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Parameter identifiability of power-law biochemical system models

Paper ID Volume ID Publish Year Pages File Format Full-Text
24218 43505 2010 9 PDF Available
Title
Parameter identifiability of power-law biochemical system models
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.

Keywords
Identifiability analysis; Power-law models; Inverse modeling; Confidence region; Biochemical Systems Theory
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Parameter identifiability of power-law biochemical system models
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Publisher
Database: Elsevier - ScienceDirect
Journal: Journal of Biotechnology - Volume 149, Issue 3, 1 September 2010, Pages 132–140
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us