Sung‐Young Shin

The paper identifies network topology principles that determine sequential drug efficacy through bistability-mediated state switching.

This study identifies a novel feed-forward signalling axis between PEAK1 and CAMK2 in TNBC, showing that targeting CAMK2 can inhibit PEAK1-driven oncogenic processes.

This study identifies ribosomal biogenesis hyperactivation and ErbB signaling reactivation as mechanisms of resistance to FGFR2 inhibitors in breast cancer cells and shows synergy with MEK inhibitors.

GeneSurv enables multi-gene cancer survival analysis by correlating gene expression with patient outcomes, demonstrating its utility in identifying prognostic gene associations in breast cancer.

SynDISCO uses mechanistic ODE modeling to predict synergistic drug combinations targeting signaling networks, demonstrated on EGFR-MET in triple negative breast cancer.

This paper develops a novel MDN modelling technique to study how heterogeneity influences adaptive resistance to CDK4/6 and estrogen receptor inhibitors through protein signalling dynamics.

The paper develops a computational model of FGFR4 signalling to identify cell type-selective combination therapies for FGFR4-driven cancers, revealing AKT and ERK reactivation as key resistance mechanisms.

The paper uses computational models and experiments to show mTORC1 activity depends biphasically on mTORC2 subunit Sin1 and highlights mLST8 as a therapeutic target in breast cancer.