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Related Experiment Video

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Easy parameter identifiability analysis with COPASI.

Jörg Schaber¹

¹Institute for Experimental Internal Medicine, Medical Faculty, Otto von Guericke University, Magdeburg, Germany. schaber@med.ovgu.de

|October 9, 2012

Summary

This study introduces a new method for parameter identifiability analysis in biochemical models using COPASI software. It enables researchers to easily determine which kinetic parameters are well-defined by experimental data.

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Area of Science:

Systems biology
Biochemical reaction networks
Computational modeling

Background:

Quantitative predictions from biochemical models require experimental data validation.
Parameter non-identifiability hinders reliable model predictions.
Identifiability analysis is crucial for robust biochemical network modeling.

Purpose of the Study:

To describe a method for parameter identifiability analysis in differential equation systems.
To leverage a feature within the COPASI software for this analysis.
To provide an accessible tool for researchers, including non-experts.

Main Methods:

Utilizing the COPASI software's hidden feature for parameter identifiability analysis.
Calculating likelihood profiles to assess parameter identifiability.
Combining established identifiability analysis methods with user-friendly software.

Main Results:

Demonstration of an easy and quick method for parameter identifiability analysis.
Facilitation of the determination of practically non-identifiable parameters.
Enhanced accessibility to identifiability analysis for a broader range of users.

Conclusions:

The described method offers a rapid and straightforward approach to parameter identifiability analysis.
COPASI software can be effectively used to improve the reliability of biochemical model predictions.
This approach empowers researchers to better understand parameter constraints in their models.