|Year : 2017 | Volume
| Issue : 3 | Page : 28-37
Progress of studies on traditional chinese medicine based on complex network analysis
Qian-Ru Zhang1, Xiang-Jun Kong1, Hai-Yu Xu2, Yi-Tao Wang3, Yuan-Jia Hu1
1 Specialty Committee on Network Pharmacology, World Federation of Chinese Medicine Societies, Beijing 100084; State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China
2 Specialty Committee on Network Pharmacology, World Federation of Chinese Medicine Societies; Institute of Chinese Materia Medica, Academy of Chinese Medical Sciences, Beijing 100700, China
3 State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China
|Date of Submission||02-Oct-2016|
|Date of Acceptance||27-Apr-2017|
|Date of Web Publication||24-Oct-2017|
State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078
Source of Support: None, Conflict of Interest: None
Traditional Chinese medicine (TCM) is a distinct medical system that deals with the life–health–disease–environment relationship using holistic, dynamic, and dialectical thinking. However, reductionism has often restricted the conventional studies on TCM, and these studies did not investigate the central concepts of TCM theory about the multiple relationships among life, health, disease, and environment. Complex network analysis describes a wide variety of complex systems in the real world, and it has the potential to bridge the gap between TCM and modern science owing to the holism of TCM theory. This article summarizes the current research involving TCM network analysis and highlights the computational tools and analysis methods involved in this research. Finally, to inspire a new approach, the article discussed the potential problems underlying the application of TCM network analysis.
Keywords: Complex network analysis, formula, mechanism, syndrome, traditional Chinese medicine
|How to cite this article:|
Zhang QR, Kong XJ, Xu HY, Wang YT, Hu YJ. Progress of studies on traditional chinese medicine based on complex network analysis. World J Tradit Chin Med 2017;3:28-37
|How to cite this URL:|
Zhang QR, Kong XJ, Xu HY, Wang YT, Hu YJ. Progress of studies on traditional chinese medicine based on complex network analysis. World J Tradit Chin Med [serial online] 2017 [cited 2020 May 25];3:28-37. Available from: http://www.wjtcm.net/text.asp?2017/3/3/28/217102
| Introduction|| |
Unlike allopathic Western medicine, traditional Chinese medicine (TCM) emphasizes managing the life–health–disease–environment relationship and regaining the balance of one's bodily functions, based on holistic, dynamic, and dialectical thinking. Thus, the holistic concepts of TCM coincide with the properties of a complex system in emergence, multidimensionality, nonlinearity, etc., However, the reductionism of Western medicine has restricted the experimental research on TCM to focusing on elucidating the chemical structures and bioactivities of single compounds, instead of tapping into the core concept of TCM, namely, the multiple relationships between different systems. Moreover, compared with the abundant achievements in the study of the chemical ingredients in Chinese materia medica (CMM),, the modernization of TCM has developed slowly and been obstructed by the negligence of the theoretical system of TCM.
A complex network approach has been proposed to elucidate the wide variety of complex systems in physics, mathematics, economics, sociology, biology, and medical science. These complex systems in the real world are commonly characterized by scale-free degree distribution and small-world effect rather than random graphs.,, Complex network approach has revealed some significant properties of real world including the global properties of natural and artificial systems, and the unifying principles on the basis of network architectures. Specifically speaking, complex network approach has been applied to various types of networks, such as social collaboration network, internet network, logistical network, epidemic spreading network, and energy-engineering network. More importantly, complex network approach shows promising application prospects in biology and medicine. Several pioneering interdisciplinary fields, such as systems biology, network biology, and network medicine, have explored the multiple correlations between biological systems, rather than single units, to unveil the complicated nature of life and disease from a systems view., Meanwhile, network pharmacology and system pharmacology both have integrated information from chemistry, pharmacology, informatics and biology, provided platforms to explore multiple interactions among genes, drugs, and diseases, and finally promoted the radical transformation of the drug development paradigm.,
Owing to its complementarity with the holistic and complex character of TCM, complex network analysis is expected to bridge the gap between TCM and modern medicine. First, TCM network pharmacology stems from three initial research works as below. As a founder of TCM network pharmacology, Li originally proposed a hypothesis that there are potential associations between TCM Syndrome (ZHENG in mandarin) and molecular networks in 1999. On March 2006, biomolecular networks of cold/hot syndromes were first established, while intervention effects of hot/cold herbal formulae on hub nodes in the above biomolecular networks were demonstrated experimentally. In 2007, a milestone article proposed a new network-based TCM research paradigm, in which the relationships of formula-syndrome and of disease-syndrome can be clearly explicated by biological networks and synergistic actions within a herbal formula are regarded as a kind of network connections triggered by actions of multiple ingredients of a formula on targets. Afterward, the term “network pharmacology” was first introduced in a “news and views” article in Nature Biotechnology by Hopkins.
On the basis of above pioneering works, subsequent researchers have conducted studies on TCM network pharmacology, network toxicology, and network formulaology by integrating network approaches and TCM theory, and they have applied these approaches to explore the molecular mechanisms of CMMs or Chinese medicine formulas (CMFs), combinational roles, and TCM clinical principles.,, These studies have integrated methods from pioneering disciplines, such as network pharmacology, systems biology, bioinformatics, and cheminformatics, to explore the scientific significance of TCM from a systems perspective. The first part of this article outlined the general procedure of TCM network research. The second part introduced the computational approaches, data sources, and tools involved in the network-based research on TCM. The third part discussed network topological analysis. Finally, the fourth part discussed the current research status of TCM complex network analysis in the study of the biological mechanism of TCM syndrome, TCM therapeutic principles, combination role of CMMs, and molecular basis of CMMs/CMFs.
