Daniel R. Bergman

UQ-PhysiCell provides a Python framework for uncertainty quantification, calibration, and model analysis in PhysiCell simulations, enabling scalable and reproducible uncertainty-aware analysis of agent-based models.

This paper introduces a trajectory-centric framework to model dynamic tumor-immune interactions, showing that treatment-phase TME states predict immunotherapy response better than pre-treatment states.

This study improves immunotherapy model predictions by integrating data and ensuring parameter identifiability using ODE models and bladder cancer data.

The paper introduces an algorithm to correct mislabeling and align object positions in longitudinal 3D cell culture imaging, improving data integrity and reproducibility.

The paper introduces a framework for mathematical modeling of multicellular systems using interpretable cell behavior rules derived from biological knowledge and multi-omics data.

This study uses spatial multi-omics to identify TLS-associated immune niches in PDAC that correlate with improved survival in immunotherapy responders.

SMoRe GloS provides an efficient framework for global sensitivity analysis of agent-based models, achieving speedups while maintaining accuracy across different model complexities.

This study compares ODE and ABM models to show how spatial components affect immunotherapy outcomes in tumors with different antigenicity and CTL killing mechanisms.

PhysiCell Studio is a graphical tool that simplifies the creation and editing of multicellular models by providing a user-friendly interface for XML-based model definition.

The paper introduces a global method to simulate agent-based models of biological tissue with phenotype switching, reducing computational time while preserving key dynamics.

PhysiPKPD integrates pharmacokinetics and pharmacodynamics into PhysiCell models to enhance in silico simulations of molecular therapies.

The paper models EMT-immune dynamics in epithelial cancer progression, identifying key factors that influence invasion-free survival and predicting new genes affecting cancer outcomes.