UQ-PhysiCell: An extensible Python framework for uncertainty quantification and model analysis in PhysiCell

作者：Heber L. Rocha, Elmar Bucher, Shuming Zhang, Atul Deshpande, Daniel Bergman, Randy Heiland, Paul Macklin · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2026 · DOI：10.64898/2026.04.06.716692 · 被引用次数：1 · 研究领域：Gene Regulatory Network Analysis、Mathematical Biology Tumor Growth、Cell Image Analysis Techniques

Abstract Agent-based models (ABMs) are widely used to study complex multiscale biological systems, particularly in cancer research. However, their high-dimensional parameter spaces, stochasticity, and computational costs pose significant challenges for uncertainty quantification, calibration, and systematic comparison of competing mechanistic hypotheses. PhysiCell has evolved into a growing ecosystem of open-source tools supporting physics-based multicellular modeling, including model construction, visualization, experimental data integration (e.g., spatial multiomics), and downstream output analysis. However, despite these advances, systematic support for uncertainty-aware model analysis, scalable parameter exploration, and formal calibration workflows remains limited. Here, we introduce UQ-PhysiCell, an open-source Python package that enables uncertainty quantification, calibration, and model selection for PhysiCell models using a modular and scalable workflow. UQ-PhysiCell acts as a manager of PhysiCell simulation inputs and outputs, including parameters, initial conditions, rules, and MultiCellDS-compliant objects, and provides automated orchestration of large ensembles of simulations. The framework supports multiple levels of parallelism to accelerate the analysis, including the parallel execution of independent simulations, stochastic replicates, and downstream analysis tasks. UQ-PhysiCell integrates directly with established Python libraries for sensitivity analysis, o...