Network-based modeling of herb combinations in traditional Chinese medicine

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Wang , Y , Yang , H , Chen , L , Jafari , M & Tang , J 2021 , ' Network-based modeling of herb combinations in traditional Chinese medicine ' , Briefings in Bioinformatics , vol. 22 , no. 5 , 106 , pp. 1-13 . https://doi.org/10.1093/bib/bbab106

Title: Network-based modeling of herb combinations in traditional Chinese medicine
Author: Wang, Yinyin; Yang, Hongbin; Chen, Linxiao; Jafari, Mohieddin; Tang, Jing
Contributor organization: Research Program in Systems Oncology
Department of Mathematics and Statistics
Department of Biochemistry and Developmental Biology
Medicum
Date: 2021-09
Language: eng
Number of pages: 13
Belongs to series: Briefings in Bioinformatics
ISSN: 1477-4054
DOI: https://doi.org/10.1093/bib/bbab106
URI: http://hdl.handle.net/10138/335319
Abstract: Traditional Chinese medicine (TCM) has been practiced for thousands of years for treating human diseases. In comparison to modern medicine, one of the advantages of TCM is the principle of herb compatibility, known as TCM formulae. A TCM formula usually consists of multiple herbs to achieve the maximum treatment effects, where their interactions are believed to elicit the therapeutic effects. Despite being a fundamental component of TCM, the rationale of combining specific herb combinations remains unclear. In this study, we proposed a network-based method to quantify the interactions in herb pairs. We constructed a protein–protein interaction network for a given herb pair by retrieving the associated ingredients and protein targets, and determined multiple network-based distances including the closest, shortest, center, kernel, and separation, both at the ingredient and at the target levels. We found that the frequently used herb pairs tend to have shorter distances compared to random herb pairs, suggesting that a therapeutic herb pair is more likely to affect neighboring proteins in the human interactome. Furthermore, we found that the center distance determined at the ingredient level improves the discrimination of top-frequent herb pairs from random herb pairs, suggesting the rationale of considering the topologically important ingredients for inferring the mechanisms of action of TCM. Taken together, we have provided a network pharmacology framework to quantify the degree of herb interactions, which shall help explore the space of herb combinations more effectively to identify the synergistic compound interactions based on network topology.
Subject: 111 Mathematics
3111 Biomedicine
natural products
herb combinations
network modeling
Traditional Chinese medicine (TCM)
formulae
network pharmacology
PROTEOME-SCALE MAP
DRUG DISCOVERY
HEPATIC-FIBROSIS
LIVER FIBROSIS
PANAX-GINSENG
DATABASE
PHARMACOLOGY
PAIRS
Peer reviewed: Yes
Rights: cc_by_nc
Usage restriction: openAccess
Self-archived version: publishedVersion


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