The bioinformatics analysis of a computational network pharmacology model can accurately identify potent functional components and decipher potential mechanisms in traditional Chinese medical formulas.
In this research, scientists designed a novel bioinformatics analysis strategy in conjunction with a computational network pharmacology model. This model utilized the traditional Chinese medicine (TCM) formula Chai-Hu-Shu-Gan-San, employed in the treatment of depression, as its primary study subject. Initially, an effective intervention space was created for examining the transfer of intervention effects from individual component targets to pathogenic genes, with this space being developed through a unique method for calculating node importance. Following this construction, intervention-response proteins were identified from the intervention space, and a core group of functional components (CGFC) was selected based on these specific proteins.
Results derived from the analysis underscore the significant coverage of intervention-response proteins in the paths and Gene Ontology (GO) terms that cater to major functional therapeutic effects. Out of 1,012 components, 71 were predicted as CGFC. Interestingly, the targets of CGFC were enriched in pathways that were seen to cover the most dominant proportion of pathogenic gene-enriched pathways. This led to the inference and subsequent validation of two primary mechanism chains based on the CGFC. Evaluative experiments were also conducted on the core components present within the CGFC, with results underlining the impressive accuracy of the proposed model in scrutinizing the CGFC and deducing potential mechanisms within TCM formulas.
