Elmar Bucher

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 study develops a multicellular agent-based model to simulate nanoparticle interactions in cancer therapy and evaluates how pharmacokinetic parameters and therapeutic strategies affect tumor dynamics.

PhysiGym integrates PhysiCell with Gymnasium to enable reinforcement learning control of in silico biological processes, demonstrating tumor elimination in a case study.

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

The paper presents a LINCS Microenvironment perturbation dataset cataloging transcriptional, proteomic, epigenomic, and phenotypic responses of MCF10A cells to six ligands, enabling integrative analysis of molecular and phenotypic changes.