Abstract
| Language | English |
|---|---|
| Title of host publication | Proceedings of the 17th IFAC World Congres |
| Editors | Myung Yin Chung, Pradeep Misar |
| Place of Publication | Seoul, Korea |
| Pages | 313-318 |
| Number of pages | 5 |
| Volume | 41 |
| DOIs | |
| Publication status | Published - 2008 |
Publication series
| Name | IFAC Proceedings |
|---|---|
| Publisher | IFAC |
| No. | 2 |
| Volume | 41 |
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Keywords
- parameter estimation
- parameter identi¯cation
- biological systems
- biochemical systems
- high-gain observers
- reduced-order observers
- observer Lyapunov function
Cite this
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Parameter estimation in kinetic reaction models using nonlinear observers facilitated by model extensions. / Fey, D.; Findeisen, R.; Bullinger, E.
Proceedings of the 17th IFAC World Congres. ed. / Myung Yin Chung; Pradeep Misar. Vol. 41 Seoul, Korea, 2008. p. 313-318 (IFAC Proceedings; Vol. 41, No. 2).Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Parameter estimation in kinetic reaction models using nonlinear observers facilitated by model extensions
AU - Fey, D.
AU - Findeisen, R.
AU - Bullinger, E.
PY - 2008
Y1 - 2008
N2 - An essential part of mathematical modelling is the accurate and reliable estimation of model parameters. In biology, the required parameters are particularly difficult to measure due to either shortcomings of the measurement technology or a lack of direct measurements. In both cases, parameters must be estimated from indirect measurements, usually in the form of time-series data. Here, we present a novel approach for parameter estimation that is particularly tailored to biological models consisting of nonlinear ordinary differential equations. By assuming specific types of nonlinearities common in biology, resulting from generalised mass action, Hill kinetics and products thereof, we can take a three step approach: (1) transform the identification into an observer problem using a suitable model extension that decouples the estimation of non-measured states from the parameters; (2) reconstruct all extended states using suitable nonlinear observers; (3) estimate the parameters using the reconstructed states. The actual estimation of the parameters is based on the intrinsic dependencies of the extended states arising from the definitions of the extended variables. An important advantage of the proposed method is that it allows to identify suitable measurements and/or model structures for which the parameters can be estimated. Furthermore, the proposed identification approach is generally applicable to models of metabolic networks, signal transduction and gene regulation.
AB - An essential part of mathematical modelling is the accurate and reliable estimation of model parameters. In biology, the required parameters are particularly difficult to measure due to either shortcomings of the measurement technology or a lack of direct measurements. In both cases, parameters must be estimated from indirect measurements, usually in the form of time-series data. Here, we present a novel approach for parameter estimation that is particularly tailored to biological models consisting of nonlinear ordinary differential equations. By assuming specific types of nonlinearities common in biology, resulting from generalised mass action, Hill kinetics and products thereof, we can take a three step approach: (1) transform the identification into an observer problem using a suitable model extension that decouples the estimation of non-measured states from the parameters; (2) reconstruct all extended states using suitable nonlinear observers; (3) estimate the parameters using the reconstructed states. The actual estimation of the parameters is based on the intrinsic dependencies of the extended states arising from the definitions of the extended variables. An important advantage of the proposed method is that it allows to identify suitable measurements and/or model structures for which the parameters can be estimated. Furthermore, the proposed identification approach is generally applicable to models of metabolic networks, signal transduction and gene regulation.
KW - parameter estimation
KW - parameter identi¯cation
KW - biological systems
KW - biochemical systems
KW - high-gain observers
KW - reduced-order observers
KW - observer Lyapunov function
UR - http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2008/start.htm
U2 - 10.3182/20080706-5-KR-1001.00053
DO - 10.3182/20080706-5-KR-1001.00053
M3 - Chapter
SN - 9783902661005
VL - 41
T3 - IFAC Proceedings
SP - 313
EP - 318
BT - Proceedings of the 17th IFAC World Congres
A2 - Chung, Myung Yin
A2 - Misar, Pradeep
CY - Seoul, Korea
ER -