臺灣博碩士論文加值系統

生物體內有著各式各樣的新陳代謝，而肝臟則是生物體的代謝中心，除去體內毒素、合成蛋白質、分泌消化液…等，是生物體中最重要的器官之一。而俗話說「肝是沉默的器官」其來有自，肝臟是唯一沒有痛覺神經的器官，故當肝臟發生病變，沒有辦法經由痛覺提醒我們注意，因此，肝癌的篩檢就成為了治療的關鍵。生物體內的代謝物透過網路結構運行，發生病變時，代謝網路就會改變，本研究利用肝細胞代謝網路模型Recon2 Liver Model，進行巢狀式混合進化計算法(Nest Hybrid Differential Evolution , NHDE) 與通量變化量分析 (Flux Variation Analysis , FVA)，再經集群分析(Cluster Analysis)後，挑選出可能成為生物標記的代謝物，搜尋結果中，部分已經由文獻證實和肝癌有所關連，而尚未驗證的，則可以提供實驗學者一個找尋肝癌生物標記的方向與參考。

Metabolism processes that occur within a living organism in order to maintain life. Liver, center of metabolism of organism, has a wide range of functions, including detoxification of various metabolites , protein synthesis , and the production of bio-chemicals necessary for digestion …, is one of the most important organ of the organism. People says “Liver is a silent organ”, the liver is the only organ that feel no pain, even when it is in disease. So, the key point of treatment is medical inspection. Metabolism processes in organism in network, and the network statue will be influenced by the disease or any abnormal outside impact. In this study, we use Recon2 liver model to describe model of liver network, and do the optimal search for liver cancer biomarker within Nested Hybrid Differential Evolution (NHDE), Flux Variation Analysis (FVA) and Cluster Analysis. Some metabolites of the result had been proven by literature that there are some relationship between these metabolites and liver cancer. And the others, may be suggestions of new liver cancer biomarkers searching.

目錄
致謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 前言 1
1.2 研究動機 3
1.3 瓦氏效應 (Warburg Effect，又稱沃柏格效應) 4
第二章 生物資料庫及工具程式 5
2.1 生物資料庫 5
2.1.1 BRENDA (BRaunschweig ENzyme DAtabase) 5
2.1.2 KEGG (Kyoto Encyclopedia of Genes and Genomes) 7
2.1.3 HMDB (Human Metabolome DataBase) 9
2.2 工具程式 11
2.2.1 MTP (Model Transformation Program) 11
2.2.2 GAMS (General Algebraic Modeling System) 12
2.2.3 Gene-E 13
第三章 代謝網路模型與分析方法 14
3.1 前言 14
3.2 肝細胞代謝網路模型 14
3.3 最佳化目標函數設定 16
3.4 計算方式 16
3.4.1 問題描述 16
3.4.2 生理功能目標設定 17
3.4.3 通量最小最大值分析 (Min-Max Analysis) 20
3.4.4 通量平衡分析 (Flux Balance Analysis , FBA) 21
3.4.5 雙層最佳化問題(Bi-Level Optimization Problem,BLOP) 22
3.4.6 通量變化量分析(Flux Variation Analysis) 23
3.4.7 集群分析 (Cluster Analysis) 24
3.4.8 計算步驟 25
第四章 結果與討論 26
4.1 前言 26
4.2 癌化重點酵素 27
4.3 對瓦氏效應假說及實驗數據進行集群分析 28
4.3.1 瓦氏效應集群分析 28
4.3.2 實驗數據集群分析 29
4.4 生物標記之挑選 30
4.4.1 前言 30
4.4.2 第一組- enoylcoa類 30
4.4.3 第二組- enoylcoa類 31
4.4.4 第三組- 無明顯分群 32
4.4.5 第四組- 無明顯分群 32
4.4.6 第五組- Phosphatidylinositol phosphate 33
4.4.7 第六組- Prostaglandin 33
4.4.8 第七組- Prostaglandin 34
4.4.9 第八組- Cholesterol metabolism 34
4.5 FVA計算篩選 35
4.5.1 第四組- 抗氧化與免疫相關 35
4.5.2 第六組、第七組-前列腺素相關 36
4.5.3 通量完全區隔比例 36
4.6 老鼠組的計算結果及與人類組的比較 36
4.7生物標記的預測 37
第五章 結論與未來展望 38
5.1 結論 38
5.2 未來展望 39
第六章 參考文獻 40
附錄 42
 

