Trachette Levon Jackson

The paper develops a spatial agent-based model and interpretable machine learning to analyze how HER2 heterogeneity and combination therapy affect breast cancer treatment response.

The paper develops a mathematical model to study how phenotypic plasticity and competition between HER2+ and HER2- cells affect treatment outcomes in breast cancer, showing that treatment order and intensity influence long-term tumour composition.

This paper introduces a computational model to study intratumor heterogeneity in melanoma by integrating cancer hallmarks and simulating genetic, immune, and spatial pressures.

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

This paper develops a mechanistic model of oncolytic virus-CAR T therapy, showing that OV-induced CAR T-cell memory is critical for long-term control of solid tumors.

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.

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

The paper models prostate cancer response to sipuleucel-T and androgen depletion using a deterministic model with standing variations to capture inter-individual heterogeneity, predicting an optimal combination therapy.