Bayesian Calibration and Model Assessment of Cell Migration Dynamics with Surrogate Model Integration
This provides practical calibration guidance for researchers modeling complex biological systems like cancer evolution, though it represents an incremental methodological comparison rather than a breakthrough.
The researchers tackled the challenge of calibrating complex, parameter-rich computational models of cell migration by systematically evaluating four Bayesian calibration strategies combining parametric/surrogate models with/without model discrepancy. Their application to glioblastoma progression data showed surrogate models achieved higher computational efficiency and predictive accuracy, while parametric models provided more reliable parameter estimates, and model discrepancy analysis revealed structural limitations.
Computational models provide crucial insights into complex biological processes such as cancer evolution, but their mechanistic nature often makes them nonlinear and parameter-rich, complicating calibration. We systematically evaluate parameter probability distributions in cell migration models using Bayesian calibration across four complementary strategies: parametric and surrogate models, each with and without explicit model discrepancy. This approach enables joint analysis of parameter uncertainty, predictive performance, and interpretability. Applied to a real data experiment of glioblastoma progression in microfluidic devices, surrogate models achieve higher computational efficiency and predictive accuracy, whereas parametric models yield more reliable parameter estimates due to their mechanistic grounding. Incorporating model discrepancy exposes structural limitations, clarifying where model refinement is necessary. Together, these comparisons offer practical guidance for calibrating and improving computational models of complex biological systems.