Fast Parameter Estimation in Biochemical Models
Author Information
Author(s): Chou I-Chun, Martens Harald, Voit Eberhard O
Primary Institution: The Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University
Hypothesis
Can alternating regression improve the speed and accuracy of parameter estimation in biochemical systems models?
Conclusion
Alternating regression is a fast and effective method for estimating parameters and identifying structures in S-systems.
Supporting Evidence
- Alternating regression can dissect nonlinear problems into simpler linear regression steps.
- The method is three to five orders of magnitude faster than conventional methods for structure identification.
- The algorithm allows for quick exploration of different initial guesses and settings.
Takeaway
This study shows a new way to quickly figure out important numbers in biological models, making it easier to understand how they work.
Methodology
The study uses alternating regression combined with decoupling techniques to estimate parameters from time series data in S-systems.
Limitations
The convergence patterns of the method are complex and require further investigation.
Digital Object Identifier (DOI)
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