A simple work flow for biologically inspired model reduction - application to early JAK-STAT signaling
2011
Simplifying JAK-STAT Signaling Models
publication
10 minutes
Evidence: moderate
Author Information
Author(s): Quaiser Tom, Dittrich Anna, Schaper Fred, Mönnigmann Martin
Primary Institution: Ruhr University Bochum
Hypothesis
Can a systematic procedure for model simplification lead to identifiable models in JAK-STAT signaling?
Conclusion
The proposed model simplification workflow results in an identifiable model with a good balance between fit and complexity.
Supporting Evidence
- The model simplification workflow was applied iteratively to achieve an identifiable model.
- Parameter estimation was conducted using simulated data from a reference model.
- The final model showed improved AICc values indicating better fit.
Takeaway
This study shows how to make complex biological models simpler and easier to understand, while still being accurate.
Methodology
The study used a systematic procedure involving parameter estimation, identifiability ranking, and iterative model simplification.
Limitations
The model simplification process requires a skilled modeler and is not fully automated.
Statistical Information
P-Value
p<0.01
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website