Resumen
The heterogeneous response of cancer cells to targeted drugs is associated with the state transition dynamics of a molecular network. Identifying combinatorial drug targets to compensate for these heterogeneous responses can counteract adaptive resistance in cancer. To achieve this, we developed an algorithm called ?merged transition map?, which explores essential state transition dynamics to identify combinatorial drug targets. Our analysis showed that drug-induced state conflicts within the molecular regulatory motifs of a network can result in heterogeneous responses. Moreover, we found that addressing these conflicts with additional perturbations can synergistically improve drug efficacy. Compared to other network control algorithms, our approach showed higher performance in drug efficacy of the suggested combinatorial target pairs with reduced computational complexity. Furthermore, by applying the MTM on a Boolean network model, we identified a new target combination that induces apoptosis in gastric cancer, supported by previous experimental data.