Order matters: how the sequence of sensitivity analysis, parameter identifiability and uncertainty quantification...

Area of Science:

• Computational Biology
• Mathematical Modeling
• Systems Biology

Background:

• Mathematical models are crucial for understanding biological systems.
• Predictive accuracy of models hinges on effective parameter uncertainty management.
• Uncertainty quantification (UQ), sensitivity analysis (SA), and parameter identifiability (ID) are key methods, often used in isolation.

Purpose of the Study:

• To investigate the impact of workflow order on the application of UQ, SA, and ID.
• To demonstrate how integrating these methods provides more robust insights than individual application.
• To establish a generalizable framework for combining UQ, SA, and ID for improved biological modeling.

Main Methods:

• Comparison of three distinct analytical sequences: SA→ID→UQ, ID→SA→UQ, and UQ→ID→SA.
• Evaluation of how different workflow orders affect parameter prioritization and model reliability.
• Application of the integrated framework to case studies in bacterial persistence and wastewater filtration.

Main Results:

• The sequence of applying UQ, SA, and ID systematically alters which parameters are prioritized.
• Each analytical sequence yields different recommendations for focusing research efforts.
• Integrating insights from all three methods provides consistent and comprehensive understanding.

Conclusions:

• The order of analysis in UQ, SA, and ID is critical for accurate parameter prioritization and model interpretation.
• A combined approach offers superior insights compared to isolated analyses.
• The proposed framework enhances the link between parameter analysis, biological questions, and experimental design.