title: Ensemble kinetic modeling of metabolic networks from dynamic metabolic profiles.
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.
authors: Chemical and Pharmaceutical Engineering, Singapore-MIT Alliance, Singapore 117576. jiagengjie@nus.edu.sg.
authors: Gengjie
authors: G
authors: Jia
authors: Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. gregstep@mit.edu.
authors: Gregory
authors: G
authors: Stephanopoulos
authors: Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zurich, Switzerland. rudi.gunawan@chem.ethz.ch.
authors: Rudiyanto
authors: R
authors: Gunawan