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New tools for mass isotopomer data evaluation in 13 C flux analysis: Mass isotope correction, data consistency checking, and precursor relationships
Author(s) -
Wahl S.Aljoscha,
Dauner Michael,
Wiechert Wolfgang
Publication year - 2003
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.10909
Subject(s) - metabolic flux analysis , isotopomers , consistency (knowledge bases) , data consistency , preprocessor , isotope , flux (metallurgy) , mass spectrum , biological system , chemistry , isotopes of carbon , computer science , isotopic labeling , mass spectrometry , data mining , algorithm , molecule , artificial intelligence , physics , chromatography , nuclear physics , biochemistry , organic chemistry , metabolism , biology , operating system
13 C 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.©2004 Wiley Periodicals, Inc.