Literature DB >> 10567066

Bidirectional reaction steps in metabolic networks: III. Explicit solution and analysis of isotopomer labeling systems.

W Wiechert1, M Möllney, N Isermann, M Wurzel, A A de Graaf.   

Abstract

The last few years have brought tremendous progress in experimental methods for metabolic flux determination by carbon-labeling experiments. A significant enlargement of the available measurement data set has been achieved, especially when isotopomer fractions within intracellular metabolite pools are quantitated. This information can be used to improve the statistical quality of flux estimates. Furthermore, several assumptions on bidirectional intracellular reaction steps that were hitherto indispensable may now become obsolete. To make full use of the complete measurement information a general mathematical model for isotopomer systems is established in this contribution. Then, by introducing the important new concept of cumomers and cumomer fractions, it is shown that the arising nonlinear isotopomer balance equations can be solved analytically in all cases. In particular, the solution of the metabolite flux balances and the positional carbon-labeling balances presented in part I of this series turn out to be just the first two steps of the general solution procedure for isotopomer balances. A detailed analysis of the isotopomer network structure then opens up new insights into the intrinsic structure of isotopomer systems. In particular, it turns out that isotopomer systems are not as complex as they appear at first glance. This enables some far-reaching conclusions to be drawn on the information potential of isotopomer experiments with respect to flux identification. Finally, some illustrative examples are examined to show that an information increase is not guaranteed when isotopomer measurements are used in addition to positional enrichment data. Copyright 1999 John Wiley & Sons, Inc.

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Year:  1999        PMID: 10567066

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  65 in total

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Journal:  Appl Environ Microbiol       Date:  2002-04       Impact factor: 4.792

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Journal:  Plant Mol Biol       Date:  2002-01       Impact factor: 4.076

3.  Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions.

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4.  Modeling of spatial metabolite distributions in the cardiac sarcomere.

Authors:  Vitaly A Selivanov; Stephen Krause; Josep Roca; Marta Cascante
Journal:  Biophys J       Date:  2007-02-26       Impact factor: 4.033

5.  Global metabolic effects of glycerol kinase overexpression in rat hepatoma cells.

Authors:  Ganesh Sriram; Lola Rahib; Jian-Sen He; Allison E Campos; Lilly S Parr; James C Liao; Katrina M Dipple
Journal:  Mol Genet Metab       Date:  2007-10-29       Impact factor: 4.797

6.  (13)C-based metabolic flux analysis.

Authors:  Nicola Zamboni; Sarah-Maria Fendt; Martin Rühl; Uwe Sauer
Journal:  Nat Protoc       Date:  2009-05-21       Impact factor: 13.491

7.  Quantitation of cellular metabolic fluxes of methionine.

Authors:  Tomer Shlomi; Jing Fan; Baiqing Tang; Warren D Kruger; Joshua D Rabinowitz
Journal:  Anal Chem       Date:  2014-01-16       Impact factor: 6.986

8.  Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

Authors:  Jan Schellenberger; Richard Que; Ronan M T Fleming; Ines Thiele; Jeffrey D Orth; Adam M Feist; Daniel C Zielinski; Aarash Bordbar; Nathan E Lewis; Sorena Rahmanian; Joseph Kang; Daniel R Hyduke; Bernhard Ø Palsson
Journal:  Nat Protoc       Date:  2011-08-04       Impact factor: 13.491

9.  Palmitate-induced activation of mitochondrial metabolism promotes oxidative stress and apoptosis in H4IIEC3 rat hepatocytes.

Authors:  Robert A Egnatchik; Alexandra K Leamy; Yasushi Noguchi; Masakazu Shiota; Jamey D Young
Journal:  Metabolism       Date:  2013-10-24       Impact factor: 8.694

Review 10.  Understanding metabolism with flux analysis: From theory to application.

Authors:  Ziwei Dai; Jason W Locasale
Journal:  Metab Eng       Date:  2016-09-22       Impact factor: 9.783

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