臺灣博碩士論文加值系統

在正常細胞的代謝活化主要依賴於粒線體氧化磷酸化（Oxidative phosphorylation）路徑生成的三磷酸腺苷(Adenosine triphosphate，ATP)來提供能量，但在異常細胞或各種癌細胞中，糖酵解(glycolysis)路徑通量會增強而粒線體中的氧化磷酸化路徑通量會減少。符合這種現象的狀態稱做沃伯格效應(Warburg Effect)。
本研究使用的Recon2 liver model包含2168個代謝物、3041條反應、八個胞器以及1410個基因。針對其他Recon2子模型(肝細胞、肺泡巨噬細胞、皮膚表皮細胞)進行資料比對，分析其共同特點與差異性。並且與其它常見肝細胞模型(例如: Nielsen etc. liver model及Nathan D Price etc. liver model) 做資料交叉比對來說明 Recon2 liver model特色與優點。分析Recon2 liver model包含的代謝途徑，了解那些代謝途徑在模型中扮演重要角色。
本研究利用人類肝細胞代謝網路模型Recon2 liver model，進行最適化通量均衡分析方法 (Flux balance analysis, FBA) 的計算。接著以腫瘤抑制基因Mir122a為基因剃除目標，進行模擬突變通量均衡分析方法(Mutant Flux balance analysis, mFBA)的計算。FBA得到的穩態通量分佈，在與mFBA的穩態通量分布結果做比對，接著利用通量變異性分析(Flux Variability Analysis, FVA)結果作驗證，發現基因剔除前後代謝通量的改變符合沃伯格效應(Warburg Effect)。

Metabolic activities in normal cells rely primarily on mitochondrial oxidative phosphorylation to generate ATP for energy. Unlike in normal cells, glycolysis is enhanced and oxidative phosphorylation capacity is reduced in various cancer cells. This phenomenon is corresponding to the Warburg Effect.
Recon2 liver model used in this study include 2168 metabolites, 3041 reaction, eight compartments and 1410 genes. For data comparison against other Recon2 sub-model (liver cells, lung macrophages, epidermal cells), analysis of their common features and differences. And other common hepatic cell model (for example: Nielsen etc. liver model and Nathan D Price etc. liver model) do cross comparison data to illustrate Recon2 liver model features and benefits. Analysis of metabolic pathways Recon2 liver model includes about metabolic pathways that play an important role in the model.
In this study, the metabolic network model of human liver cells Recon2 liver model, the analysis method (Flux balance analysis, FBA) is calculated optimal flux balance. Then the tumor suppressor gene Mir122a shave target gene, the method for analysis (Mutant Flux balance analysis, mFBA) mutant analog flux balance calculation. Steady-state flux distribution obtained FBA, in steady-state flux distribution with mFBA results do compare, then use flux variability analysis (Flux Variability Analysis, FVA) results for the verification and found that knockout metabolic flux changes after meet the Warburg effect (Warburg Effect).

誌謝 I
摘要 II
ABSTRACT IV
目錄 VI
表目錄 IX
圖目錄 XII
第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 5
1.3 研究動機 6
1.4 組織章節 9
第二章 代謝網路生物資料庫及工具程式簡介 10
2.1 代謝網路生物資料庫 10
2.1.1 KYOTO ENCYCLOPEDIA OF GENES AND GENOMES (KEGG) 10
2.1.1 IPA(INGENUITY PATHWAY ANALYSIS) 14
2.2工具程式簡介 15
2.2.1 Model Transformation Program (MTP) 15
2.2.2 General Algebraic Modeling System (GAMS) 19
第三章 人類基因體代謝模式結構分析 21
3.1 引言 21
3.2 肝細胞代謝網路重建 21
3.3 Recon2與Recon2子Model資訊比對 23
3.4 Recon2 liver model與其他liver model 資訊比對 35
3.5 Recon2 liver model與HepatoNet1-mouse model資訊比較 41
3.6 Recon2 liver model Metabolic Pathway分析 45
3.7 Recon2 liver model代謝物參與反應分析 46
第四章 探討肝細胞代謝通量分析 50
4.1前言 50
4.2數學方法描述 51
4.2.1代謝網路最佳化目標 51
4.2.2數學方法 52
4.3結果分析 59
4.3.1最大最小邊界值(Max-Min bound)分析結果 59
4.3.2通量均衡分析(Flux Balance Analysis, FBA)結果 68
4.3.3突變通量均衡分析(Mutant-Flux Balance Analysis, mFBA)結果 76
4.3.4通量變異性分析(Flux Variability Analysis, FVA)結果 81
第五章 結論與未來展望 108
5.1結論 108
5.2 未來展望與建議 109
參考文獻 110
附錄A 113
附錄B 119

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