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An iterative identification procedure for dynamic modeling of biochemical networks
Eva Balsa-Canto1, Antonio A Alonso, Julio R Banga
1Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, 36208 Vigo-Spain. ebalsa@iim.csic.es
BMC Systems Biology
|February 19, 2010
Summary
This study introduces a new iterative method to improve parameter identifiability in mathematical models. The procedure enhances model predictions by optimizing experimental designs for biological systems.
Area of Science:
- Systems Biology
- Mathematical Modeling
- Computational Biology
Background:
- Mathematical models represent biological systems but require parameter identification from experimental data.
- Model predictability depends on accurately determining non-measurable parameters.
- Parameter identifiability is crucial for successful model fitting.
Purpose of the Study:
- To develop and present a novel iterative procedure for detecting and addressing parameter non-identifiability in mathematical models.
- To enhance the predictive capabilities of biological models through improved parameter estimation.
- To optimize experimental designs for better model parameterization.
Main Methods:
- Structural and practical identifiability analyses were performed.
- Parameters were globally ranked to identify the most relevant ones.
- Model calibration utilized global optimization methods.
- Optimal experimental designs were computed to maximize information content.
Main Results:
- A novel iterative procedure for parameter identifiability was successfully developed.
- The procedure effectively detected and addressed non-identifiable parameters.
- Optimal dynamic experiments were computed, significantly improving model identifiability properties.
Conclusions:
- The iterative procedure enhances the identifiability of parameters in complex biological models.
- This approach is applicable to models such as the NF-kappaB regulatory module.
- Optimized experimental designs are key to improving model accuracy and predictive power.
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