DocumentCode :
3430902
Title :
Parameter estimation of biological phenomena modeled by S-systems: An Extended Kalman filter approach
Author :
Meskin, N. ; Nounou, H. ; Nounou, M. ; Datta, A. ; Dougherty, E.R.
Author_Institution :
Electrical Engineering Department, Qatar University, Doha, Qatar
fYear :
2011
fDate :
12-15 Dec. 2011
Firstpage :
4424
Lastpage :
4429
Abstract :
Recent advances in high-throughput technologies for biological data acquisition have spurred a broad interest in the development of mathematical models for biological phenomena. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of genetic regulatory networks (GRNs), as well as that of biochemical pathways. In the S-system modeling framework, the number of unknown parameters is much more than the number of metabolites and this makes the parameter estimation task a challenging one. In this paper, a new parameter estimation algorithm is developed based on the Extended Kalman filter (EKF) approach. It is first shown that the conventional EKF approach is not capable of estimating the unknown parameters of S-systems. To remedy this problem, a new iterative extended Kalman Filtering algorithm is developed in which the EKF algorithm is applied iteratively to the available noisy time profiles of the metabolites. The proposed estimation algorithm is applied to a generic branched pathway and the Cad system of E.coli. The simulation results demonstrate the effectiveness of the proposed scheme.
Keywords :
Biology; Estimation; Heuristic algorithms; Kalman filters; Noise measurement; Parameter estimation; Solid modeling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on
Conference_Location :
Orlando, FL, USA
ISSN :
0743-1546
Print_ISBN :
978-1-61284-800-6
Electronic_ISBN :
0743-1546
Type :
conf
DOI :
10.1109/CDC.2011.6160690
Filename :
6160690
Link To Document :
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