Study Shows Parameter Range Selection Significantly Affects Sensitivity Analysis Results in Epithelial-Mesenchymal Transition Modeling
A new preprint on bioRxiv demonstrates that the range of parameter values chosen before conducting Latin Hypercube Sampling–Partial Rank Correlation Coefficient (LHS-PRCC) sensitivity analysis substantially changes both the results and the biological behavior observed in a mathematical model of the epithelial-mesenchymal transition (EMT). Prior work on this model restricted parameters to within ±10% of their original values, a narrow window that assumes high measurement precision; this study extended the analysis to ±25% and ±50% ranges and found that key findings shifted accordingly. The work highlights a largely underappreciated methodological pitfall: seemingly routine pre-analysis choices can obscure or destroy important dynamical features—such as bistable switching—and mislead experimental or model-development decisions.
Researchers posting to bioRxiv examined how the selection of parameter ranges prior to LHS-PRCC global sensitivity analysis affects both the quantitative outputs of the analysis and the qualitative phenomenological behavior of a mathematical model of the epithelial-mesenchymal transition (EMT), a process central to cancer metastasis and development. The study found that restricting parameters to ±10% of their baseline values—as done in previous analyses of the same model—implicitly assumes parameters are well-measured with minimal biological variability, a condition rarely met in practice. Expanding the ranges to ±25% and ±50% produced meaningfully different PRCC rankings, altering which parameters appeared most influential. Critically, the authors also tested whether the bistable switch—a key dynamical feature of the original EMT model—was preserved across all three parameter ranges, finding that broader ranges could eliminate this behavior entirely. This loss of phenomenological fidelity represents a hidden consequence of parameter range selection that is not captured by the sensitivity statistics alone. The authors conclude that explicit, biologically grounded prior knowledge about parameter values is essential before using sensitivity analyses to guide experiments or refine models. The findings serve as a cautionary note for modelers working in systems biology and other fields where parameter uncertainty is high.
What's missing
The preprint has not yet undergone peer review, so the robustness of the conclusions has not been independently validated. The study uses a single EMT model as its test case, leaving open whether the magnitude of sensitivity-analysis shifts observed here generalizes to other biological models or dynamical systems. Additionally, the authors do not propose a standardized method for selecting parameter ranges when experimental data are sparse, which is the practical challenge most modelers face.
What different sources said
- bioRxivCenter
Varying parameter ranges alters both Partial Rank Correlation Coefficient results and phenomenological behavior when modeling the epithelial mesenchymal transition
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