Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles
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metabolites-02-00891-v2.pdf
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558.21 KB
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Author(s)
Jia, Gengjie
Stephanopoulos, Gregory
Gunawan, Rudiyanto
Date Issued
November 2012
Journal
Metabolites
Publisher
MDPI AG
Citation
Jia, Gengjie et al. "Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles." Metabolites 2, 4 (November 2014): 891-912 © 2014 The Author(s)
Version
Final published version
Abstract
Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics. Keywords: ensemble modeling; incremental identification; dynamic flux estimation; independent parameter set; generalized mass action model
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/metabo2040891
