[An optimization method of weighted network module partition based on TCM theory of "monarch, minister, assistant and guide"].
Sihong Liu, Hanqing Zhao, Hongjie Gao, Lin Tong, Lei Zhang, Huamin Zhang
Abstract
The disease-gene-drug multi-level network constructed by network pharmacology can predict drug targets and has been widely used in the study of material basis and mechanism of action of Chinese medicinal prescriptions. However, most of the current studies have normalized the efficacies of Chinese herbal medicines in the compounds during the construction of the network. There is also a lack of in-depth exploration of the mechanism of synergy among multiple components. This study proposed a network module partition method based on group collaboration and the pharmacological network was weighed according to the traditional Chinese medicine(TCM) theory of "monarch, minister, assistant and guide". Taking the Tanyu Tongzhi Prescription as an example, we constructed its pharmacological network for the treatment of myocardial ischemia-reperfusion injury. The group collaboration module in the network was identified and the network changes before and after the weighting were compared based on the network topology analysis to explore a new method to find the core nodes of the network as well as the core drugs that affected the efficacy of the compounds. The results showed that the module partition method based on group collaboration could be used to identify and partition group collaboration mo-dules in pharmacological networks of compounds. The proposed weighted network based on the TCM theory of "monarch, minister, assistant, and guide" could identify and partition the modules based on the characteristics of the pharmacological network. The identification and partition results of modules of Tanyu Tongzhi Prescription in the weighted network were superior to those in the unweighted network. The weighted closeness centrality(WCC) evaluation method was conducive to finding key nodes and relations in the network as compared with traditional methods, thereby providing a basis for analyzing the core components of drugs and extracting more accurate drug components and targets.