| A Development Process of the Network Research on Traditional Chinese Medicine|| |
The progression of the TCM network research is as follows: (1) Various units, such as CMMs, molecules, cells, organs, and diseases, are derived from databases, literature, experiments, and computational simulations; (2) these units are vertices which construct complex networks through the multiple correlations among them, and the complex network models are involved in the CMM combination network, compound/drug-target network, metabolism network, signaling pathway network, protein–protein interaction (PPI) network, drug-disease network, etc.; (3) the scientific significance of TCM is illustrated by integrating multiple disciplines, including bioinformatics, polypharmacology, and systems biology, on the basis of TCM theory; and (4) the computational results might be enhanced with further experimental validation. According to the holistic concept of CMM–compound–target–disease, the TCM complex network research has uncovered potential evidence, under the network topological structure, of a relationship between the structure and function of the network. Moreover, this promotes explanations of the scientific significance of TCM syndromes, principles of clinical therapy, combinational role of CMMs, and molecular basis of CMMs/CMFs.
| Methods and Tools of Complex Network Analysis|| |
For studies using TCM network analysis, it is possible to obtain comprehensive information from multiple sources, such as databases, experiments, literatures, and computational modeling. Furthermore, it is possible to visualize and analyze the network using network tools.
There are large-scale biological and chemical databases that can support TCM complex network analysis. These databases offer a wealth of information on various units as well as their interactions. [Table 1] shows the classification of the resources as the following four categories in accordance with the type of information that these databases contain: (1) biological databases providing extensive information on biomolecules (PDB, UniProtKB), biomolecular interactions such as PPI (BioCarta, HPRD), gene ontology, and signaling pathways (KEGG, Reactome); (2) chemical or drug databases that provide useful cheminformatics and bioinformatics relating to chemical or drug substances, such as bioinformatics (ChemSpider), chemical structures, as well as physicochemical properties (ChemProt), comprehensive targets (DrugBank, ChEMBL, PubChem), bioactivities (STITCH), and drugs and diseases (TTD); (3) disease/phenotype databases on the phenotypes of human diseases and their related genes; and (4) TCM-related databases that provide comprehensive information on CMM (TCM database@Taiwan, CVDHD), CMFs (TCMID, NSTI), chemical ingredients, and relevant target proteins (HIT, TCMSP).
|Table 1: Databases for complex network analysis of traditional Chinese medicine|
Click here to view
Text mining tools
The reliability of studying TCM from the perspective of complex network analysis depends on the accuracy and availability of the data. As a sample from a single source that has limited scope and bias, it is beneficial to use multiple databases and various retrieval methods simultaneously to ensure the integrity and accuracy of the data and information. Besides databases, the scientific literature is another significant data source. One can obtain such data by using tools for text mining, such as Agilent literature search, Polysearch, and SciMiner.,, Otherwise, it is possible to explore and analyze the biomolecule–pathway–disease correlation using bioinformatics tools, such as Clugo and DAVID, to illustrate the biological signaling pathway and bioactive mechanism.,
Often, the interactions between chemical and biological substances are too finite to support certain research into TCM using network analysis that involves searching databases or conducting text mining. However, computational methods are extremely useful for predicting and simulating unexplored interactions from acknowledged information. The technologies for computational simulation can be divided into the following four categories as listed in [Table 2]: (1) computation modeling, such as a pharmacophore model and quantitative structure-activity relationships; (2) molecular docking; (3) similarity ensemble methods; and (4) machine learning algorithm including binary kernel discrimination, support vector machine (SVM), random forest, Bayesian classification, etc.
|Table 2: Computational simulation technologies for traditional Chinese medicine complex network analysis|
Click here to view
Li et al. provided 14 algorithms (CIPHER, drugCIPHER, comCIPHER, CIPHER-HIT, DMIM, NADA, NIMS, SAF, LMMA, CSPN, sGSCA, ClustEx, GIFT, and DGPsubNet) and 2 platforms (HerbBioMap and dbNEI) for TCM network pharmacology based on the “network target” paradigm., The applications of these methods have further involved in the study of the activity of compounds in CMM or CMF, synergism of CMMs, the disease–syndrome–formulae relationship, and biological markers.
The application of network tools is mainly for carrying out the visualization and topological analysis of a complex network. Network visualization tools are appropriate for mapping the indescribable relationships between connected units. Subsequently, using network analysis tools can uncover information hidden under the network topological structure. These two types of network tools are usually integrated using the same software. The major network tools involved in the study of TCM were initially developed for biological network analysis or social network analysis, such as Cytoscape, Pajek, Ucinet, Netdraw, NetMiner, Gephi, Guess, networkX, and so on.,,,,, Besides this professional network software, network toolboxes of Matlab and R environment can be utilized in the network visualization and topological analysis.,
| Network Topological Analysis|| |
According to the graph theory and complex network approach, nodes in a complex network represent the units in the complex system, and edges indicate the relationships among them. The primary components of a complex network on which we focus are nodes, edges, and clusters. Then a network analysis can illustrate the inner correlation of the network function and topological structure. Subsequently, the analysis and application of the properties of the complex network, including heterogeneity, centrality, shortest path length, clustering coefficient, centralization, density, and degree distribution, can elucidate the topological structure. Network topological analysis plays an important role in understanding the network structure, discovering the hidden information, and predicting the function of the complex network.
Analysis of nodes
A node (or vertex) is an indispensable element of a complex network. To estimate the significance of nodes, centrality is a typical parameter for evaluating the status of nodes in the whole network. As Freeman formalized in 1978, degree, betweenness, and closeness are the three prime measurements of centrality, as shown in [Table 3]. The degree of a focal node is the number of its adjacencies in the network; betweenness is the sum of the number of shortest paths passing that focal node, and closeness is defined as the inverse sum of graph-theoretic distances from all other connected nodes., According to the three different measures of centrality, higher degree or betweenness indicates more significance, whereas higher closeness means less significance. In TCM network analysis, many types of complex networks, such as a metabolic network or Chinese medicine (CM) combinational network, have shown identical properties, such as scale-free or power-law degree distribution. In such scale-free networks, small series of nodes with high degree, also called hubs, play a crucial role in discovering the key CMMs, screening active compounds, and predicting potential therapeutic targets.,, For instance, Kong et al. constructed a molecular network in the mechanism of thrombosis by combining platelet signaling, coagulation cascade, and natural clot dissolution systems, and further predicted potential therapeutic antithrombotic targets through a centrality analysis of the vertices.