1.Kitano H. Foundations of systems biology. MIT press Cambridge 2001.
2.K. J. Kauffman PP, and J. S. Edwards. Advances in flux balance analysis. Current opinion in biotechnology 2003:vol. 14, pp. 491-6.
3.Stefan Schuster TD, David A. Fell, . Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends in biotechnology 1999.
4.Papin JA, Stelling J, Price ND, Klamt S, Schuster S, Palsson BO. Comparison of network-based pathway analysis methods. Trends in biotechnology 2004;22:400-5.
5.Margaret Sullivan Pepe RE, Ziding Feng, John D. Potter, Mary Lou ,Thompson, Mark Thornquist, Marcy Winget, Yutaka Yasui. Phases of Biomarker Development for Early Detection of Cancer. Journal of the National Cancer Institute 2001:Vol. 93, No. 14, July 8,.
6.Administration FaD. In vitro Diagnostics- Role in Efficacy Biomarker Assessment 2012.
7.Japan LCSGo. The general rules for the clinical and pathological study of primary liver cancer. The Japanese journal of surgery 1989:Volume 19, Issue 1, pp 98-129.
8.W. H. Koppenol PLB, and C. V. Dang. Otto Warburg's contributions to current concepts of cancer metabolism. Nature Reviews Cancer 2011:vol. 11, pp. 325-37.
9.Ida Schomburg AC, Christian Ebeling, Marion Gremse, Christian Heldt, Gregor Huhn and Dietmar Schomburg. BRENDA, the enzyme database: updates and major new developments. Nucleic acids research 2003:Pp. D431-D3.
10.Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic acids research 2011:gkr988.
11.Wishart DS, Tzur D, Knox C, et al. HMDB: the Human Metabolome Database. Nucleic Acids Res 2007;35:D521-6.
12.Thiele I, Swainston N, Fleming RM, et al. A community-driven global reconstruction of human metabolism. Nature biotechnology 2013;31:419-25.
13.Fukumoto K, Kikuchi S, Itoh N, et al. Effects of genetic backgrounds on hyperbilirubinemia in radixin-deficient mice due to different expression levels of Mrp3. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 2007;1772:298-306.
14.Trotman BW, Bernstein SE, Balistreri WF, Wirt GD, Martin RA. Hemolysis-induced gallstones in mice: increased unconjugated bilirubin in hepatic bile predisposes to gallstone formation. Gastroenterology 1981;81:6.
15.Vera T, Stec DE. Moderate hyperbilirubinemia improves renal hemodynamics in ANG II-dependent hypertension. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 2010;299:R1044-R9.
16.Garcia Sanchez CE, Torres Saez RG. Comparison and analysis of objective functions in flux balance analysis. Biotechnology progress 2014;30:985-91.
17.Naomi E. Allena PBG, Annette Ginna, Ruth C. Travisa, Andrew W. Roddama, Paul N. Applebya and Timothy Key. Phytanic acid: measurement of plasma concentrations by gas–liquid chromatography–mass spectrometry analysis and associations with diet and other plasma fatty acids. British Journal of Nutrition 2008:pp 653-9.
18.Marchitti SA DR, Vasiliou V. Neurotoxicity and metabolism of the catecholamine-derived 3,4-dihydroxyphenylacetaldehyde and 3,4-dihydroxyphenylglycolaldehyde: the role of aldehyde dehydrogenase. Pharmacol Rev 2007.