New tools for mass isotopomer data evaluation in 13C flux analysis: Mass isotope correction, data consistency checking, and precursor relationships

2003/12/31 by S.Aljoscha Wahl, A. Wahl, Michael Dauner +1 · 171 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · #Algorithm #Artificial intelligence #Biological system #Chemistry #Chromatography #Computer science #Consistency (knowledge bases) #Data consistency #Data mining #Flux (metallurgy) #Isotope #Isotopes of carbon #Isotopic labeling #Isotopomers #Mass spectrometry #Mass spectrum #Metabolic flux analysis #Metabolomics and Mass Spectrometry Studies #Microbial Metabolic Engineering and Bioproduction #Mitochondrial Function and Pathology #Molecule #Nuclear physics #Physics #Preprocessor

paper · doi:10.1002/bit.10909

published in Biotechnology and Bioengineering 85(3), 259-268 (Wiley)

openalex publication_date 2003/12/31 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/04

Abstract

13C metabolic flux analysis (MFA) is based on carbon-labeling experiments where a specifically (13)C labeled substrate is fed. The labeled carbon atoms distribute over the metabolic network and the label enrichment of certain metabolic pools is measured by using different methods. Recently, MS methods have been dramatically improved-large and precise datasets are now available. MS data has to be preprocessed and corrected for natural stable mass isotopes. In this article we present (1). a new elegant method to correct MS measurement data for natural stable mass isotopes by infinite dimensional matrix calculus and (2). we statistically analyze and discuss a reconstruction of labeling pattern in metabolic precursors from biosynthesis molecules. Moreover, we establish a new method for consistency checking of MS spectra that can be applied for automatic error recognition in high-throughput flux analysis procedures. Preprocessing the measurement data changes their statistical properties which have to be considered in the subsequent parameter fitting process for (13)C MFA. We show that correcting for stable mass isotopes leads to rather small correlations. On the other hand, a direct reconstruction of a precursor labeling pattern from an aromatic amino acid measurement turns out to be critical. Reasonable results are only obtained if additional, independent information about the labeling of at least one precursor is available. A versatile MatLab tool for the rapid correction and consistency checking of MS spectra is presented. Practical examples for the described methods are also given.

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