Analysis of edges
As another essential element of a complex network, an edge (or arc) is a link between two different units as well as a path for information transmission within the network. The analysis of edges mainly concerns the weight and the shortest path. First, the capability or capacity of the association between a pair of adjacent nodes can be quantified as the weight of an edge. The weight of an edge indicates the strength of the link between two adjacent nodes to illustrate pairwise nodes. As an illustration, a pair of CMMs with high weight value in a formulae combination network might have high co-occurrence frequency and have a synergetic effect in clinical therapy., However, the shortest path plays a crucial role in the transmission and communication in a network. Closeness centrality and betweenness centrality are measured through the application of the shortest path. Meanwhile, the distance between any two indirectly connected nodes is defined as the number of edges along the shortest path between them., In the biological field, the shortest path is helpful for predicting the function of unknown genes from known genes and identifying functional modules of proteins in a PPI network.
Analysis of network community
A network community (or modularity, cluster, module) is a series of nodes with tight intra-community interactions and loose intercommunity correlations. That is, the nodes are homogeneous in the same community and heterogeneous between communities. The available methods for detecting community structures are as follows: (1) participation graph clustering including a Kernighan–Lin algorithm, fast Newman algorithm, Guimera–Amaral algorithm, etc.; (2) clustering based on a heuristic algorithm, such as maximum flow community, hyperlink-induced topic search, Girvan–Newman, etc.; (3) hierarchical clustering including random walk similarity, clustering centrality, etc.; and (4) spatial clustering, such as K-mean. The homogeneity of a network community plays a crucial role in the topological analysis of biological networks aimed at identifying units with high similarity in function or structure.,, The studies on TCM network analysis have utilized network communities to uncover the core combinations of CMMs, chemical clusters with similar structures or bioactivities, and proteins with analogs functions.
| Application of a Complex Network in the Study of Traditional Chinese Medicine|| |
The holistic thinking behind TCM about the associations among CM, life, disease, and environment penetrates every aspect of the TCM therapeutic strategy, described as principle–method–prescription–CMMs (Li-Fa-Fang-Yao in Mandarin). The foundational concepts and clinical practices of TCM simultaneously focus on handling the complicated relationships between different systems  and emphasize restoring homeostasis, described as yin and yang in equilibrium. It can be said that the TCM complex system has four different levels: (1) complex adaptive system of human-to-natural environment; (2) various functional systems of the body, such as the visceral organs and relevant functions (Zangfu in Mandarin), different physiological systems involved in immunity (Defense), respiratory (Qi), digestion (Nutrient), circulation (Blood), and so on (Wei-Qi-Ying-Xue in Mandarin), meridian-collateral system (Jingluo in Mandarin); (3) four diagnostic methods for personalized therapy (inspection, listening and smelling, inquiry, and pulse-taking and palpation), syndrome differentiation through eight principles, and syndrome-based treatments; (4) the properties of CM including the theory of flavor-meridian tropism (Xingwei-guijing in Mandarin), combination roles based on the compatibility of CMMs, and the diverse effects of different CMMs in a formula (sovereign–minister–assistant–courier).
Studies on TCM have employed complex network methods to illuminate the holistic properties of TCM at multiple levels and thus elucidate the TCM theory and clinical application, material basis, and relevant mechanism. This section mainly reviews the application of the complex network approach in TCM studies, primarily for investigating the biological mechanisms of syndromes, principles of clinical therapy, formulation theory, and CM-oriented compounds as well as bioactivities.
Exploring the biological mechanism of traditional Chinese medicine syndrome
As a fundamental principle of TCM for the prevention and treatment of disease, TCM syndrome (or pattern) differentiation plays a crucial role in making treatment decisions, guiding the administration of CMMs, and dispensing herbal formulae. Unlike Western medicine, which approaches disease from the perspective of anatomy, TCM syndrome is the comprehensive description of the outward manifestation of the pathological, physical, and psychological status of a specific environmental aspect. Thus, TCM syndrome can be characterized as a multidimensional complex system that is concerned with pathological and physical conditions simultaneously. The complex characteristics of a syndrome are as follows: (1) it emphasizes the integrity of the human body rather than a certain lesion distribution; (2) it is not only a stage of pathogenesis but also a progress in flux, and distinctive syndromes can be co-occurring or interconverting; (3) a modern disease can be differentiated into multiple syndrome types, and a syndrome is also involved in various modern diseases; and (4) the molecular mechanism of TCM syndrome is too complicated to be illustrated by current physiological or biochemical indexes., The holistic and complex properties of a syndrome coincide with the hypothesis about the biological network of network pharmacology. Thus, TCM complex network analysis might offer a new strategy for exploring the biomolecular mechanism of a syndrome and its relationships with diseases, CMMs, and CMFs.
