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Using active subspaces to explore discrepancies between global and local parameter sensitivities in a Lotka-Volterra
1Department of Mathematical Sciences, Lafayette College, 730 High Street, Easton, PA 18042, USA.
Mathematical Biosciences and Engineering : MBE
|August 2, 2026
Summary
Global sensitivity analysis can be misleading. This study introduces a framework using active subspaces to identify reliable parameter importance regions, improving model calibration and surrogate modeling accuracy.
Area of Science:
- Computational Science
- Mathematical Modeling
- Systems Biology
Background:
- Global sensitivity metrics are crucial for parameter importance in complex models, aiding dimension reduction.
- However, global metrics can mask local variations, leading to inaccurate conclusions and impacting model calibration.
- Existing methods may not fully capture localized parameter sensitivity.
Purpose of the Study:
- To investigate discrepancies between global and local sensitivity information.
- To introduce a framework for evaluating sensitivity metric stability across parameter spaces.
- To enhance the reliability of sensitivity analysis in complex systems.
Main Methods:
- Utilized active subspace methodology to analyze parameter sensitivity.
- Developed a framework to assess the stability of global sensitivity metrics within local parameter subregions.
- Applied the framework to illustrative examples and a Lotka-Volterra tumor cell competition model.
Main Results:
- Demonstrated how global sensitivity metrics can obscure local parameter importance.
- Showcased the proposed active subspace framework's ability to identify representative parameter space subregions.
- Illustrated exacerbated issues in higher-dimensional models, like the Lotka-Volterra system.
Conclusions:
- Global sensitivity analysis results should be interpreted with caution due to potential local variations.
- Incorporating local subregion analysis enhances the robustness and accuracy of downstream modeling tasks.
- The active subspace framework provides a robust method for assessing sensitivity metric reliability.
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