Abstract
Background: Traditional Chinese Medicine (TCM) is widely utilized as complementary health care in China whose acceptance is still hindered by conventional scientific research methodology, although it has been exercised and implemented for nearly 2000 years. Identifying the molecular mechanisms, targets and bioactive components in TCM is a critical step in the modernization of TCM because of the complexity and uniqueness of the TCM system. With recent advances in computational approaches and high throughput technologies, it has become possible to understand the potential TCM mechanisms at the molecular and systematic level, to evaluate the effectiveness and toxicity of TCM treatments. Bioinformatics is gaining considerable attention to unearth the in-depth molecular mechanisms of TCM, which emerges as an interdisciplinary approach owing to the explosive omics data and development of computer science. Systems biology, based on the omics techniques, opens up a new perspective which enables us to investigate the holistic modulation effect on the body.
Objective: This review aims to sum up the recent efforts of bioinformatics and omics techniques in the research of TCM including Systems biology, Metabolomics, Proteomics, Genomics and Transcriptomics.
Conclusion: Overall, bioinformatics tools combined with omics techniques have been extensively used to scientifically support the ancient practice of TCM to be scientific and international through the acquisition, storage and analysis of biomedical data.
Keywords: Traditional Chinese Medicine (TCM), bioinformatics, system biology, omics, metabolomics, biomarkers.
Graphical Abstract
[2]
Qiu J. When the East meets the West: the future of traditional Chinese medicine in the 21st century. Natl Sci Rev 2015; 2: 377-80.
[3]
Uzuner H, Fan TP, Dias A, Guo DA, El-Nezami HS, Xu Q. Establishing an EU-China consortium on traditional Chinese medicine research. Chin Med 2010; 5: 42.
[4]
Uzuner H, Bauer R, Fan TP, et al. Traditional Chinese medicine research in the post-genomic era: good practice, priorities, challenges and opportunities. J Ethnopharmacol 2012; 140(3): 458-68.
[5]
Buriani A, Garcia-Bermejo ML, Bosisio E, et al. Omic techniques in systems biology approaches to traditional Chinese medicine research: present and future. J Ethnopharmacol 2012; 140(3): 535-44.
[6]
Leung EL, Cao ZW, Jiang ZH, Zhou H, Liu L. Network-based drug discovery by integrating systems biology and computational technologies. Brief Bioinform 2013; 14(4): 491-505.
[7]
Mihalov JJ, Marderosian AD, Pierce JC. DNA identification of commercial ginseng samples. J Agric Food Chem 2000; 48(8): 3744-52.
[8]
Zhang Y-B, Wang J, Wang ZT, But PP, Shaw PC. DNA microarray for identification of the herb of dendrobium species from Chinese medicinal formulations. Planta Med 2003; 69(12): 1172-4.
[9]
Lum JHK, Fung KL, Cheung PY, et al. Proteome of Oriental ginseng Panax ginseng C. A. Meyer and the potential to use it as an identification tool. Proteomics 2002; 2(9): 1123-30.
[10]
Weston AD, Hood L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Proteome Res 2004; 3(2): 179-96.
[11]
Lu CL, Qv XY, Jiang JG. Proteomics and syndrome of Chinese medicine. J Cell Mol Med 2010; 14(12): 2721-8.
[12]
Cho WCS. Application of proteomics in Chinese medicine research. Am J Chin Med 2007; 35(6): 911-22.
[13]
Ji Q, Zhu F, Liu X, et al. Recent advance in applications of proteomics technologies on traditional chinese medicine research. Evid Based Complement Alternat Med 2015; 2015: 983139.
[14]
Murch SJ, Rupasinghe HPV, Goodenowe D, Saxena PK. A metabolomic analysis of medicinal diversity in Huang-qin (Scutellaria baicalensis Georgi) genotypes: discovery of novel compounds. Plant Cell Rep 2004; 23(6): 419-25.
[15]
Xiang Z, Wang XQ, Cai XJ, Zeng S. Metabolomics study on quality control and discrimination of three curcuma species based on gas chromatograph-mass spectrometry. Phytochem Anal 2011; 22(5): 411-8.
[16]
Cao H, Zhang A, Zhang H, Sun H, Wang X. The application of metabolomics in traditional Chinese medicine opens up a dialogue between Chinese and Western medicine. Phytother Res 2015; 29(2): 159-66.
