Cargando…

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores 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 (MLR), principal component regression (PCR), partial...

Descripción completa

Detalles Bibliográficos
Autores principales: Antoniewicz, Maciek R., Stephanopoulos, Gregory, Kelleher, Joanne K.
Formato: Texto
Lenguaje:English
Publicado: Kluwer Academic Publishers-Plenum Publishers 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1622920/
https://www.ncbi.nlm.nih.gov/pubmed/17066125
http://dx.doi.org/10.1007/s11306-006-0018-2
_version_ 1782130564664393728
author Antoniewicz, Maciek R.
Stephanopoulos, Gregory
Kelleher, Joanne K.
author_facet Antoniewicz, Maciek R.
Stephanopoulos, Gregory
Kelleher, Joanne K.
author_sort Antoniewicz, Maciek R.
collection PubMed
description This study explores 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 (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−(13)C]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.
format Text
id pubmed-1622920
institution National Center for Biotechnology Information
language English
publishDate 2006
publisher Kluwer Academic Publishers-Plenum Publishers
record_format MEDLINE/PubMed
spelling pubmed-16229202006-10-25 Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway Antoniewicz, Maciek R. Stephanopoulos, Gregory Kelleher, Joanne K. Metabolomics Article This study explores 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 (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−(13)C]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. Kluwer Academic Publishers-Plenum Publishers 2006-05-20 2006-03 /pmc/articles/PMC1622920/ /pubmed/17066125 http://dx.doi.org/10.1007/s11306-006-0018-2 Text en © Springer Science+Business Media, Inc. 2006
spellingShingle Article
Antoniewicz, Maciek R.
Stephanopoulos, Gregory
Kelleher, Joanne K.
Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway
title Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway
title_full Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway
title_fullStr Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway
title_full_unstemmed Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway
title_short Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway
title_sort evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1622920/
https://www.ncbi.nlm.nih.gov/pubmed/17066125
http://dx.doi.org/10.1007/s11306-006-0018-2
work_keys_str_mv AT antoniewiczmaciekr evaluationofregressionmodelsinmetabolicphysiologypredictingfluxesfromisotopicdatawithoutknowledgeofthepathway
AT stephanopoulosgregory evaluationofregressionmodelsinmetabolicphysiologypredictingfluxesfromisotopicdatawithoutknowledgeofthepathway
AT kelleherjoannek evaluationofregressionmodelsinmetabolicphysiologypredictingfluxesfromisotopicdatawithoutknowledgeofthepathway