Jasmin Fisher

This paper uses agent-based simulations to test models of lung tumour growth, finding that stringent cell competition after tumour size stabilization produces realistic clonal expansions and better estimates of driver mutation effects.

MAGELLAN constructs interpretable computational models from pathway data and experimental rules, demonstrating predictive accuracy in breast cancer and NSCLC signalling.

This paper develops an in silico model to predict personalized therapeutic combinations for NSCLC by simulating genetic profiles and testing drug combinations to overcome resistance.

The paper develops methods to classify homologous recombination deficiency in breast cancer using mutational and transcriptional signatures, showing heterogeneity and single-cell microenvironment interactions.

This paper predicts effective drug combinations for treating early- and late-stage severe SARS-CoV-2 infection using an executable network model of host-virus interactions.

The paper introduces a computational method to identify missing signaling pathway elements from temporal phosphoproteomic data, demonstrating its effectiveness in both human EGF response and yeast osmotic stress response.

The paper introduces a library of discrete target functions for cell signalling models using BioModelAnalyzer, showing they can predict genetic perturbations with limited data.