Speaker
Description
Traditional computational approaches struggle to capture the complex interactions that occur across multiple molecular layers in disease systems, particularly as the volume of biological data continues to grow. We developed mathematical model–based computational tools to identify potential drug targets from large-scale datasets. Focusing on bistability in cell signalling networks, we investigated how protein–protein interaction (PPI) motif structures influence input–output (I/O) relationships. A model-based analysis is conducted to explore the critical conditions underlying the emergence of distinct bistable protein–protein interaction (PPI) motifs and their potential applications for identifying drug targets. The influence of stochastic perturbations that could hinder the desired functionality of any signalling networks is also explored. To account for intrinsic cellular noise, we employed stochastic differential equation (SDE) models to study the relationship between motif architecture and signal–noise dynamics. We quantified node vulnerability to stochastic perturbations and identified noise-sensitive, highly druggable motifs as promising therapeutic targets. Finally, we proposed a theoretical framework for systematic drug-target identification and applied it to three cancer signalling networks, validating the predicted targets using established databases.
