Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/64370
Title: Parameter identifiability of power-law biochemical system models
Authors: Srinath, S.
Gunawan, R. 
Keywords: Biochemical Systems Theory
Confidence region
Identifiability analysis
Inverse modeling
Power-law models
Issue Date: Sep-2010
Citation: Srinath, S., Gunawan, R. (2010-09). Parameter identifiability of power-law biochemical system models. Journal of Biotechnology 149 (3) : 132-140. ScholarBank@NUS Repository.
Abstract: Mathematical modeling has become an integral component in biotechnology, in which these models are frequently used to design and optimize bioprocesses. Canonical models, like power-laws within the Biochemical Systems Theory, offer numerous mathematical and numerical advantages, including built-in flexibility to simulate general nonlinear behavior. The construction of such models relies on the estimation of unknown case-specific model parameters by way of experimental data fitting, also known as inverse modeling. Despite the large number of publications on this topic, this task remains the bottleneck in canonical modeling of biochemical systems. The focus of this paper concerns with the question of identifiability of power-law models from dynamic data, that is, whether the parameter values can be uniquely and accurately identified from time-series data. Existing and newly developed parameter identifiability methods were applied to two power-law models of biochemical systems, and the results pointed to the lack of parametric identifiability as the root cause of the difficulty faced in the inverse modeling. Despite the focus on power-law models, the analyses and conclusions are extendable to other canonical models, and the issue of parameter identifiability is expected to be a common problem in biochemical system modeling. © 2010 Elsevier B.V.
Source Title: Journal of Biotechnology
URI: http://scholarbank.nus.edu.sg/handle/10635/64370
ISSN: 01681656
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

41
checked on May 10, 2018

WEB OF SCIENCETM
Citations

36
checked on May 15, 2018

Page view(s)

42
checked on May 5, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.