Following the phenotype network–biological network–herb network structure, Li et al. used literature mining, network analysis, and topological comparison to explore the biomolecular basis of cold/hot syndromes within the context of the neuro-endocrine-immune system (NEI). The results in silico validated the different biological patterns identified in the cold/hot syndrome networks. An in vivo experiment further confirmed that some key genes and chemical messengers distinguished cold and hot syndromes through the corresponding cold/hot herbal treatment. Lu et al. integrated microarray technology and PPI network analysis to explore the distinct molecular signatures of TCM cold and hot patterns in rheumatoid arthritis (RA) patients. Their study indicated that the cold pattern of RA was related to the Toll-like receptor signaling pathway, and the related pathways in the heat pattern involved the calcium signaling pathway, cell adhesion molecules, PPAR signaling pathway, and fatty acid metabolism. In addition, Lu et al. explored the correlations between the PPI network of an RA hot pattern and the target protein networks of four relevant CMMs through microarray technology, text mining, and complex network analysis. The mutual pathways of the RA hot pattern and CMMs include GM-CSF signaling, CLTA4 signaling in Cytotoxic T Lymphocytes, T cell receptor signaling, and CD28 signaling in T Helper cells, which were related to the cell survival, cell-mediated immune response, inflammatory response, and so on. Dai et al. conceived a two-level model, integrating a SVM-based diagnostic model and a symptom-gene-pathway multilayer correlation network, to detect the molecular mechanism of wind-phlegm collateral obstruction syndrome of acute ischemic stroke.
Discovering the underlying principle of traditional Chinese medicine therapy
The therapeutic purpose of TCM is to regulate homeostasis, remove pathogenesis, relieve symptoms, and finally promote physical and emotional health. The complex properties of TCM therapy can be further categorized as follows: (1) regaining the balance of the physiological functions of different subsystems at the global level; (2) making the TCM therapeutic schedule individualized and flexible on the basis of the pathogenic factors, the pathological process, physical and psychological conditions, and environmental aspects; and (3) identifying the individual subjective factors of TCM physicians and patients that affect the TCM clinical therapy. Restricting the worldwide application of TCM therapy are the lack of appropriate research approaches and a long history of an irreconcilable philosophical divergence between holistic CM and allopathic Western medicine. Yet modern medicine has gradually validated that most diseases, especially chronic diseases and complex diseases, are induced by various factors and have multiple mechanisms. Moreover, the perspective of network pharmacology acknowledges that a complex disease is caused by the perturbation of the biological network, while the random variation of individual genes or proteins has little effect on disease. Thus, the concept of TCM therapy aligns with the modern medical concept of complex diseases and the hypothesis about a biological network.
Complex network analysis provides a powerful tool for decoding the connotation of TCM therapy. The study of network analysis in relation to TCM therapy can adopt either a bottom-up or top-down approach according to the correlation among drugs, life, health, and disease. Gao et al. used complex network analysis to explore the syndrome–therapy–CM relationship within TCM therapy in the context of coronary artery disease (CAD). Moreover, they analyzed the topological structure of the syndrome–clinical therapy network to estimate the underlying treatments and CMMs that were not included in the TCM diagnostic standard of CAD. Furthermore, Li et al. explored the pharmacological effect of the Bufei Yishen formula (BYF, Formula of Tonifying Lung and Replenishing Kidney) against chronic obstructive pulmonary disease (COPD) by integrating network analysis and bioinformatics. Their computational result uncovered the active compounds in BYF and the relevant potential targets through the compound–target network. Their experiment validated that the underlying mechanism of BYF against COPD might contribute to the inflammatory cytokine, hypertrophic factors expression, protease-antiprotease imbalance, and collagen deposition. Wang et al. took a novel approach to dissecting with holistic treatment in the TCM therapy of cardio-cerebrovascular diseases (CCDs) using systems pharmacology and complex network analysis. They screened the potential bioactive ingredients in the Xinnaoxin Pill by integrating the absorption–distribution–metabolism–excretion (ADME) model and complex network analysis based on the multiple layers of drug–target–pathway–organ, and further experimentally validated at the molecular level. Finally, they extracted a disease-therapeutic module for understanding the complex disease at the global level.
Understanding combinations in traditional Chinese medicine
The CM formula (Fufang in Mandarin), which follows the TCM therapeutic principle, can be characterized as the most typical treatment in the personalized therapy of TCM. With the purpose of synergy and attenuation, several CMMs are carefully dispensed into a specific prescription and play an integrative role for eliminating the symptoms and regulating the body's homeostasis. CMFs have been accumulated from clinical experience and verified in TCM therapy. However, we still lack a full understanding of the combination roles, dose-effect relationships, and molecular mechanisms relating to CMFs. And, furthermore, the complex properties of CMFs might also increase the difficulty of applying and modernizing TCM, which are as follows: (1) the pharmacological effects of CMFs depend on the combinational principles and status of different CMMs; (2) the pharmacological efficacy of CMFs is not a simple linear superposition but the synergistic activity of CMMs with multiple compounds; and (3) CMFs might be adjusted in CMMs or dosages to coincide with the patient's pathological and physiological status.
Complex network analysis plays an important role in revealing the complex properties of CMFs in the combination principle, active compounds, and biological mechanisms. For example, to identify useful relationships among herbs in numerous CMFs, Li et al. established a novel DMIM method combining mutual information entropy and “between-herb-distance” to score herb interactions and construct a herb network. The outcomes of this research were the construction of a herb network comprising 3,865 collaterals-related CMFs, the retrieval of herb pairs and formulae, and the generation of further novel anti-angiogenic herb ingredients. The researchers also integrated network-based methods to illustrate the network regulation mechanism and identify active ingredients as well as synergistic combinations for Qing-Luo-Yin (QLY, Drink of Clearing Lung Collaterals), an anti-RA formula. Their research predicted synergetic activities and the combinatorial rule of QLY and identified several ingredient groups as active components through the use of cluster analysis. As the combination of CMMs in a CMF is essentially the combination of compounds, Ding et al. constructed a combination network of compounds based on the combinational relationship of CMMs in the CMF. In total, 1,588 compounds in 8 classic CMFs against CAD were clustered into 9 network communities using topological analysis, and 20 compounds with high centrality were screened out.