[17]
Huang Y, Ren HT, Zou Q, Wang YQ, Zhang JL, Yu XL. Computational identification and characterization of miRNAs and their target genes from five cyprinidae fishes. Saudi J Biol Sci 2017; 24(6): 1126-35.
[18]
Liu Y, Zeng X, He Z, Zou Q. Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources. IEEE/ACM Trans Comput Biol Bioinformatics 2017; 14(4): 905-15.
[19]
Zeng X, Liao Y, Liu Y, Zou Q. Prediction and validation of disease genes using HeteSim Scores. IEEE/ACM Trans Comput Biol Bioinformatics 2017; 14(3): 687-95.
[20]
Zou Q, Li J, Song L, Zeng X, Wang G. Similarity computation strategies in the microRNA-disease network: a survey. Brief Funct Genomics 2016; 15(1): 55-64.
[21]
Zeng X, Zhang X, Zou Q. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Brief Bioinform 2016; 17(2): 193-203.
[23]
Chen HY, Lin YH, Huang JW, Chen YC. Chinese herbal medicine network and core treatments for allergic skin diseases: Implications from a nationwide database. J Ethnopharmacol 2015; 168: 260-7.
[24]
Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin J Nat Med 2013; 11(2): 110-20.
[25]
Liu CX, Liu R, Fan HR, et al. Network pharmacology bridges traditional application and modern development of traditional Chinese medicine. Chin Herb Med 2015; 7: 3-17.
[26]
Liu YF, Ai N, Keys A, et al. Network pharmacology for traditional Chinese Medicine Research: methodologies and applications. Chin Herb Med 2015; 7: 18-26.
[27]
Li P, Chen J, Zhang W, Fu B, Wang W. Transcriptome inference and systems approaches to polypharmacology and drug discovery in herbal medicine. J Ethnopharmacol 2017; 195: 127-36.
[28]
Zhang Q, Yu H, Qi J, et al. Natural formulas and the nature of formulas: Exploring potential therapeutic targets based on traditional Chinese herbal formulas. PLoS One 2017; 12(2): e0171628.
[29]
Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 2012; 28(10): 1353-8.
[30]
Rozowsky J, Abyzov A, Wang J, et al. AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol Syst Biol 2011; 7: 522.
[31]
Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010; 38(16): e164.
[32]
Kim D, Li R, Dudek SM, Ritchie MD. ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network. BioData Min 2013; 6(1): 23.
[33]
Tsuda K, Shin H, Schölkopf B. Fast protein classification with multiple networks. Bioinformatics 2005; 21(Suppl. 2): ii59-65.
[34]
Wan H, Moens MF, Luyten W, et al. Extracting relations from traditional Chinese medicine literature via heterogeneous entity networks. J Am Med Inform Assoc 2016; 23(2): 356-65.
[35]
Zhao P, Yang L, Li J, Li Y, Tian Y, Li S. Combining systems pharmacology, transcriptomics, proteomics, and metabolomics to dissect the therapeutic mechanism of Chinese herbal Bufei Jianpi formula for application to COPD. Int J Chron Obstruct Pulmon Dis 2016; 11: 553-66.
[36]
Sharma V, Sarkar IN. Bioinformatics opportunities for identification and study of medicinal plants. Brief Bioinform 2013; 14(2): 238-50.
[37]
Gu P, Chen H. Modern bioinformatics meets traditional Chinese medicine. Brief Bioinform 2014; 15(6): 984-1003.
[39]
Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008; 4(11): 682-90.
[40]
Li S. Network target: a starting point for traditional Chinese medicine network pharmacology. Zhongguo Zhongyao Zazhi 2011; 36(15): 2017-20.
[41]
Yu G, Zhang Y, Ren W, et al. Network pharmacology-based identification of key pharmacological pathways of Yin-Huang-Qing-Fei capsule acting on chronic bronchitis. Int J Chron Obstruct Pulmon Dis 2016; 12: 85-94.
[42]
Yang KL, Wang Y, Ai L, Yang J, Gao S, Yu CQ. Research ideas on Tongmai Yangxin Prescription in treatment of coronary artery disease based on network pharmacology. Chin Tradit Herbal Drugs 2015; 46: 2979-84.
[43]
Zhang YQ, Mao X, Guo QY, et al. Network pharmacology-based approaches capture essence of Chinese herbal medicines. Chin Herb Med 2016; 8: 107-16.
