Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13C, 1H] COSY NMR measurements and cumulative bondomer simulation

• Published in: Biotechnology and bioengineering 2003-07, Vol.83 (1), p.75-92
• Main Authors: van Winden, Wouter A., van Gulik, Walter M., Schipper, Dick, Verheijen, Peter J.T., Krabben, Preben, Vinke, Jacobus L., Heijnen, Joseph J.
• Format: Article
• Language: English
• Subjects: 13C-labeling; Ammonia - metabolism; Biological and medical sciences; Biology of microorganisms of confirmed or potential industrial interest; Bioreactors - microbiology; Biotechnology; bondomer; Carbon - metabolism; Carbon Isotopes; Computer Simulation; Fundamental and applied biological sciences. Psychology; Glycolysis - physiology; Magnetic Resonance Spectroscopy - methods; metabolic flux analysis; metabolic network analysis; Mission oriented research; Models, Biological; Nitrates - metabolism; NMR; Penicillium chrysogenum; Penicillium chrysogenum - metabolism; Pentose Phosphate Pathway - physiology; Physiology and metabolism; Protons
• ISSN: 0006-3592; 1097-0290
• DOI: 10.1002/bit.10648

At present two alternative methods are available for analyzing the fluxes in a metabolic network: (1) combining measurements of net conversion rates with a set of metabolite balances including the cofactor balances, or (2) leaving out the cofactor balances and fitting the resulting free fluxes to measured 13C‐labeling data. In this study these two approaches are applied to the fluxes in the glycolysis and pentose phosphate pathway of Penicillium chrysogenum growing on either ammonia or nitrate as the nitrogen source, which is expected to give different pentose phosphate pathway fluxes. The presented flux analyses are based on extensive sets of 2D [13C, 1H] COSY data. A new concept is applied for simulation of this type of 13C‐labeling data: cumulative bondomer modeling. The outcomes of the 13C‐labeling based flux analysis substantially differ from those of the pure metabolite balancing approach. The fluxes that are determined using 13C‐labeling data are shown to be highly dependent on the chosen metabolic network. Extending the traditional nonoxidative pentose phosphate pathway with additional transketolase and transaldolase reactions, extending the glycolysis with a fructose 6‐phosphate aldolase/dihydroxyacetone kinase reaction sequence or adding a phosphoenolpyruvate carboxykinase reaction to the model considerably improves the fit of the measured and the simulated NMR data. The results obtained using the extended version of the nonoxidative pentose phosphate pathway model show that the transketolase and transaldolase reactions need not be assumed reversible to get a good fit of the 13C‐labeling data. Strict statistical testing of the outcomes of 13C‐labeling based flux analysis using realistic measurement errors is demonstrated to be of prime importance for verifying the assumed metabolic model. © 2003 Wiley Periodicals, Inc. Biotechnol Bioeng 83: 75–92, 2003.