Exploring the material basis of traditional Chinese medicine
Abundant effective CMMs have been clinically validated and used for thousands of years under the theoretical system of TCM. Regular studies on CMMs have focused primarily on identifying the chemical ingredients and chemical structures, physicochemical properties, pharmacodynamics, and pharmacokinetic process. However, each CM can be regarded as a complicated chemical system with numerous compounds that are too complex to be investigated solely by using conventional empirical approaches. Moreover, the pharmacological activities and adverse effects of CMMs are mainly the result of the interactions between multiple compounds on multiple targets. The complex properties of the molecular basis of CMMs may include the following: (1) the compounds of CMMs vary in their chemical elements, chemical structures, physicochemical properties, and bioactivities; (2) the pharmacokinetic progress of multiple components is too complicated to be elucidated, and some metabolites of phytochemicals are also bioactive; and (3) the comprehensive bioactivities of CMMs are characterized as emergent due to the synergetic effects of compounds on multiple targets. Confronted with the complex issue of CMMs, the current reductionist approaches are limited to illustrating the molecular mechanisms from a global perspective.
Based on the correlations among disease, gene, target protein, and compound, a complex network analysis can be applied to analyze and dissect, with the molecular basis as well as the relevant biological mechanism, and provide theoretical and technical rationales for the efficiency and safety of CMMs or CMFs. Xu et al. elucidated the anti-cancer effects of the compounds in the Xiao-Chai-Hu Decoction (XCHT, Minor Bupleurum Decoction) by combining the methods of chemical space mapping, molecular docking, and network analysis. Their research enabled them to construct a compound–compound network based on multiple compound-target interactions and demonstrate the multi-compound combinations that might be effective on differentiated targets. Zhang et al. predicted the candidate targets for comprehensive compounds of the Wu-Tou Decoction (WTD, Aconite Main Root Decoction) against RA using the drugCIPHER-CS platform. They identified major hubs of the PPI network as candidate targets for WTD by analyzing the degree centrality, which was implicated in the NEI system and validated in further experiments. Yang et al. unified the chemical analysis, prediction of ADME, and network pharmacology to highlight the active components in Dragon's blood tablet (DBT, Resina Draconis Tablet). Finally, they investigated 22 potentially absorbed components and 8 metabolites of DBT and determined that the molecular mechanism of these active components might be involved in the nucleotide-binding oligomerization domain-like receptor (NLRs) signaling pathway.
However, the complex network approach also provides an important clue for the toxicological mechanism of CMMs. For example, to explore the biomarkers and toxicity mechanism of processed Aconitum carmichaelii Debx tablet (Baifupian [BFP] in Mandarin], Zhang and Lu et al. comprehensively utilized serum metabolomics and complex networks to illustrate betaine and phosphatidylcholine as the biomarkers for the toxic responses of BFP. The toxic effects of BFP might be partially due to the perturbations of RhoA signaling, choline metabolism, and free radical scavenging.
| Conclusion|| |
Researchers have used complex network analysis to illustrate the complex correlations between different units and uncover the underlying principles of TCM theory and clinical practice. Despite the abundant achievements in the study of TCM network analysis, researchers have yet to clarify the deeper significance of TCM. They face many challenges as follows. First, although it is possible to use complex network analysis to derive the therapeutic role of TCM in treating diseases on the basis of the number of clinical practices, such an approach has yet to penetrate the scientific principles of TCM theory, such as TCM pharmacology, properties theory of CMMs, and combination role of CMFs. Second, based on the CM–compound–target–disease relationship, significant molecules as well as the bioactive mechanisms can be illustrated or predicted through complex network analysis. However, the comprehensive effects of activations/inhibitions/competitions of multi-compounds on the biological system are not a simple linear superposition, and we lack appropriate approaches for studying the synergetic effects of compounds. Third, an important property of TCM is its dynamic character. Many aspects of TCM can be characterized as dynamic systems including syndromes, TCM therapeutic schedules, the ADME progress of compounds in CMMs/CMFs, etc., However, the focus in the research involving TCM network analysis has tended to be on the static analysis of topological structure to investigate veiled herb pairs, chemical combinations, target groups, and so on, whereas dynamic network analyses of TCM are nearly absent. Fourth, having reliable data are the essential condition for a successful analysis of the TCM network. Although it is possible to retrieve abundant information about chemistry and biology from databases, literature, high-throughput experimentation, or computational modeling, the lack of sufficient interactions between different units, especially compounds and targets, might present an obstacle to constructing an integrated and predictable network. Finally, the computational results of TCM network analysis should be validated by further supplementary experiments.
Although the TCM network analysis faces serious challenges in its infancy, its prospects are still promising, especially with the development of big data, computational capabilities, and experimental methods. In the future, pharmacodynamic material basis of CMMs, synergistic combinations among CMMs, pharmacological mechanisms of CMFs, and indications of TCM therapies may be elucidated through TCM network analysis. Thus, this approach is expected to bridge the gap between traditional medicine and modern medicine, allow for investigating the scientific basis of TCM theory, and accelerate the modernization of TCM.
We would like to thank for financial support for this research by the projects: FDCT013/2015/A1 by the Science and Technology Development Fund of Macao SAR and MYRG2015-00172-ICMS-QRCM and MYRG2016-00144-ICMS-QRCM by the University of Macau.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Jiang WY. Therapeutic wisdom in traditional Chinese medicine: A perspective from modern science. Trends Pharmacol Sci 2005;26:558-63.
Qiu J. Traditional medicine: A culture in the balance. Nature 2007;448:126-8.
Miller LH, Su X. Artemisinin: Discovery from the Chinese herbal garden. Cell 2011;146:855-8.
Shen ZX, Chen GQ, Ni JH, Li XS, Xiong SM, Qiu QY, et al.