[44]
Wang L, Li Z, Shao Q, et al. Dissecting active ingredients of Chinese medicine by content-weighted ingredient-target network. Mol Biosyst 2014; 10(7): 1905-11.
[45]
Ma Y, Sun S, Peng CK. Applications of dynamical complexity theory in traditional Chinese medicine. Front Med 2014; 8(3): 279-84.
[46]
Ma Y, Zhou K, Fan J, Sun S. Traditional Chinese medicine: potential approaches from modern dynamical complexity theories. Front Med 2016; 10(1): 28-32.
[47]
Tian T, Chen C, Yang F, et al. Establishment of apoptotic regulatory network for genetic markers of colorectal cancer and optimal selection of traditional Chinese medicine target. Saudi J Biol Sci 2017; 24(3): 634-43.
[48]
Liu X, Liu Y, Cheng M, Xiao H. Metabolomic responses of human hepatocytes to emodin, aristolochic acid, and triptolide: chemicals purified from traditional Chinese medicines. J Biochem Mol Toxicol 2015; 29(11): 533-43.
[49]
Wang M, Chen L, Liu D, Chen H, Tang DD, Zhao YY. Metabolomics highlights pharmacological bioactivity and biochemical mechanism of traditional Chinese medicine. Chem Biol Interact 2017; 273: 133-41.
[50]
Shroff R, Rulísek L, Doubský J, Svatoš A. Acid-base-driven matrix-assisted mass spectrometry for targeted metabolomics. Proc Natl Acad Sci USA 2009; 106(25): 10092-6.
[51]
Godzien J, Ciborowski M, Angulo S, et al. Metabolomic approach with LC-QTOF to study the effect of a nutraceutical treatment on urine of diabetic rats. J Proteome Res 2011; 10(2): 837-44.
[52]
Want EJ, Wilson ID, Gika H, et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat Protoc 2010; 5(6): 1005-18.
[53]
Chu H, Zhang A, Han Y, et al. Metabolomics approach to explore the effects of Kai-Xin-San on Alzheimer’s disease using UPLC/ESI-Q-TOF mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2016; 1015-1016: 50-61.
[54]
Guo LX, Li R, Liu K, et al. Structural characterization and discrimination of Chinese medicinal materials with multiple botanical origins based on metabolite profiling and chemometrics analysis: Clematidis Radix et Rhizoma as a case study. J Chromatogr A 2015; 1425: 129-40.
[55]
Sugimoto M, Hirayama A, Ishikawa T, et al. Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis. Metabolomics 2010; 6: 27-41.
[56]
Duran AL, Yang J, Wang L, Sumner LW. Metabolomics spectral formatting, alignment and conversion tools (MSFACTs). Bioinformatics 2003; 19(17): 2283-93.
[57]
Liu CC, Wu YF, Feng GM, et al. Plasma-metabolite-biomarkers for the therapeutic response in depressed patients by the traditional Chinese medicine formula Xiaoyaosan: A (1)H NMR-based metabolomics approach. J Affect Disord 2015; 185: 156-63.
[58]
Wang J, Guo S, Gao K, et al. Plasma metabolomics combined with personalized diagnosis guided by Chinese medicine reveals subtypes of Chronic heart failure. J Tradit Chin Med Sci 2015; 2: 80-90.
[59]
Chen J, Zhou J, Wei S, Xie Z, Wen C, Xu G. Effect of a traditional Chinese medicine prescription Quzhuotongbi decoction on hyperuricemia model rats studied by using serum metabolomics based on gas chromatography-mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2016; 1026: 272-8.
[60]
Ashour ML, Youssef FS, Gad HA, Wink M. Inhibition of Cytochrome P450 (CYP3A4) Activity by Extracts from 57 Plants Used in Traditional Chinese Medicine (TCM). Pharmacogn Mag 2017; 13(50): 300-8.
[61]
Zhou N, Sun YP, Zheng X-K, et al. A Metabolomics-Based Strategy for the Mechanism Exploration of Traditional Chinese Medicine: Descurainia sophia Seeds Extract and Fractions as a Case Study. Evid Based Complement Alternat Med 2017; 2017: 2845173.
[62]
Sun H, Ni B, Zhang A, Wang M, Dong H, Wang X. Metabolomics study on Fuzi and its processed products using ultra-performance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry coupled with pattern recognition analysis. Analyst 2012; 137(1): 170-85.
