臺灣博碩士論文加值系統

本研究依據The Cancer Genome Atlas (TCGA)資料庫與Human Protein Atlas (HPA)網站所提供各種組織器官之RNA Seq與蛋白質表現數據，並搭配Recon2.2人體基因組規模代謝網路模型以及兩種模型重建演算法：最佳化反應依賴性評估(Cost Optimization Reaction Dependency Assessment, CORDA)、整合代謝分析方法(Integrative Metabolic Analysis Tool, iMAT)，建立出4種不同的結腸組織細胞特異性的代謝網路模型。
考慮到搭配實驗所需，本研究根據DMEM與RPMI-1640 兩種培養基的成分作為攝取物質的條件，同時使用突變通量平衡分析方法(Mutant Flux Balance Analysis, MFBA)模擬癌細胞與正常細胞的代謝重編程的差異。
本研究也分別在4種不同的結腸組織代謝模型成功計算出潛在的致癌基因，有助於日後研究學者能針對這些導致結腸癌細胞產生代謝重編程的致癌基因能夠開發出靶向的治療方式。

According to the RNA Seq and protein expression, data of various tissues provided by the Cancer Genome Atlas (TCGA) database and the Human Protein Atlas (HPA) website, and based on the human genome-scale metabolic network model (Recon 2.2 Model). This study used two different algorithms: Cost Optimization Reaction Dependency Assessment (CORDA) and Integrative Metabolic Analysis Tool (iMAT) to reconstruct four different metabolic network models of colon cell.
Considering the requirements of the experiment, this study used the components of DMEM and RPMI-1640 as the conditions for the uptake of substance, and used Mutant Flux Balance Analysis (MFBA) to simulate the metabolism reprogramming occurred in colon cancer cells
This study successfully calculated potential oncogenes in four different colon metabolic models, which can enable future researchers to develop targeted therapies for these oncogenic genes that cause metabolic reprogramming of colon cancer cells.

致謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 IX
第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 2
1.3 研究動機 4
第二章 代謝生物資料庫與工具程式 6
2.1生物資料庫 6
2.1.1 The Cancer Genome Atlas (TCGA) 7
2.1.2 The Human Protein Atlas (HPA) 9
2.1.3 Virtual Metabolic Human (VMH) 12
2.1.4 The Human Metabolome Database (HMDB) 14
2.1.5 Uniprot Database 15
2.2工具程式說明 16
2.2.1 General Algebraic Modeling System (GAMS) 16
2.2.2 Genesis (Cluster analysis) 21
第三章 代謝網路模型與計算方法之介紹與分析 22
3.1 前言 22
3.2 結腸細胞之代謝網路模型重建 23
3.3 計算方法與基本設定介紹 27
3.3.1 巢狀式混合差值進化法 (Nested Hybrid Differential Evolution, NHDE) 27
3.3.2 基質最少成分分析(Minimum Nutrients Requirement Analysis) 29
3.3.3突變通量均衡分析(mutant-Flux Balance Analysis, mFBA) 30
3.3.4通量變異性分析(Flux Variability Analysis, FVA) 31
第四章 結果討論 32
4.1前言 32
4.2 TCGA基因結果分析 33
4.2.1 TCGA基因表現量計算說明 34
4.2.2 TCGA基因上/下調結果 38
4.3 結腸組織代謝網路模型結構分析 43
4.4 通量變異性分析(Flux Variability Analysis)結果分析 52
4.4.1代謝反應通量變異性分析 54
4.4.2 Choke point通量變異性分析 57
4.5 結腸組織代謝網路模型之潛在致癌基因搜尋 59
4.5.1 致癌性類似度之計算 59
4.5.2 潛在致癌基因搜尋 60
4.5.3 致癌基因-物質FVA分析 64
4.5.4 致癌基因-通量平衡分析 71
4.5.5 肌醇磷酸代謝基因-PIK3C3, PI4K2A, PIP5K1A, MTMR3 73
4.5.6 氨基糖代謝基因-NANS, NANP 78
4.5.7 維他命B2基因-RFK, ACP5 82
4.5.8 其他代謝致癌基因-ALDH18A1, HIBADH, SLC25A11 86
第五章 結論與未來展望 93
5.1結論 93
5.2 未來展望 95
參考文獻 99

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