用贝叶斯方法结合代理模型，提升脑癌细胞迁移模型的校准效率与可信度。
Bayesian Calibration and Model Assessment of Cell Migration Dynamics with Surrogate Model Integration
- 融合代理模型与贝叶斯推断，联合分析参数不确定性与预测性能。
- 代理模型提速且精度更高，参数模型更可靠地估计关键参数。
- 揭示模型结构性缺陷，指导生物系统模型的优化方向。
计算模型为癌症演化等复杂生物过程提供关键洞见，但其机制性常导致非线性与高参数化，难以校准。本文系统评估细胞迁移模型中参数概率分布，采用四种互补策略进行贝叶斯校准：含与不含显式模型偏差的参数化模型和代理模型。该方法实现参数不确定性、预测性能与可解释性的联合分析。应用于微流控装置中胶质母细胞瘤进展的真实数据实验，代理模型在计算效率与预测准确性上表现更优，而参数模型因机制基础更可靠地估计参数。引入模型偏差揭示结构局限性，明确模型改进需求。综合比较为复杂生物系统计算模型的校准与优化提供实用指导。
原文摘要 · Abstract (English)
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.
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