Use of arsenic trioxide (As2O3) in the treatment of acute promyelocytic leukemia (APL): II. Clinical efficacy and pharmacokinetics in relapsed patients. Blood 1997;89:3354-60.
Albert R, Barabási AL. Statistical mechanics of complex networks. Rev Modern Phys 2002;74:47.
Fell DA, Wagner A. The small world of metabolism. Nat Biotechnol 2000;18:1121-2.
Newman ME. The structure of scientific collaboration networks. Proc Natl Acad Sci U S A 2001;98:404-9.
Boccaletti S, Latora V, Moreno Y, Chavezf M, Hwang DU. Complex networks: Structure and dynamics. Complex Syst Complexity Sci 2007;424:175-308.
Barabási AL, Albert R, Jeong H. Scale-free characteristics of random networks: The topology of the world-wide web. Physica A 2000;281:69-77.
Guimera R, Amaral LA. Modeling the world-wide airport network. Eur Phys J B 2004;38:381-5.
Shi H, Duan Z, Chen G. An SIS model with infective medium on complex networks. Physica A 2008;387:2133-44.
Pagani GA, Aiello M. The power grid as a complex network: A survey. Physica A 2013;392:2688-700.
Barabási AL, Oltvai ZN. Network biology: Understanding the cell's functional organization. Nat Rev Genet 2004;5:101-13.
Barabási AL, Gulbahce N, Loscalzo J. Network medicine: A network-based approach to human disease. Nat Rev Genet 2011;12:56-68.
Hopkins AL. Network pharmacology: The next paradigm in drug discovery. Nat Chem Biol 2008;4:682-90.
Berger SI, Iyengar R. Network analyses in systems pharmacology. Bioinformatics 2009;25:2466-72.
Li S. Mapping ancient remedies: Applying a network approach to traditional Chinese medicine. Science 2015;350:S72-4.
Li S. Possible Relationship Between Traditional Chinese Medicine ZHENG and Molecular Networks. The First Academic Annual Meeting of the China Association for Science and Technology; 1999.
Li S, Zhang ZQ, Wu LJ, Zhang XG, Li YD, Wang YY, et al.
Understanding ZHENG in traditional Chinese medicine in the context of neuro-endocrine-immune network. IET Syst Biol 2007;1:51-60.
Li S. Framework and practice of network-based studies for Chinese herbal formula. Zhong Xi Yi Jie He Xue Bao 2007;5:489-93.
Hopkins AL. Network pharmacology. Nat Biotechnol 2007;25:1110-1.
Li S, Zhang B. Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chin J Nat Med 2013;11:110-20.
Fan X, Zhao X, Jin Y, Shen X, Liu C. Network toxicology and its application to traditional Chinese medicine. Zhongguo Zhong Yao Za Zhi 2011;36:2920-2.
Fan XH, Cheng YY, Zhang BL. Network formulaology: A new strategy for modern research of traditional Chinese medicine formulae. Zhongguo Zhong Yao Za Zhi 2015;40:1-6.
Nishimura D. BioCarta. Biotechnol Softw Internet Rep 2001;2:117-20.
Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M, et al.
BioGRID: A general repository for interaction datasets. Nucleic Acids Res 2006;34:D535-9.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al.
Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet 2000;25:25-9.
Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, et al.
Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res 2004;32:D497-501.
Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27-30.
Brown KR, Jurisica I. Online predicted human interaction database. Bioinformatics 2005;21:2076-82.
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al.
The protein data bank. Nucleic Acids Res 2000;28:235-42.
Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, et al.
PID: The pathway interaction database. Nucleic Acids Res 2009;37:D674-9.
Bader GD, Cary MP, Sander C. Pathguide: A pathway resource list. Nucleic Acids Res 2006;34:D504-6.
Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, et al.
Reactome: A knowledgebase of biological pathways. Nucleic Acids Res 2005;33:D428-32.
Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, et al.
The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 2011;39:D561-8.
UniProt Consortium. Reorganizing the protein space at the universal protein resource (UniProt). Nucleic Acids Res 2012;40:D71-5.
Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, et al.
ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res 2012;40:D1100-7.
Taboureau O, Nielsen SK, Audouze K, Weinhold N, Edsgärd D, Roque FS, et al.
ChemProt: A disease chemical biology database. Nucleic Acids Res 2011;39:D367-72.
Pence HE, Williams A. ChemSpider: An online chemical information resource. J Chem Educ 2010;87:1123-4.
Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al.
DrugBank: A comprehensive resource for in silico
drug discovery and exploration. Nucleic Acids Res 2006;34:D668-72.
Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH, et al.
PubChem: A public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009;37:W623-33.
Kuhn M, Szklarczyk D, Franceschini A, Campillos M, von Mering C, Jensen LJ, et al.
STITCH 2: An interaction network database for small molecules and proteins. Nucleic Acids Res 2010;38:D552-6.
Chen X, Ji ZL, Chen YZ. TTD: Therapeutic target database. Nucleic Acids Res 2002;30:412-5.
Piñero J, Queralt-Rosinach N, Bravo À, Deu-Pons J, Bauer-Mehren A, Baron M, et al.
DisGeNET: A discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford) 2015;2015:bav028.
Stenson PD, Ball EV, Mort M, Phillips AD, Shiel JA, Thomas NS, et al.
Human gene mutation database (HGMD): 2003 update. Hum Mutat 2003;21:577-81.
Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S, et al.
The human phenotype ontology: A tool for annotating and analyzing human hereditary disease. Am J Hum Genet 2008;83:610-5.
Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 2005;33:D514-7.
Altman RB. PharmGKB: A logical home for knowledge relating genotype to drug response phenotype. Nat Genet 2007;39:426.
Davis AP, Murphy CG, Saraceni-Richards CA, Rosenstein MC, Wiegers TC, Mattingly CJ, et al.
Comparative Toxicogenomics Database: A knowledgebase and discovery tool for chemical-gene-disease networks. Nucleic Acids Res 2009;37:D786-92.
