Cumulative bondomers: A new concept in flux analysis from 2D [13C,1H] COSY NMR data

Wouter A. Van Winden, Joseph J. Heijnen, Peter J T Verheijen

Research output: Contribution to journalArticleScientificpeer-review

63 Citations (Scopus)

Abstract

A well-established way of determining metabolic fluxes is to measure 2D [13C,1H] COSY NMR spectra of components of biomass grown on uniformly 13C-labeled carbon sources. When using the entire set of measured data to simultaneously determine all fluxes in a proposed metabolic network model, the 13C-labeling distribution in all measured compounds has to be simulated. This requires very large sets of isotopomer or cumomer balances. This article introduces the new concept of bondomers; entities that only vary in the numbers and positions of C-C bonds that have remained intact since the medium substrate molecule entered the metabolism. Bondomers are shown to have many analogies to isotopomers. One of these is that bondomers can be transformed to cumulative bondomers, just like isotopomers can be transformed to cumomers. Similarly to cumomers, cumulative bondomers allow an analytical solution of the entire set of balances describing a metabolic network. The main difference is that cumulative bondomer models are considerably smaller than corresponding cumomer models. This saves computational time, allows easier identifiability analysis, and yields new insights in the information content of 2D [13C,1H] COSY NMR data. We illustrate the theoretical concepts by means of a realistic example of the glycolytic and pentose phosphate pathways. The combinations of 2D [13C,1H] COSY NMR data that allow identification of all metabolic fluxes in these pathways are analyzed, and it is found that the NMR data contain less information than was previously expected.

Original languageEnglish
Pages (from-to)731-745
Number of pages15
JournalBiotechnology and Bioengineering
Volume80
Issue number7
DOIs
Publication statusPublished - 30 Dec 2002

Keywords

  • C-labeling
  • Cumulative bondomers
  • Identifiability
  • Metabolic flux analysis

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