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General Information info

Manuscript title Metabolic network reconstruction, growth characterization and 13C-metabolic flux analysis of the extremophile Thermus thermophilus HB8
PubMed ID 24909362
Journal Metabolic Engineering
Year 2014
Authors Aditi Swarup, Jimg Lu, Kathleen C. DeWoody, Maciek R. Antoniewicz
Affiliations Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark DE 19716, USA
Keywords Extremophile, Thermophilic bacterium, Optimal growth, Metabolic network model, Isotopic labeling
Full text article Downloadarticle Swarup_2014.pdf
Project name not specified

Experiment Description info

Organism Thermus thermophilus
Strain HB8 (ATCC 27634)
Data type flux measurements
Data units % substrate uptake
Execution date not specified

Experimental Details info

Temperature (0C) 70
pH 7.9
Carbon source glucose,
Culture mode batch
Process condition aerobic
Dilution rate (h-1)
Working volume (L) 0.01
Biomass concentration (g/L) 0.39 (g/L)/OD600 cell dry weight and 25.3 g/C-mol for the molecular weight of dry biomass
Medium composition

Glucose stock solutions were prepared at 20 wt% in distilled water. The optimized minimum medium contained (per liter of medium): 0.50 g K2HPO4, 0.30 g KH2PO4, 0.50 g NH4Cl, 0.50 g NaCl, 0.20 g MgCl2.6H2O, 0.04 g CaSO4.2H2O, 40 mL of 1 M Tris, 5 mL of Wolfe׳s minerals, and 5 mL of Wolfe׳s vitamins.

General protocol information Flux analysis method: 13C constrained MFA

Platform: GC-MS

Methods description - Notes

13C-Metabolic flux analysis - 13C –MFA was performed using the Metran software [1,2], which is based on the elementary metabolite units (EMU) framework [3,4]. Fluxes were estimated by minimizing the variance-weighted sum of squared residuals (SSR) between the experimentally measured and model predicted mass isotopomer distributions of amino acids using non-linear least-squares regression [5]. Flux estimation was repeated 10 times starting with random initial values for all fluxes to find a global solution. At convergence, accurate 95% confidence intervals were computed for all estimated fluxes by evaluating the sensitivity of the minimized SSR to flux variations [5]. Metabolic network model - A metabolic network model of T. thermophilus central carbon metabolism was constructed for 13C–MFA based on KEGG and PathwayTools metabolic pathway databases [6,7,8]. The model includes all major metabolic pathways of central carbon metabolism. A lumped biomass reaction was used to describe the drain of precursor metabolites and co-factors required for cell growth. The complete model is provided in the original paper.

[1] S.B. Crown, M.R. Antoniewicz,Metab. Eng., 20 (2013), pp. 42–48.
[2] H. Yoo, M.R. Antoniewicz, G. Stephanopoulos, J.K. Kelleher, J. Biol. Chem., 283 (2008), pp. 20621–20627.
[3] M.R. Antoniewicz, J.K. Kelleher, G. Stephanopoulos, Metab. Eng., 9 (2007), pp. 68–86.
[4] J.D. Young, J.L. Walther, M.R. Antoniewicz, H. Yoo, G. Stephanopoulos, Biotechnol. Bioeng., 99 (2008), pp. 686–699.
[5] M.R. Antoniewicz, J.K. Kelleher, G. Stephanopoulos, Metab. Eng., 8 (2006), pp. 324–337.
[6] R. Caspi, T. Altman, K. Dreher, C.A. Fulcher, P. Subhraveti, I.M. Keseler, A. Kothari, M. Krummenacker, M. Latendresse, L.A. Mueller, Q. Ong, S. Paley, A. Pujar, A.G. Shearer, M. Travers, D. Weerasinghe, P. Zhang, P.D. Karp, Nucleic Acids Res., 40 (2012), pp.–D753
[7] M. Kanehisa, S. Goto, Nucleic Acids Res., 28 (2000), pp. 27–30.
[8] M. Kanehisa, S. Goto, Y. Sato, M. Furumichi, M. Tanabe, Nucleic Acids Res., 40 (2012), pp. D109–D114.

Data file
Downloadfluxes KIMODATAID90_v0.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 2015-09-22 12:48:07 UTC

Updated 2015-09-22 13:06:38 UTC

Version 0

Status (reviewed) 2015-09-22 13:06:46 UTC

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