DocumentCode :
757133
Title :
Efficient algorithms for ordinary differential equation model identification of biological systems
Author :
Gennemark, P. ; Wedelin, D.
Author_Institution :
Dept. of Comput. Sci. & Eng., Chalmers Univ. of Technol., Goteborg
Volume :
1
Issue :
2
fYear :
2007
fDate :
3/1/2007 12:00:00 AM
Firstpage :
120
Lastpage :
129
Abstract :
Algorithms for parameter estimation and model selection that identify both the structure and the parameters of an ordinary differential equation model from experimental data are presented. The work presented here focuses on the case of an unknown structure and some time course information available for every variable to be analysed, and this is exploited to make the algorithms as efficient as possible. The algorithms are designed to handle problems of realistic size, where reactions can be nonlinear in the parameters and where data can be sparse and noisy. To achieve computational efficiency, parameters are mostly estimated for one equation at a time, giving a fast and accurate parameter estimation algorithm compared with other algorithms in the literature. The model selection is done with an efficient heuristic search algorithm, where the structure is built incrementally. Two test systems are used that have previously been used to evaluate identification algorithms, a metabolic pathway and a genetic network. Both test systems were successfully identified by using a reasonable amount of simulated data. Besides, measurement noise of realistic levels can be handled. In comparison to other methods that were used for these test systems, the main strengths of the presented algorithms are that a fully specified model, and not only a structure, is identified, and that they are considerably faster compared with other identification algorithms.
Keywords :
biochemistry; biology computing; differential equations; genetics; molecular biophysics; noise; parameter estimation; physiological models; biological systems; genetic network; heuristic search algorithm; identification algorithms; metabolic pathway; model identification; model selection; ordinary differential equation; parameter estimation;
fLanguage :
English
Journal_Title :
Systems Biology, IET
Publisher :
iet
ISSN :
1751-8849
Type :
jour
DOI :
10.1049/iet-syb:20050098
Filename :
4140673
Link To Document :
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