Paper Info
Title
Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method.
Abstract
Motivation: Time-series measurements of metabolite concentration have become increasingly more common, providing data for building kinetic models of metabolic networks using ordinary differential equations (ODEs). In practice, however, such time-course data are usually incomplete and noisy, and the estimation of kinetic parameters from these data is challenging. Practical limitations due to data and computational aspects, such as solving stiff ODEs and finding global optimal solution to the estimation problem, give motivations to develop a new estimation procedure that can circumvent some of these constraints. Results: In this work, an incremental and iterative parameter estimation method is proposed that combines and iterates between two estimation phases. One phase involves a decoupling method, in which a subset of model parameters that are associated with measured metabolites, are estimated using the minimization of slope errors. Another phase follows, in which the ODE model is solved one equation at a time and the remaining model parameters are obtained by minimizing concentration errors. The performance of this two-phase method was tested on a generic branched metabolic pathway and the glycolytic pathway of Lactococcus lactis. The results showed that the method is efficient in getting accurate parameter estimates, even when some information is missing.
Year
DOI
Venue
2011
10.1093/bioinformatics/btr293
BIOINFORMATICS
Field
DocType
Volume
Data mining,Applied mathematics,Ordinary differential equation,Computer science,Decoupling (cosmology),Kinetic model,Minification,Estimation theory,Iterated function,Calculus,Ode,Kinetic energy
Journal
27
Issue
ISSN
Citations 
14
1367-4803
9
PageRank 
References 
Authors
0.66
8
3
Name
Order
Citations
PageRank
Gengjie Jia1161.17
Gregory Stephanopoulos212413.54
Rudiyanto Gunawan315315.50