Abstract
Dynamic models present a fundamental tool in systems biology, but rely on kinetic parameters, such as association and dissociation constants. Their direct estimation from studies on isolated reactions is usually expensive, time-consuming or even infeasible for large models. As a consequence, they must be estimated from indirect measurements, usually in the form of time-series data. We describe an observer-based parameter estimation approach taking the specific structure of biochemical reaction networks into account. Considering reaction kinetics described by polynomial or rational functions, we propose a three step approach. In a first step, the estimation of not directly measured states is decoupled from the estimation of the parameters using a suitable model extension. In a second step, a specially suited nonlinear observer estimates the extended state. Based on the obtained state estimates, the parameter estimates are calculated in a straightforward way in the final step. The applicability of the approach is exemplified considering a simplified model of the circadian rhythm.
Original language | English |
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Pages (from-to) | 269-279 |
Number of pages | 10 |
Journal | At-automatisierungstechnik |
Volume | 56 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2008 |
Keywords
- parameter identification
- identifiability
- observer
- observability
- systems biology
- parameter-estimation
- global identifiability
- model identification
- gene-expression
- systems
- optimization
- equations