[63]
Zhang SN, Li XZ, Lu F, Liu SM. Cerebral potential biomarkers discovery and metabolic pathways analysis of α-synucleinopathies and the dual effects of Acanthopanax senticosus Harms on central nervous system through metabolomics analysis. J Ethnopharmacol 2015; 163: 264-72.
[64]
Ma X, Chi YH, Niu M, et al. Metabolomics Coupled with Multivariate Data and Pathway Analysis on Potential Biomarkers in Cholestasis and Intervention Effect of Paeonia lactiflora Pall. Front Pharmacol 2016; 7: 14.
[65]
Gou XJ, Feng Q, Fan LL, Zhu J, Hu YY. Serum and Liver Tissue Metabonomic Study on Fatty Liver in Rats Induced by High-Fat Diet and Intervention Effects of Traditional Chinese Medicine Qushi Huayu Decoction. Evid Based Complement Alternat Med 2017; 2017: 6242697.
[66]
Zou ZJ, Liu ZH, Gong MJ, Han B, Wang SM, Liang SW. Intervention effects of puerarin on blood stasis in rats revealed by a (1)H NMR-based metabonomic approach. Phytomedicine 2015; 22(3): 333-43.
[67]
Ji P, Wei Y, Sun H, et al. Metabolomics research on the hepatoprotective effect of Angelica sinensis polysaccharides through gas chromatography-mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2014; 973C: 45-54.
[68]
Chao J, Huo TI, Cheng HY, et al. Gallic acid ameliorated impaired glucose and lipid homeostasis in high fat diet-induced NAFLD mice. PLoS One 2014; 9(2): e96969.
[69]
Lao YM, Jiang JG, Yan L. Application of metabonomic analytical techniques in the modernization and toxicology research of traditional Chinese medicine. Br J Pharmacol 2009; 157(7): 1128-41.
[70]
Wei Z, Qian Q, Dong X, et al. Metabolomic approach to understand the acute and chronic hepatotoxicity of Veratrum nigrum extract in mice based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Toxicol Mech Methods 2017; 27(9): 687-96.
[71]
Aa J, Shao F, Wang G, et al. Gas chromatography time-of-flight mass spectrometry based metabolomic approach to evaluating toxicity of triptolide. Metabolomics 2011; 7: 217-25.
[72]
Hao YM, Hong MC, Wang WJ. Study on proteins in urine of chronic renal failure patients of different TCM syndrome types Zhongguo Zhong xi yi jie he za zhi Zhongguo Zhongxiyi jiehe zazhi 2012; 32: 1196-9.
[73]
Li C, Zhao R, Xie M, et al. Proteomics analysis of liver proteins from rats with spleen-deficiency syndrome induced by chronic improper diet consumption and fatigue. J Tradit Chin Med Sci 2017; 4: 113-26.
[74]
Manavalan A, Ramachandran U, Sundaramurthi H, et al. Gastrodia elata Blume (tianma) mobilizes neuro-protective capacities. Int J Biochem Mol Biol 2012; 3: 219-41.
[75]
Chen L, Hou Q, Zhou ZZ, et al. Comparative Proteomic Analysis of the Effect of the Four-Herb Chinese Medicine ANBP on Promoting Mouse Skin Wound Healing. Int J Low Extrem Wounds 2017; 16(3): 154-62.
[76]
Chikan NA, Vipperla B. KAISO inhibition: an atomic insight. J Biomol Struct Dyn 2015; 33(8): 1794-804.
[77]
Wang Y, Lin HQ, Xiao CY, et al. Using molecular docking screening for identifying hyperoside as an inhibitor of fatty acid binding protein 4 from a natural product database. J Funct Foods 2016; 20: 159-70.
[78]
Suo T, Wang H, Li Z. Application of proteomics in research on traditional Chinese medicine. Expert Rev Proteomics 2016; 13(9): 873-81.
[79]
Liu Y, Liu P, Dai R, et al. Analysis of plasma proteome from cases of the different traditional Chinese medicine syndromes in patients with chronic hepatitis B. J Pharm Biomed Anal 2012; 59: 173-8.
[80]
Liu Z, Yu Z, Ouyang X, Du J, Lan X, Zhao M. Applied research on serum protein fingerprints for prediction of Qi deficiency syndrome and phlegm and blood stasis in patients with non-small cell lung cancer. J Tradit Chin Med 2012; 32(3): 350-4.
