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Regression models for metabolic physiology: Predicting fluxes from isotopic data without knowledge of the pathway
Author(s) -
Antoniewicz Maciek R,
Stephanopoulos Gregory,
Kelleher Joanne K
Publication year - 2006
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.20.4.a410-a
Subject(s) - linear regression , regression , isotopomers , regression analysis , partial least squares regression , artificial neural network , metabolic flux analysis , bayesian multivariate linear regression , multivariate statistics , metabolomics , test set , data set , set (abstract data type) , flux (metallurgy) , metabolome , gluconeogenesis , principal component analysis , statistics , computer science , mathematics , artificial intelligence , chemistry , biology , bioinformatics , metabolism , biochemistry , organic chemistry , molecule , programming language
We explored the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared; multiple linear regression, principal component regression, partial least‐squares regression and regression using artificial neural networks. Mammalian gluconeogenesis was analyzed using [U‐13C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers; For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non‐linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.