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Title: Metabolic Network Model Identification-Parameter Estimation and Ensemble Modeling
Keywords: Metabolic model identification, Parameter estimation, Ensemble modeling, Incomplete data, Incremental estimation, Model uncertainty.
Issue Date: 7-Aug-2012
Citation: JIA GENGJIE (2012-08-07). Metabolic Network Model Identification-Parameter Estimation and Ensemble Modeling. ScholarBank@NUS Repository.
Abstract: The PhD project focuses on the development of efficient model identification methods and a framework to capture model uncertainty, motivated by three common issues related to the parameter estimation of kinetic metabolic models, namely (1) missing information of metabolites, (2) high computational demand associated with stiff ordinary differential equations and large parameter search space, and (3) degrees of freedom due to larger number of metabolic fluxes than metabolites. Correspondingly, three computationally efficient algorithms are proposed for the purposes of (1) estimating parameters from incomplete metabolic profiles using a two-phase dynamic decoupling method, (2) estimating parameters using an incremental approach, and (3) constructing a kinetic model ensemble using an incremental approach. The efficacy of these proposed methods has been demonstrated through applications to a few case studies (artificial and real metabolic pathways) and through comparisons with existing methods.
Appears in Collections:Ph.D Theses (Open)

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