[81]
Jiang TT, Wang C, Wei LL, et al. Serum protein gamma-glutamyl hydrolase, Ig gamma-3 chain C region, and haptoglobin are associated with the syndromes of pulmonary tuberculosis in traditional Chinese medicine. BMC Complement Altern Med 2015; 15: 243.
[82]
Wang J, Gao L, Lee YM, et al. Target identification of natural and traditional medicines with quantitative chemical proteomics approaches. Pharmacol Ther 2016; 162: 10-22.
[83]
Xu J, Li Y, Zhang S, Jiang H, Wang N, Lin H. Identification of Tengfu Jiangya Tablet Target Biomarkers with Quantitative Proteomic Technique. Evid Based Complement Alternat Med 2017; 2017: 7594805.
[84]
Shi H, Zhang CJ, Chen GYJ, Yao SQ. Cell-based proteome profiling of potential dasatinib targets by use of affinity-based probes. J Am Chem Soc 2012; 134(6): 3001-14.
[85]
Zhang L, Yu Z, Wang Y, et al. Quantitative proteomics reveals molecular mechanism of gamabufotalin and its potential inhibition on Hsp90 in lung cancer. Oncotarget 2016; 7(47): 76551-64.
[86]
Wang Q, Song W, Qiao X, et al. Simultaneous quantification of 50 bioactive compounds of the traditional Chinese medicine formula Gegen-Qinlian decoction using ultra-high performance liquid chromatography coupled with tandem mass spectrometry. J Chromatogr A 2016; 1454: 15-25.
[87]
Cheng M, Chen Z. Trypsin inhibitor screening in traditional Chinese medicine by using an immobilized enzyme microreactor in capillary and molecular docking study. J Sep Sci 2017; 40(15): 3168-74.
[88]
Chen KB, Chen KC, Chang YL, et al. In Silico Investigation of Traditional Chinese Medicine for Potential Lead Compounds as SPG7 Inhibitors against Coronary Artery Disease. Molecules 2016; 21(5): 588.
[89]
Chang YL, Chen HY, Chen KB, et al. Investigation of the inhibitors of histone-lysine N-methyltransferase SETD2 for acute lymphoblastic leukaemia from traditional Chinese medicine. SAR QSAR Environ Res 2016; 27(7): 589-608.
[90]
Adams MD, Kelley JM, Gocayne JD, et al. Complementary DNA sequencing: expressed sequence tags and human genome project. Science 1991; 252(5013): 1651-6.
[91]
Perez-Iratxeta C, Bork P, Andrade MA. Association of genes to genetically inherited diseases using data mining. Nat Genet 2002; 31(3): 316-9.
[92]
Turner FS, Clutterbuck DR, Semple CAM. POCUS: mining genomic sequence annotation to predict disease genes. Genome Biol 2003; 4(11): R75.
[93]
Zou Q, Li J, Wang C, Zeng X. Approaches for recognizing disease genes based on network. BioMed Res Int 2014; 2014: 416323.
[94]
Auffray C, Caulfield T, Griffin JL, Khoury MJ, Lupski JR, Schwab M. From genomic medicine to precision medicine: highlights of 2015. Genome Med 2016; 8(1): 12.
[95]
Ni L, Zhao Z, Xu H, Chen S, Dorje G. The complete chloroplast genome of Gentiana straminea (Gentianaceae), an endemic species to the Sino-Himalayan subregion. Gene 2016; 577(2): 281-8.
[96]
Zhang G, Sun J, Li Y, et al. The complete chloroplast genome of Paeonia anomala subsp. veitchii. Mitochondrial DNA B Resour 2016; 1(1): 191-2.
[97]
Jie H, Lei M, Li P, et al. The complete nucleotide sequence of the mitochondrial genome of Epicauta aptera Kaszab. Mitochondrial DNA B Resour 2016; 1: 489-90.
[98]
Tian X, Ma G, Cui Y, Dong P, Zhu Y, Gao X. The complete mitochondrial genomes of Opisthoplatia orientalis and Blaptica dubia (Blattodea: Blaberidae). Mitochondrial DNA A DNA Mapp Seq Anal 2017; 28(1): 139-40.
[99]
Cheng HT, Chen CR, Li CY, Huang CY, Shu WY, Hsu IC. The classification of Sini decoction pattern in traditional Chinese medicine by gene expression profiling. Evid Based Complement Alternat Med 2016; 2016: 8239817.
