Comprehensive assessment of measurement uncertainty in 13C-based metabolic flux experiments

Abstract

In the field of metabolic engineering 13C-based metabolic flux analysis experiments have proven successful in indicating points of action. As every step of this approach is affected by an inherent error, the aim of the present work is the comprehensive evaluation of factors contributing to the uncertainty of nonnaturally distributed C-isotopologue abundances as well as to the absolute flux value calculation. For this purpose, a previously published data set, analyzed in the course of a 13C labeling experiment studying glycolysis and the pentose phosphate pathway in a yeast cell factory, was used. Here, for isotopologue pattern analysis of these highly polar metabolites that occur in multiple isomeric forms, a gas chromatographic separation approach with preceding derivatization was used. This rendered a natural isotope interference correction step essential. Uncertainty estimation of the resulting C-isotopologue distribution was performed according to the EURACHEM guidelines with Monte Carlo simulation. It revealed a significant increase for low-abundance isotopologue fractions after application of the necessary correction step. For absolute flux value estimation, isotopologue fractions of various sugar phosphates, together with the assessed uncertainties, were used in a metabolic model describing the upper part of the central carbon metabolism. The findings pinpointed the influence of small isotopologue fractions as sources of error and highlight the need for improved model curation. Graphical abstractᅟ Electronic supplementary materialThe online version of this article (10.1007/s00216-018-1017-7) contains supplementary material, which is available to authorized users.

Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions

“…However, in both cases, the experimentally‐measured flux values were very close to the range reported by ccFVA indicating that the observed discrepancy could be caused by an experimental or conversion calculation error. 13 C measurements are prone to a number of measurement errors, such as suboptimal metabolism quenching, unaccounted carbon sources in the media (Mairinger et al, ), or errors in conversion calculations. An example of the latter is the conversion of experimental flux values from the conventionally reported g cells −1 h −1 into mmol gDCW −1 h −1 usually used in GEMs.…”

“…The cell size and dry cell weight of individual cells vary through cultures leading to difficulties in estimating dry cell weight accurately (Pan, Dalm, Wijffels, & Martens, ) and is a known potential source of error (Ahn & Antoniewicz, ). A comprehensive list of potential errors in 13 C measurements has been reported by Mairinger and co‐workers (Mairinger et al, ). Despite this minor discrepancy, the predictions of biomass and product yields were not affected by ccFBA/ccFVA in any of the seven experimental datasets considered compared with the reported experimental values further increasing confidence in the algorithm.…”

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

“…However, it still remains a challenge for many systems, especially those with slow labelling dynamics, multiple compartments, and different carbon sources. An assessment of the measurement uncertainty of this method has also highlighted: (i) the importance of improving the model used to reduce its structural error, and (ii) that suitable certified matrix reference material is lacking for isotopologue analysis [ 90 ]. Finally, 13 C-MFA is used to determine flux values, but provides no mechanistic explanation for observed events.…”

Mapping Salmonella typhimurium pathways using 13C metabolic flux analysis

“…It is known that the determination of metabolic fluxes is affected by a variety of errors. Besides the choice of the isotopic tracer, analytical labeling analysis procedures and the structure of metabolic network are factors that contribute to the uncertainties of the flux estimation (Crown et al, 2016;Leighty and Antoniewicz, 2012;Mairinger et al, 2018). Nevertheless, it is important to highlight that the present work was conducted with the aim of explore the S. typhimurium metabolism, raising hypothesis to be further explored.…”