Data-driven systems biology models of signaling predict cellular response to untested perturbations and can nominate drug combinations to overcome drug resistance in cancer cells.

The transcriptomic profiles of the constituent monotherapies of synergistic drug pairs tend to be correlated and result in novel gene expression in the combinations.

Engineered local heterogeneity in drug concentration is used as a tool to encode drug treatment regimens and to predict the macroscopic cellular response to drug perturbations.

Knowledge graph identified an herbal drug combination for the treatment of plasma cell mastitis.

Drugs in a curative chemotherapy regimen are independently effective and resisted by different mechanisms, so cancer cells have little chance of surviving all drugs, and this benefit occurs without synergistic interactions.

Models of malaria and treatment dynamics were combined with emulator-based global sensitivity analysis to elucidate the interplay between drug, biology, and epidemiological factors on the evolution of resistance to malaria treatments, including artemisinin.

Combination of glutaminase inhibitor CB-839 and ASCT2 inhibitor V-9302 showed efficient antitumor effect against glutamine addicted liver cancer cells via glutathione depletion and reactive oxygen species (ROS) induction.

Libraries of patient-derived tumor organoids are a reliable and scalable model system that can help identify and optimize targeted therapies in a pre-clinical setting.

A mathematical model of osteoporosis explains why the sequence of osteoporosis medications matters for short-term and long-term treatment success.

Tailoring Boolean models to 488 prostate cancer patients and 8 cell lines data allows for the experimentally validated personalisation of drug treatments.