Gu J, Gui Y, Chen L, Yuan G, Xu X. CVDHD: A cardiovascular disease herbal database for drug discovery and network pharmacology. J Cheminform 2013;5:51.
Ye H, Ye L, Kang H, Zhang D, Tao L, Tang K, et al.
HIT: Linking herbal active ingredients to targets. Nucleic Acids Res 2011;39:D1055-9.
Chen CY. TCM database@Taiwan: The world's largest traditional Chinese medicine database for drug screening in silico
. PLoS One 2011;6:e15939.
Xue R, Fang Z, Zhang M, Yi Z, Wen C, Shi T, et al.
TCMID: Traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res 2013;41:D1089-95.
Fang YC, Huang HC, Chen HH, Juan HF. TCMGeneDIT: A database for associated traditional Chinese medicine, gene and disease information using text mining. BMC Complement Altern Med 2008;8:58.
Zhang JH, Zhu Y, Fan XH, Zhang BL. Efficacy-oriented compatibility for component-based Chinese medicine. Acta Pharmacol Sin 2015;36:654-8.
Ru J, Li P, Wang J, Zhou W, Li B, Huang C, et al.
TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J Cheminform 2014;6:13.
Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, et al.
Atravel guide to Cytoscape plugins. Nat Methods 2012;9:1069-76.
Cheng D, Knox C, Young N, Stothard P, Damaraju S, Wishart DS, et al.
PolySearch: A web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res 2008;36:W399-405.
Hur J, Schuyler AD, States DJ, Feldman EL. SciMiner: Web-based literature mining tool for target identification and functional enrichment analysis. Bioinformatics 2009;25:838-40.
Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, et al.
DAVID bioinformatics resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 2007;35:W169-75.
Güner OF. Pharmacophore Perception, Development, and use in Drug Design. La Jolla, CA: International University Line; 2000. p. 29.
Gramatica P. Principles of QSAR models validation: Internal and external. QSAR Comb Sci 2007;26:694-701.
Cheng F, Zhou Y, Li J, Li W, Liu G, Tang Y, et al.
Prediction of chemical-protein interactions: Multitarget-QSAR versus computational chemogenomic methods. Mol Biosyst 2012;8:2373-84.
Zheng CS, Wu YS, Bao HJ, Xu XJ, Chen XQ, Ye HZ, et al.
Understanding the polypharmacological anticancer effects of Xiao Chai Hu Tang via a computational pharmacological model. Exp Ther Med 2014;7:1777-83.
Jiang F, Kim SH. “Soft docking”: Matching of molecular surface cubes. J Mol Biol 1991;219:79-102.
Willett P. Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 2006;11:1046-53.
He Z, Zhang J, Shi XH, Hu LL, Kong X, Cai YD, et al.
Predicting drug-target interaction networks based on functional groups and biological features. PLoS One 2010;5:e9603.
Willett P. Similarity methods in chemoinformatics. Annu Rev Inform Sci 2009;43:1-117.
Tao W, Xu X, Wang X, Li B, Wang Y, Li Y, et al.
Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. J Ethnopharmacol 2013;145:1-0.
Ehrman TM, Barlow DJ, Hylands PJ. Virtual screening of Chinese herbs with random forest. J Chem Inf Model 2007;47:264-78.
Friedman N, Linial M, Nachman I, Pe'er D. Using Bayesian networks to analyze expression data. J Comput Biol 2000;7:601-20.
Zhang YQ, Li S. Progress in network pharmacology for modern research of traditional Chinese medicine. Chin J Pharm Toxicol 2015;29:883-92.
Yang M, Chen JL, Xu LW, Ji G. Navigating traditional Chinese medicine network pharmacology and computational tools. Evid Based Complement Alternat Med 2013;2013:731969.
Bastian M, Heymann S, Jacomy M. Gephi: An open source software for exploring and manipulating networks. Proceedings Third Int ICWSM Conference 2009;8:361-2.
Schult DA, Swart P. Exploring Network Structure, Dynamics, and Function using NetworkX. Proceedings of the 7th
Python in Science Conferences; 2008.
Borgatti SP, Everett MG, Freeman LC. Ucinet: Encyclopedia of Social Network Analysis and Mining. New York: Springer; 2014. p. 2261-7.
Borgatti SP. NetDraw Software for Network Visualization. Lexington, KY, USA: Analytic Technologies; 2002.
Batagelj V, Mrvar A. Pajek-program for large network analysis. Connections 1998;21:47-57.
Schmidt H, Jirstrand M. Systems biology toolbox for MATLAB: A computational platform for research in systems biology. Bioinformatics 2006;22:514-5.
Mueller LA, Kugler KG, Dander A, Graber A, Dehmer M. QuACN: An R package for analyzing complex biological networks quantitatively. Bioinformatics 2011;27:140-1.
Freeman LC. Centrality in social networks conceptual clarification. Soc Networks 1978;1:215-39.
Borgatti SP. Centrality and network flow. Soc Networks 2005;27:55-71.
Zhang YL, Huang MF, Qiao YJ. Study on preventive and therapeutic mechanism of traditional Chinese medicine based on analysis on key network nodes. Zhongguo Zhong Yao Za Zhi 2013;38:2359-64.
Cheng XR, Cui XL, Zheng Y, Zhang GR, Li P, Huang H, et al.
Nodes and biological processes identified on the basis of network analysis in the brain of the senescence accelerated mice as an Alzheimer's disease animal model. Front Aging Neurosci 2013;5:65.
Wang Y, Liu Z, Li C, Li D, Ouyang Y, Yu J, et al.
Drug target prediction based on the herbs components: The study on the multitargets pharmacological mechanism of qishenkeli acting on the coronary heart disease. Evid Based Complement Alternat Med 2012;2012:698531.
