臺灣博碩士論文加值系統

在本研究以肝臟為主軸，利用正常的細胞和癌細胞之間顯著的通量分佈代謝差異，找到在癌化情況下的生物標誌物或致癌基因，基於人類代謝網路模型Recon2.2，並透過人類蛋白質圖譜網站(The Human Protein Atlas，HPA )中具有組織特異性的數據做應用，再搭配Virtual Metabolic Human (VMH)網站中所提供的營養物質做為模型攝取的依據，經由成本最佳化反應依賴性評估(Cost Optimization Reaction Dependency Assessment，CORDA)的算法，重建出肝臟的健康與癌症的模型，透過實驗室所開發的Nested Hybrid Differential Evolution (NHDE)，配合突變通量平衡分析(Mutant Flux Balance Analysis，mFBA)並以瓦氏效應(Warburg Effect)的現象做為研究的指標，模擬肝癌代謝重編程的情況，而找到的致癌基因，大多指向脂肪代謝的相關基因酵素，它們將會誘導體內代謝網路出現紊亂，促進腫瘤的生長與癌症的發展，並預測生物標誌物，做為肝癌在未來更進一步的研究以及給予醫療上一個明確的方向。

This study used the liver as the main axis to exploit the significant flux distribution differences between normal cells and cancer cells to find biomarkers or oncogenes under the case of cancer. Based on the human metabolic network model Recon2.2, and use tissue-specific data from the Human Protein Atlas (HPA), the Virtual Metabolic Human (VMH) to provide the nutrients and Cost Optimization Reaction Dependency Assessment (CORDA) algorithm to generate the model reconstructs of liver health and cancer. Through the use of the Nested Hybrid Differential Evolution (NHDE) and Mutant Flux Balance Analysis (mFBA) developed in the laboratory, the phenomenon of the Warburg Effect as indicators of research to simulate the reprogramming of liver cancer metabolism. Most of the found oncogenes are related to the enzyme enzymes involved in fat metabolism. They will induce human body metabolic network disorders to promote the growth of tumors and the development of cancer. The studies of liver cancer, predict biomarkers, give medical care a clear direction in the further .

致謝
摘要
Abstract
目錄
表目錄
圖目錄
第一章緒論
1.1前言
1.2文獻回顧
1.3研究動機
1.4組織章節
第二章 代謝生物資料庫及工具簡介
2.1生物資料庫
2.1.1 National Center for Biotechnology Information (NCBI)
2.1.2 Expert Protein Analysis System (ExPASy)
2.1.3 Kyoto Encyclopedia of Genes and Genomes (KEGG)
2.1.4 GeneCards : The Human Gene Database
2.1.5 The Human Protein Atlas (HPA)
2.1.6 Virtual Metabolic Human (VMH)
2.2 工具簡介
2.2.1 MATrix LABoratory (MATLAB)
2.2.2 Systems Biology Program (SBP)
2.2.3 General Algebraic Modeling System (GAMS)
第三章 代謝網路模型介紹與方法分析
3.1前言
3.2代謝網路模型的建構
3.2.1人類基因體規模之代謝網路模型(Recon 2.2)
3.2.2肝臟組織特異性模型
3.2.2.1健康之肝臟組織特異性模型(HT-Liver Model)
3.2.2.2癌症之肝臟組織特異性模型(CA-Liver Model)
3.2.3在生物體代謝途徑中重要物質和反應的加入
3.2.4 CORDA (Cost Optimization Reaction Dependency Assessment)
3.3生理條件設定(Target setting)
3.4計算方法的介紹
3.4.1將問題以數學模式做描述
3.4.2通量平衡分析(Flux Balance Analysis，FBA)
3.4.3突變通量平衡分析(Mutant Flux Balance Analysis，mFBA)
3.4.4基質最小成分分析(Minimum Uptake Requirement)
3.4.5 Nested Hybrid Differential Evolution (NHDE)
第四章 結果與討論
4.1前言
4.2 Warburg Effect、致癌性及LC/MS相似度的介紹
4.3與肝癌相關的基因酵素
4.3.1甲羥戊酸途徑(mevalonate pathway)之單基因失調
4.3.2甲羥戊酸途徑(mevalonate pathway)之雙基因失調
4.3.2.1 HMGCR基因搭配HMGCS2之雙基因失調
4.3.2.2 HMGCR基因搭配DDC/HDC之雙基因失調
4.3.3 膽鹼代謝(Choline metabolism)之基因失調
4.3.3.1 CHDH搭配BHMT之雙基因失調
4.3.3.2 BHMT基因搭配ACAC基因之雙基因失調
4.3.3.3 CHDH與BHMT搭配SPHK之三基因失調
4.3.4 ELOVL脂肪酸延長酶與肝癌的關係
4.4與其他癌症相關的基因酵素
4.4.1 AMACR基因酵素與癌症的關係
4.4.2 SOAT1和SOAT2與癌症的關係
4.5未來可能的致癌基因
第五章 結論與未來展望
5.1結論
5.2未來展望
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