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dc.contributor.advisorAdelson, David L.-
dc.contributor.advisorKortschak, R. Daniel-
dc.contributor.authorShen, Hanyuan-
dc.description.abstractWith thousands of years of clinical practice, Traditional Chinese Medicine (TCM) is an enormous resource for both the pharmaceutical industry and daily health care. However, the wide popularization and application of TCM are hindered by the ambiguous explanation of mechanisms with ancient Chinese concepts. In addition, modern pharmacologic methods based on the interaction between single compound drug and target are inadequate to deal with the complex mixtures for TCM formulas which usually contain plant secondary metabolites from several or even dozens of herbs. New high-throughput technologies and bioinformatics methods can provide systematic and holistic ways to understand biological processes. Applying these methods to TCM research, we can clarify complex biological processes that result from hundreds or thousands of molecular interactions between components in TCM and targets in the organism. Therefore, the purpose of my project is to use models for the application of high-throughput sequencing technologies and bioinformatics methods in order to understand the molecular basis of TCM that involve drug-drug and compound-compound interactions. My model TCM, Compound Kushen Injection (CKI) is an anticancer agent clinically used in China since 1995. It’s commonly used as an adjuvant medicine in the treatment of carcinomas for pain relief, activation of innate immune response and reducing side effects of chemo or radiotherapy. Extracted from two herbs, CKI contains multiple alkaloids and flavonoids, which have been shown to be bioactive in previous studies. However, with the exception of several purified, well characterised compounds, the underlying mechanisms of action for CKI are still unclear. In this thesis, I first applied transcriptome analysis and bioinformatics methods as part of a pipeline to investigate interactions between CKI and chemotherapy drugs. With this pipeline, the mechanisms for the opposing effects of CKI combined with doxorubicin compared to 5-fluorouracil (5-Fu) were determined, and potential interactions between CKI and chemotherapeutic anticancer agents were revealed. These results are closely related to the clinical usage of CKI and may help refine its clinical application. As my second approach, I applied transcriptome analysis to investigate the role of the two plant extracts that make up CKI in order to determine which plant extract contains the primary bioactivity and to identify how the two plant extracts interact to generate the combined effects from CKI. Altogether, this thesis presents approaches for the application of transcriptome analysis in order to identify the molecular mechanisms perturbed by CKI. I have successfully applied systems-biology based approaches to analyse herb-drug interactions and herbal compatibility and demonstrated these methods are valuable additions to TCM research. In addition, my results have indicated that high-throughput sequencing technologies and bioinformatics methods are powerful tools for linking TCM with modern pharmacologic methods.en
dc.subjectTraditional Chinese Medicineen
dc.subjectcancer treatmenten
dc.subjectCompound Kushen Injectionen
dc.titleBioinformatics in Traditional Chinese Medicine (TCM): Potential anti-cancer mechanisms of Compound Kushen Injection (CKI)en
dc.contributor.schoolSchool of Biological Sciencesen
dc.provenanceThis electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at:
dc.description.dissertationThesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 2019en
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