Kong X, Zhou W, Wan JB, Zhang Q, Ni J, Hu Y, et al.
An integrative thrombosis network: Visualization and topological analysis. Evid Based Complement Alternat Med 2015;2015:265303.
Opsahl T, Agneessens F, Skvoretz J. Node centrality in weighted networks: Generalizing degree and shortest paths. Soc Networks 2010;32:245-51.
Newman ME. Analysis of weighted networks. Phys Rev E Stat Nonlin Soft Matter Phys 2004;70:056131.
Li S, Zhang B, Jiang D, Wei Y, Zhang N. Herb network construction and co-module analysis for uncovering the combination rule of traditional Chinese herbal formulae. BMC Bioinformatics 2010;11 Suppl 11:S6.
Li Y, Li R, Ouyang Z, Li S. Herb network analysis for a famous TCM doctor's prescriptions on treatment of rheumatoid arthritis. Evid Based Complement Alternat Med 2015;2015:451319.
Sharan R, Ulitsky I, Shamir R. Network-based prediction of protein function. Mol Syst Biol 2007;3:88.
Newman ME. Detecting community structure in networks. Eur Phys J B 2004;38:321-30.
Newman ME. Fast algorithm for detecting community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys 2004;69:066133.
Guimerà R, Nunes Amaral LA. Functional cartography of complex metabolic networks. Nature 2005;433:895-900.
Chen X, Liu MX, Yan GY. Drug-target interaction prediction by random walk on the heterogeneous network. Mol Biosyst 2012;8:1970-8.
Ning K, Ng HK, Srihari S, Leong HW, Nesvizhskii AI. Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology. BMC Bioinformatics 2010;11:505.
Estrada E. Quantifying network heterogeneity. Phys Rev E Stat Nonlin Soft Matter Phys 2010;82:066102.
Wilkinson DM, Huberman BA. A method for finding communities of related genes. Proc Natl Acad Sci U S A 2004;101 Suppl 1:5241-8.
Spirin V, Mirny LA. Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci U S A 2003;100:12123-8.
Liang Q, Xie Y, Fan X, Yoshida S, Yamamoto Y. Traditional Chinese medicine clinic system biology research system and practice. Modernization Traditional Chin Med Materia Med World Sci Technology 2013;15:1-8.
Zhang BL, Wang XH. Current research on syndromes. Contin Med Educ 2006;20:1-4.
Li R, Ma T, Gu J, Liang X, Li S. Imbalanced network biomarkers for traditional Chinese medicine syndrome in gastritis patients. Sci Rep 2013;3:1543.
Lu C, Xiao C, Chen G, Jiang M, Zha Q, Yan X, et al.
Cold and heat pattern of rheumatoid arthritis in traditional Chinese medicine: Distinct molecular signatures indentified by microarray expression profiles in CD4-positive T cell. Rheumatol Int 2012;32:61-8.
Niu XY, Li J, Lu C, Li LI, Guo HT, Liu CF, et al
. The molecular mechanism of 'herbs-pattern correspondence' in ra with heat pattern based on network pharmacology. Chin J Exp Tradit Med Formul 2012;18:299-303.
Dai W, Liu X, Zhang Z, Chen J, Guo R, Zheng H, et al.
Atwo-level model for the analysis of syndrome of acute ischemic stroke: From diagnostic model to molecular mechanism. Evid Based Complement Alternat Med 2013;2013:293010.
Gao ZY, Zhang JC, Xu H, Shi DZ, Fu CG, Qu D, et al.
Analysis of relationships among syndrome, therapeutic treatment, and Chinese herbal medicine in patients with coronary artery disease based on complex networks. Zhong Xi Yi Jie He Xue Bao 2010;8:238-43.
Li J, Zhao P, Li Y, Tian Y, Wang Y. Systems pharmacology-based dissection of mechanisms of Chinese medicinal formula Bufei Yishen as an effective treatment for chronic obstructive pulmonary disease. Sci Rep 2015;5:15290.
Wang Y, Zheng C, Huang C, Li Y, Chen X, Wu Z, et al.
Systems pharmacology dissecting holistic medicine for treatment of complex diseases: An example using cardiocerebrovascular diseases treated by TCM. Evid Based Complement Alternat Med 2015;2015:980190.
Wang J, Wang YY, Yang G. Methods and modes about the theory of traditional chinese prescription composition. Zhongguo Zhong Yao Za Zhi 2005;30:6-8, 11.
Zhang B, Wang X, Li S. An integrative platform of TCM network pharmacology and its application on a herbal formula, Qing-Luo-Yin. Evid Based Complement Alternat Med 2013;2013:456747.
Ding F, Zhang Q, Ung CO, Wang Y, Han Y, Hu Y, et al.
An analysis of chemical ingredients network of Chinese herbal formulae for the treatment of coronary heart disease. PLoS One 2015;10:e0116441.
Zhang Y, Bai M, Zhang B, Liu C, Guo Q, Sun Y, et al.
Uncovering pharmacological mechanisms of Wu-tou decoction acting on rheumatoid arthritis through systems approaches: Drug-target prediction, network analysis and experimental validation. Sci Rep 2015;5:9463.
Xu H, Zhang Y, Lei Y, Gao X, Zhai H, Lin N, et al.
Asystems biology-based approach to uncovering the molecular mechanisms underlying the effects of dragon's blood tablet in colitis, involving the integration of chemical analysis, ADME prediction, and network pharmacology. PLoS One 2014;9:e101432.
Tan Y, Ko J, Liu X, Lu C, Li J, Xiao C, et al.
Serum metabolomics reveals betaine and phosphatidylcholine as potential biomarkers for the toxic responses of processed aconitum carmichaelii debx. Mol Biosyst 2014;10:2305-16.
[Table 1], [Table 2], [Table 3]