Bayesian Calibration and Model Assessment of Cell Migration Dynamics with Surrogate Model Integration
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916964548476928 |
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| author | Schenk, Christina Jiménez, Jacobo Ayensa Romero, Ignacio |
| author_facet | Schenk, Christina Jiménez, Jacobo Ayensa Romero, Ignacio |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_18998 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Calibration and Model Assessment of Cell Migration Dynamics with Surrogate Model Integration Schenk, Christina Jiménez, Jacobo Ayensa Romero, Ignacio Analysis of PDEs Machine Learning Cell Behavior Quantitative Methods 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. |
| title | Bayesian Calibration and Model Assessment of Cell Migration Dynamics with Surrogate Model Integration |
| topic | Analysis of PDEs Machine Learning Cell Behavior Quantitative Methods |
| url | https://arxiv.org/abs/2509.18998 |