Jonas Béal

The paper develops patient-specific Boolean models of prostate cancer signaling networks to identify actionable targets for personalized treatment, finding dose-dependent effects of inhibitors on key targets.

The paper develops personalized logical models to analyze cancer response to BRAF treatments, showing they correlate with experimental sensitivities using omics data without model training.

This paper introduces iSCHRUNK, a method combining Monte Carlo sampling and machine learning to reduce uncertainty in kinetic models, identifying key parameters for desired metabolic behavior.