Metabolites 2012, 2(4), 891-912; doi:10.3390/metabo2040891
Article

Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles

1 Chemical and Pharmaceutical Engineering, Singapore-MIT Alliance, Singapore 117576 2 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 3 Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zurich, Switzerland
* Author to whom correspondence should be addressed.
Received: 14 September 2012; in revised form: 2 November 2012 / Accepted: 5 November 2012 / Published: 12 November 2012
(This article belongs to the Special Issue Metabolic Network Models)
PDF Full-text Download PDF Full-Text [558 KB, Updated Version, uploaded 19 December 2012 16:39 CET]
The original version is still available [558 KB, uploaded 13 November 2012 14:07 CET]
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

Supplementary Files

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

MDPI and ACS Style

Jia, G.; Stephanopoulos, G.; Gunawan, R. Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles. Metabolites 2012, 2, 891-912.

AMA Style

Jia G, Stephanopoulos G, Gunawan R. Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles. Metabolites. 2012; 2(4):891-912.

Chicago/Turabian Style

Jia, Gengjie; Stephanopoulos, Gregory; Gunawan, Rudiyanto. 2012. "Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles." Metabolites 2, no. 4: 891-912.

Metabolites EISSN 2218-1989 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert