Identifiability of large-scale non-linear dynamic network models applied to the ADM1-case study
Philippe Nimmegeers1, Joost Lauwers1, Dries Telen1
1KU Leuven, Department of Chemical Engineering, BioTeC+ & OPTEC, Gebroeders De Smetstraat 1, 9000 Ghent, Belgium.
Mathematical Biosciences
|February 27, 2017
Summary
Investigating the Anaerobic Digestion Model no. 1 (ADM1), this study found its structure is theoretically identifiable but practically challenging due to interconnected parameters. This impacts accurate real-world estimations.
Area of Science:
- * Biochemical Engineering
- * Systems Biology
- * Environmental Engineering
Background:
- * The Anaerobic Digestion Model no. 1 (ADM1) is a complex, large-scale non-linear dynamic network model.
- * Identifiability analysis is crucial for understanding model parameter estimation capabilities.
- * ADM1's complexity presents challenges for both theoretical and practical parameter determination.
Purpose of the Study:
- * To investigate the structural and practical identifiability of the ADM1.
- * To adapt identifiability analysis methods for large-scale non-linear dynamic systems.
- * To explore model modifications for improved parameter estimation.
Main Methods:
- * Structural identifiability assessed using a probabilistic algorithm tailored for differential-algebraic equations.
- * Practical identifiability analyzed via Monte Carlo parameter estimation with designed experiments.
- * Model structure modified by parameter combinations to enhance local structural identifiability.
Main Results:
- * ADM1 exhibits generally positive structural identifiability due to extensive state interconnections.
- * A modified ADM1 version achieves general local structural identifiability, enabling accurate theoretical parameter estimation.
- * High interconnectivity in the network structure complicates practical, uncorrelated parameter estimation.
Conclusions:
- * The ADM1's theoretical identifiability is confirmed, but practical estimation remains challenging.
- * Model modifications can lead to theoretically identifiable parameter sets.
- * Network interconnectivity is key to understanding both identifiability and estimation limitations.
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