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

Mathematical models are essential tools for understanding biological systems, but their predictive value depends on how parameter uncertainty is handled. Three core approaches-uncertainty quantification (UQ), sensitivity analysis (SA) and parameter identifiability (ID)-address distinct facets of the problem. However, they are usually applied in isolation. Here, we show that the workflow order in which these methods are implemented impacts parameter prioritization, model reliability and the interpretation of biological mechanisms. We compare three analytical sequences (SA→ID→UQ, ID→SA→UQ and UQ→ID→SA) and demonstrate that each suggests different parameters to focus on. Integrating these perspectives provides consistent insights that are not obtained from any single method. Our results establish a framework for combining UQ, SA and ID that links parameter analysis directly to biological questions and experimental design. We illustrate this in the context of bacterial persistence and wastewater filtration; however, the framework is general and applicable to a wide range of problems where reliable prediction from models is essential. Unlike prior studies that treat these analyses in isolation, we show that workflow order systematically changes parameter prioritization and experimental recommendations.