NDLI: Observability based parameter identifiability for biochemical reaction networks

Content Provider	IEEE Xplore Digital Library
Author	Geffen, D. Findeisen, R. Schliemann, M. Allgower, F. Guay, M.
Copyright Year	2008
Description	Author affiliation: Dept. of Chem. Eng., Queen's Univ., Kingston, ON (Geffen, D.; Findeisen, R.)
Abstract	In systems biology, models often contain a large number of unknown or only roughly known parameters that must be identified. This work examines the question of whether or not these parameters can in fact be estimated from available measurements. We consider identiflability of unknown parameters in biochemical reaction networks obtained from first-principles-modeling of metabolic and signal transduction networks. Such systems consist of continuous time, nonlinear differential equations. Several methods exist for answering the question of identiflability for such systems; many of which restate the question of identiflability as one of observability. We consider the application of such methods to a representative biological system: the $NF-_{K}B$ signal transduction pathway. It is shown that existing observability based strategies, which rely on finding an analytical solution, require significant simplifications to be applicable to systems biology problems which are often not feasible. For this reason, a new method based on the use of an 'empirical observability Gramian' for checking identifiability is proposed. This method is demonstrated through the use of a simple biological example.
Starting Page	2130
Ending Page	2135
File Size	701493
Page Count	6
File Format	PDF
ISBN	9781424420780
ISSN	07431619
DOI	10.1109/ACC.2008.4586807
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Observability Systems biology Biological systems Biological system modeling Parameter estimation Biological control systems Signal processing Differential equations Optimization methods State-space methods
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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