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Accurate Metabolic Flux Analysis through Data Reconciliation of Isotope Balance-Based Data  

Kim Tae-Yong (Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical & Biomolecular Engineering and BioProcess Engineering Research Center)
Lee Sang-Yup (Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical & Biomolecular Engineering and BioProcess Engineering Research Center)
Publication Information
Journal of Microbiology and Biotechnology / v.16, no.7, 2006 , pp. 1139-1143 More about this Journal
Abstract
Various techniques and strategies have been developed for the identification of intracellular metabolic conditions, and among them, isotope balance-based flux analysis with gas chromatography/mass spectrometry (GC/ MS) has recently become popular. Even though isotope balance-based flux analysis allows a more accurate estimation of intracellular fluxes, its application has been restricted to relatively small metabolic systems because of the limited number of measurable metabolites. In this paper, a strategy for incorporating isotope balance-based flux data obtained for a small network into metabolic flux analysis was examined as a feasible alternative allowing more accurate quantification of intracellular flux distribution in a large metabolic system. To impose GC/MS based data into a large metabolic network and obtain optimum flux distribution profile, data reconciliation procedure was applied. As a result, metabolic flux values of 308 intracellular reactions could be estimated from 29 GC/ MS based fluxes with higher accuracy.
Keywords
Data reconciliation; metabolic flux analysis; isotopic flux data; GC/MS; Escherichia coli;
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  • Reference
1 Lee, S. Y. and E. T. Papoutsakis. 1999. Metabolic Engineering. Marcel Dekker, New York
2 Varma, A. and B. O. Palsson. 1994. Metabolic flux balancing: Basic concepts, scientific and practical use. Bio/Technology 12: 994-998   DOI
3 Yang, C., Q. Hua, and K. Shimizu. 2002. Integration ofthe information from gene expression and metabolic fluxes for the analysis of the regulatory mechanisms in Synechocystis. Appl. Microbiol. Bioeng. 58: 813-822   DOI
4 Zhao, J. and K. Shimizu. 2003. Metabolic flux analysis of Escherichia coli K12 grown on 13C labeled acetate and glucose using GC-MS and powerful flux calculation method. J. Biotechnol. 101: 10 1-117   DOI   ScienceOn
5 Bailey, J. E. 1991. Towards a science of metabolic engineering. Science 252: 1668-1674   DOI
6 Schuster, S., T. Dandekar, and D. A. Fell. 1999. Detection of elementary flux modes in biochemical networks: A promising tool for pathway analysis and metabolic engineering. Trends in Biotechnology 17: 53-60   DOI   ScienceOn
7 Ben-Israel, A. and T. N. E. Greville. 1974. Generalized inverses: Theory and applications. John Wiley & Sons, New York
8 Klamt, S., S. Schuster, and E. D. Gilles. 2002. Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria. Biotechnol. Bioeng. 77: 734-751   DOI   ScienceOn
9 Park, S. M., C. Shaw-Reid, A. J. Sinskey, and G. Stephanopoulos. 1997. Elucidation of anapletoric pathway in Corynebacterium glutamicum via $^13}$C-NMR spectroscopy and GC-MS. Appl. Microbiol. Biotechnol. 47: 430-440   DOI
10 Hong, S. H. and S. Y. Lee. 2001. Metabolic flux analysis for succinic acid production by recombinant Escherichia coli with amplified malic enzyme activity. Biotechnol. Bioeng. 74: 89-95   DOI   ScienceOn
11 Stephanopoulos, G. A. A. Aristidou, and J. Nielsen. 1998. Metabolic engineering. Academic Press, San Diego
12 Christensen, B. and J. Nielsen. 2000. Metabolic network analysis of P. chrisogenum using 13C-labeled glucose. Biotechnol. Bioeng. 68: 652-659   DOI   ScienceOn
13 Shimizu, K. 2002. A review on metabolic pathway analysis with emphasis on isotope labeling approach. Biotechnol. Bioprocess Eng. 7: 237-251   DOI   ScienceOn
14 Fischer, E. and U. Sauer. 2003. Metabolic flux profiling of Escherichia coli mutants in central carbon metabolism by GC-MS. Eur. J. Biochem. 270: 880-891   DOI   ScienceOn
15 Lee, D.-Y., H. S. Yun, S. W. Park, and S. Y. Lee. 2003. MetaFluxNet: The management of metabolic reaction information and quantitative metabolic flux analysis. Bioinformatics 19: 2144-2146   DOI   ScienceOn