–992.
[3] W. A. van Winden, C. Wittmann, E. Heinzle and J. J. Heijnen, Biotechnol. Bioeng., 2002, 80, 477–479.
KiMoSys (https://kimosys.org). Data EntryID 80 (Escherichia coli). [online], [Accessed 21 November 2024]. Available from: https://doi.org/10.34619/xabb-1845
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