[100]
Gao JR, Qin XJ, Jiang H, Wang T, Song JM, Xu SZ. The effects of Qi Teng Xiao Zhuo granules, traditional Chinese medicine, on the expression of genes in chronic glomerulonephritis rats. J Ethnopharmacol 2016; 193: 140-9.
[101]
Su Z, Zhou C, Qin S, Jia E, Wu Z. The Significant Pathways and Genes Underlying the Colon Cancer Treatment by the Traditional Chinese Medicine PHY906. Evid Based Complement Alternat Med 2017; 2017: 8753815.
[102]
Zhang B, Li Y, Zhang Y, et al. ITPI: initial transcription process-based identification method of bioactive components in traditional Chinese medicine formula. Evid Based Complement Alternat Med 2016; 2016: 8250323.
[103]
Wang P, Chen Z. Traditional Chinese medicine ZHENG and Omics convergence: a systems approach to post-genomics medicine in a global world. OMICS 2013; 17(9): 451-9.
[104]
Chen J, Wu XT, Xu YQ, et al. Global transcriptome analysis profiles metabolic pathways in traditional herb Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao. BMC Genomics 2015; 16(Suppl. 7): S15.
[105]
Liu ZQ, Lin S, Baker PJ, et al. Transcriptome sequencing and analysis of the entomopathogenic fungus Hirsutella sinensis isolated from Ophiocordyceps sinensis. BMC Genomics 2015; 16: 106.
[106]
Ahmed S, Zhan C, Yang Y, et al. The transcript profile of a traditional chinese medicine, Atractylodes lancea, revealing its sesquiterpenoid biosynthesis of the major active components. PLoS One 2016; 11(3): e0151975.
[107]
Lassmann T, Sonnhammer ELL. Kalign--an accurate and fast multiple sequence alignment algorithm. BMC Bioinformatics 2005; 6: 298.
[108]
Hao X, Jiang R, Chen T. Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering. Bioinformatics 2011; 27(5): 611-8.
[109]
Thompson JD, Linard B, Lecompte O, Poch O. A comprehensive benchmark study of multiple sequence alignment methods: current challenges and future perspectives. PLoS One 2011; 6(3): e18093.
[110]
Wei L, Huang Y, Qu Y, et al. Computational analysis of miRNA target identification. Curr Bioinform 2012; 7: 512-25.
[111]
Zou Q, Hu Q, Guo M, Wang G. HAlign: Fast multiple similar DNA/RNA sequence alignment based on the centre star strategy. Bioinformatics 2015; 31(15): 2475-81.
[112]
Xue R, Fang Z, Zhang M, Yi Z, Wen C, Shi T. TCMID: Traditional Chinese Medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res 2013; 41: D1089-95.
[113]
Chen CYC. TCM Database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS One 2011; 6(1): e15939.
[114]
Ehrman TM, Barlow DJ, Hylands PJ. Phytochemical databases of Chinese herbal constituents and bioactive plant compounds with known target specificities. J Chem Inf Model 2007; 47(2): 254-63.
[115]
Sanderson K. Databases aim to bridge the East-West divide of drug discovery. Nat Med 2011; 17(12): 1531.
[117]
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.
[118]
Liu H, Wang J, Zhou W, Wang Y, Yang L. Systems approaches and polypharmacology for drug discovery from herbal medicines: an example using licorice. J Ethnopharmacol 2013; 146(3): 773-93.
[119]
Li Y, Han C, Wang J, et al. Investigation into the mechanism of Eucommia ulmoides Oliv. based on a systems pharmacology approach. J Ethnopharmacol 2014; 151(1): 452-60.
[120]
Liu Z, Guo F, Wang Y, et al. BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine. Sci Rep 2016; 6: 21146.
[121]
Li X, Xu X, Wang J, et al. A system-level investigation into the mechanisms of Chinese Traditional Medicine: Compound Danshen Formula for cardiovascular disease treatment. PLoS One 2012; 7(9): e43918.
[122]
Covert MW, Palsson BØ. Transcriptional regulation in constraints-based metabolic models of Escherichia coli. J Biol Chem 2002; 277(31): 28058-64.
[123]
Liu L, Shen F, Xin C, Wang Z. Multi-scale modeling of Arabidopsis thaliana response to different CO2 conditions: From gene expression to metabolic flux. J Integr Plant Biol 2016; 58(1): 2-11.
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