Abstract
Compartmental models are a very popular tool for the analysis of experiments in living systems. There are three main aspects that have to be taken into account: the degree of detail of the model, its a priori identifiability and the a posteriori (numerical) identifiability. In some cases, where standard approaches are adopted, the models can be either a priori or a posteriori unidentifiable. The paper proposes model identification within a Bayesian framework, to solve a posteriori unidentifiability problems. In particular, a stochastic simulation algorithm is proposed to perform a Bayesian identification of compartmental models, and an empirical Bayesian technique is proposed to propagate information among multiple experiments. The power of this methodology was demonstrated by evaluating the kinetics of thiamine under several experimental conditions. The complexity of the existing model (nine parameters) and limited experimental data (8/12 for each model) caused a posteriori identifiability problems when standard approaches were adopted. The application of the methodology identifies all 28 models (four tissues under seven different conditions).
Original language | English |
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Pages (from-to) | 700-706 |
Number of pages | 7 |
Journal | Medical and Biological Engineering and Computing |
Volume | 39 |
Issue number | 6 |
Publication status | Published - 2001 |
Keywords
- Bayes estimate
- Compartmental model
- Empirical Bayes
- Markov chain Monte Carlo
- Nervous tissue
- Thiamine
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
- Health Information Management
- Computer Science Applications
- Computational Theory and Mathematics