Reduction and identification of dynamic models. Simple example: Generic receptor model

Heikki Haario, Leonid Kalachev, Marko Laine

Research output: Contribution to journalArticlepeer-review


Identification of biological models is often complicated by the fact that the available experimental data from field measurements is noisy or incomplete. Moreover, models may be complex, and contain a large number of correlated parameters. As a result, the parameters are poorly identified by the data, and the reliability of the model predictions is questionable. We consider a general scheme for reduction and identification of dynamic models using two modern approaches, Markov chain Monte Carlo sampling methods together with asymptotic model reduction techniques. The ideas are illustrated using a simple example related to bio-medical applications: a model of a generic receptor. In this paper we want to point out what the researchers working in biological, medical, etc., fields should look for in order to identify such problematic situations in modelling, and how to overcome these problems.

Original languageEnglish
Pages (from-to)417-435
Number of pages19
JournalDiscrete and Continuous Dynamical Systems - Series B
Issue number2
StatePublished - Mar 2013


  • Asymptotic methods
  • Boundary function method
  • Markov chain Monte Carlo (MCMC)
  • Model identification
  • Model reduction


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