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To be certain about the uncertainty: Bayesian statistics for 13 C metabolic flux analysis
Author(s) -
Theorell Axel,
Leweke Samuel,
Wiechert Wolfgang,
Nöh Katharina
Publication year - 2017
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.26379
Subject(s) - confidence interval , frequentist inference , statistics , markov chain monte carlo , bayesian probability , metabolic flux analysis , credible interval , monte carlo method , uncertainty analysis , flux (metallurgy) , econometrics , mathematics , computer science , bayesian inference , chemistry , organic chemistry , metabolism , biochemistry
13 C Metabolic Fluxes Analysis ( 13 C MFA) remains to be the most powerful approach to determine intracellular metabolic reaction rates. Decisions on strain engineering and experimentation heavily rely upon the certainty with which these fluxes are estimated. For uncertainty quantification, the vast majority of 13 C MFA studies relies on confidence intervals from the paradigm of Frequentist statistics. However, it is well known that the confidence intervals for a given experimental outcome are not uniquely defined. As a result, confidence intervals produced by different methods can be different, but nevertheless equally valid. This is of high relevance to 13 C MFA, since practitioners regularly use three different approximate approaches for calculating confidence intervals. By means of a computational study with a realistic model of the central carbon metabolism of E. coli , we provide strong evidence that confidence intervals used in the field depend strongly on the technique with which they were calculated and, thus, their use leads to misinterpretation of the flux uncertainty. In order to provide a better alternative to confidence intervals in 13 C MFA, we demonstrate that credible intervals from the paradigm of Bayesian statistics give more reliable flux uncertainty quantifications which can be readily computed with high accuracy using Markov chain Monte Carlo. In addition, the widely applied chi‐square test, as a means of testing whether the model reproduces the data, is examined closer.