A methodology for performing global uncertainty and sensitivity analysis in systems biology
Simeone Marino1, Ian B Hogue, Christian J Ray
1Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109-0620, USA.
Journal of Theoretical Biology
|June 24, 2008
Summary
This study introduces global sensitivity analysis to accurately assess uncertainties in biological models. It provides a methodology to identify and control parameter uncertainties for more reliable model predictions.
Area of Science:
- Computational Biology
- Systems Biology
- Mathematical Modeling
Background:
- Model accuracy is limited by experimental data uncertainties.
- Current sensitivity analyses (single-parameter, local) are insufficient as they fix other parameters.
- These methods fail to capture complex, multi-dimensional parameter interactions.
Purpose of the Study:
- To develop and demonstrate a global sensitivity analysis methodology for biological models.
- To identify and control parameter uncertainties for improved model reliability.
- To compare robust and efficient global sensitivity analysis indices.
Main Methods:
- Utilizing multi-dimensional parameter space exploration.
- Applying existing analytical tools for global sensitivity analysis.
- Comparing two robust global sensitivity analysis indices in deterministic and stochastic settings.
Main Results:
- Demonstrated a complete methodology for global sensitivity analysis.
- Showcased techniques for handling common analysis problems.
- Provided examples across various mathematical and computational models.
Conclusions:
- Global sensitivity analysis offers a comprehensive approach to uncertainty quantification.
- The developed methods enhance the reliability and interpretability of biological models.
- This work facilitates better identification and control of model uncertainties.
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