A Novel Method for Drug Application on Signaling Pathways With Concurrent Faults Using <scp>PBNs</scp>

ABSTRACT Cellular processes are tightly regulated by various growth and transcription factors through information fluxes. They initiate cellular division and involve interactions between proteins termed signaling pathways. Dysregulated signaling can contribute to proliferative cellular states, and such pathway‐level alterations can be approximated using probabilistic network models. In this study, Boolean pathway representations are extended with probabilistic interaction weights to approximate uncertainty in pathway‐level protein–protein interactions, under simulated dysregulated signaling conditions. The simulated proliferative output states can be reduced under selected modeled drug‐combination scenarios. This study focuses on the signaling pathways' probabilistic nature, featuring concurrent multiple faults. Initially, modeling the signaling pathways to their respective Probabilistic Boolean Networks (PBNs), we observed their behavior in the presence of concurrent faults, followed by conventional drug therapy to mitigate the effect of these faults. The proposed framework introduces a Condensed_Probabilistic_Score (CPS) , which ranks modeled drug combinations according to their ability to reduce proliferative output states across simulated fault scenarios without requiring prior specification of the exact fault combination. Additionally, we propose a strategy for prioritizing candidate custom target nodes whose combinations produced higher CPS values than the modeled known‐drug combinations in the simulated PBN framework. Candidate combinations based on these custom target nodes are further evaluated as in silico intervention hypotheses. These findings should be interpreted within the assumptions of the abstracted PBN model and require biological validation using independent pathway resources and experimental perturbation studies.