Computational Approaches to Identify Key Therapeutic Targets in PPIN
DOI:
https://doi.org/10.63682/jns.v14i15S.4129Keywords:
PPIN, Domination, MCDS, Centrality, Rank correlation, Protein targetsAbstract
In recent years, the study of biological networks, particularly protein-protein interaction networks (PPINs) has gained attention due to their critical role in understanding disease mechanisms and therapeutic targeting. In this study, we apply computational graph theory and mathematical metrics to analyze the SARS-CoV-2 human PPIN with the goal of identifying novel therapeutic targets. This framework is based on domination theory, which identifies key proteins within the PPIN that could serve as crucial targets for drug delivery and therapeutic interventions. Our findings suggest that focusing on a refined subnetwork of these key proteins rather than the entire network could provide valuable insights for developing targeted therapies and efficient drug delivery systems. This study demonstrates the power of advanced computational techniques in solving complex biomedical challenges, particularly in the development of targeted medical therapies and drug delivery technologies.
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