Literature DB >> 18636449

Bidirectional reaction steps in metabolic networks: I. Modeling and simulation of carbon isotope labeling experiments.

W Wiechert1, A A de Graaf.   

Abstract

The extension of metabolite balancing with carbon labeling experiments, as described by Marx et al. (Biotechnol. Bioeng. 49: 11-29), results in a much more detailed stationary metabolic flux analysis. As opposed to basic metabolite flux balancing alone, this method enables both flux directions of bidirectional reaction steps to be quantitated. However, the mathematical treatment of carbon labeling systems is much more complicated, because it requires the solution of numerous balance equations that are bilinear with respect to fluxes and fractional labeling. In this study, a universal modeling framework is presented for describing the metabolite and carbon atom flux in a metabolic network. Bidirectional reaction steps are extensively treated and their impact on the system's labeling state is investigated. Various kinds of modeling assumptions, as usually made for metabolic fluxes, are expressed by linear constraint equations. A numerical algorithm for the solution of the resulting linear constrained set of nonlinear equations is developed. The numerical stability problems caused by large bidirectional fluxes are solved by a specially developed transformation method. Finally, the simulation of carbon labeling experiments is facilitated by a flexible software tool for network synthesis. An illustrative simulation study on flux identifiability from available flux and labeling measurements in the cyclic pentose phosphate pathway of a recombinant strain of Zymomonas mobilis concludes this contribution.

Entities:  

Year:  1997        PMID: 18636449     DOI: 10.1002/(SICI)1097-0290(19970705)55:1<101::AID-BIT12>3.0.CO;2-P

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


  63 in total

Review 1.  Metabolomics--the link between genotypes and phenotypes.

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

Review 2.  It is all about metabolic fluxes.

Authors:  Jens Nielsen
Journal:  J Bacteriol       Date:  2003-12       Impact factor: 3.490

3.  Pathway confirmation and flux analysis of central metabolic pathways in Desulfovibrio vulgaris hildenborough using gas chromatography-mass spectrometry and Fourier transform-ion cyclotron resonance mass spectrometry.

Authors:  Yinjie Tang; Francesco Pingitore; Aindrila Mukhopadhyay; Richard Phan; Terry C Hazen; Jay D Keasling
Journal:  J Bacteriol       Date:  2006-11-17       Impact factor: 3.490

4.  Calculating as many fluxes as possible in underdetermined metabolic networks.

Authors:  Steffen Klamt; Stefan Schuster
Journal:  Mol Biol Rep       Date:  2002       Impact factor: 2.316

5.  The thermodynamic meaning of metabolic exchange fluxes.

Authors:  Wolfgang Wiechert
Journal:  Biophys J       Date:  2007-05-25       Impact factor: 4.033

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.  Metabolic flux analysis of Escherichia coli creB and arcA mutants reveals shared control of carbon catabolism under microaerobic growth conditions.

Authors:  Pablo I Nikel; Jiangfeng Zhu; Ka-Yiu San; Beatriz S Méndez; George N Bennett
Journal:  J Bacteriol       Date:  2009-06-26       Impact factor: 3.490

Review 8.  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

9.  Inferring branching pathways in genome-scale metabolic networks.

Authors:  Esa Pitkänen; Paula Jouhten; Juho Rousu
Journal:  BMC Syst Biol       Date:  2009-10-29

Review 10.  Nutritional systems biology modeling: from molecular mechanisms to physiology.

Authors:  Albert A de Graaf; Andreas P Freidig; Baukje De Roos; Neema Jamshidi; Matthias Heinemann; Johan A C Rullmann; Kevin D Hall; Martin Adiels; Ben van Ommen
Journal:  PLoS Comput Biol       Date:  2009-11-26       Impact factor: 4.475

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