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系統識別號 U0026-2207201316261300
論文名稱(中文) 系統生物學方法應用於高通量全基因組資料之解析
論文名稱(英文) Systems Biology Approaches for Analysis of High-Throughput Genome-Wide Data
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 101
學期 2
出版年 102
研究生(中文) 陳偉銘
研究生(英文) Wei-Ming Chen
學號 P78981265
學位類別 博士
語文別 英文
論文頁數 107頁
口試委員 指導教授-蔣榮先
召集委員-陳信希
口試委員-徐建業
口試委員-黃憲達
口試委員-黃浩仁
口試委員-曾大千
口試委員-高宏宇
口試委員-李宗儒
中文關鍵字 系統生物學  激酶組調控網路  磷酸化  分類  演算法  ADTree決策樹  次世代定序  核小體定位 
英文關鍵字 Systems Biology  Kinome Regulatory Network  Phosphorylation  Classification  Algorithm  ADTree  Next-Generation Sequencing  Nucleosome Positioning 
學科別分類
中文摘要 解決生物的複雜性與高資料維度詛咒是深入探討生物作用機制首要挑戰的任務。系統生物學概念提供一個良好的基礎架構來剖析生物複雜性並建立生物與電腦技術之間的合作橋樑。挑戰的任務可細分為兩方面來探討:如何處理超大量資料的問題與如何逐步地解開生物的作用方式。本論文中提供了一個系統生物學架構,該架構是一個循環的機制:能使生物問題驅動電腦技術提升;進而驅動生醫網路建構的完整性;最後再反饋回生物本體以便回答生物問題。首先,該架構包含了一套系統性網路模組來處理超大資料,並且應用於建構激酶組調控網路與解開磷酸化調控之複雜性。第二,該架構包括一套系統性分析方法能有效逐步地檢驗與分析由生物網路所產生的新假設,並且應用於解析核孔複合物的機制,其主要藉由發展數學模型方法來分析巨大資料並反饋回生物本質問題。總結上述,本論文主要致力於發展系統生物學方法,其貢獻包含了:建立新的數學模型方法以解決複雜生物問題;分析巨量數據並且通過統計方法來驗證生物假設;資訊視覺化以助於透視生物複雜性;最後,利用循環的機制來解決生物問題並且突破電腦技術瓶頸。
英文摘要 The two main challenges from the biological complexity and high data dimensionality are to understand how biological systems function and to determine how enormous data can be dealt with, respectively. The objective of this dissertation is to propose a systems biology framework and aims to target the problem with the purpose of exploring the biological complexity and high data dimensionality. This framework that contains two systems biology approaches is a constantly evolving loop with three elements: biology, computation, and network. In our research, we first have proposed a systems network that can analyze big data, build a kinome network from huge datasets, and uncover the complex regulation by phosphorylation. Secondly, we have developed a systems analysis that can decipher nuclear pore complexes (NPCs) mechanism by analyzing tremendous data and providing feedback to biology. In conclusion, our research contributes to the development of systems biology framework in four ways: 1) it creates new models in order to address new biological questions, 2) it summarizes and describes the data through statistical tests to validate the hypothesis, 3) it gives visualization of the analyzed results effectively to break down the biological complexity, and 4) it provides an evolving cycle framework to address biological issues and to advance in computation.
論文目次 中文摘要 I
ABSTRACT II
ACKNOWLEDGEMENTS III
CONTENTS V
TABLES VIII
FIGURES IX
CHAPTER 1 INTRODUCTION 1
1.1 OVERVIEW 1
1.2 OBJECTIVE 2
1.3 ORGANIZATION OF DISSERTAION 4
CHAPTER 2 WHY WE NEED SYSTEMS BIOLOGY? 5
2.1 WHY IS SYSTEMS BIOLOGY IMPORTANT? 5
2.2 NETWORK ANALYSIS IN SYSTEMS BIOLOGY 7
2.3 SYSTEMS BIOLOGY FOR DISEASE AND MEDICINE 8
2.4 SYSTEMS BILOGY FOR BIG DATA 9
CHAPTER 3 A SYSTEMAS NETWORK TO BUILD THE KINOME NETWORK 11
3.1 RELATED WORK 12
3.2 MATERIALS AND METHODS 16
3.2.1 Datasets used to predict Phosphorylation networks 21
3.2.2 Data Processing 21
3.2.3 Model Learning 25
3.3 RESULTS 27
3.2.1 PhosphoChain evaluation 28
3.2.2 Reconstruction of MAPK Pathways 34
3.2.3 Predicting Phosphorylation Sites 40
3.2.4 Reconstruction of the Genome-wide Kinome Network 47
3.4 SUMMARY 54
CHAPTER 4 A SYSTEMS ANALYSIS IN DECIPHERING THE NUCLEAR PORE COMPLEXES MECHANISM 56
4.1 RELATED WORK 57
4.2 MATERIALS AND METHODS 60
4.2.1 Telomeric enrichment model 60
4.2.2 Nucleosome fluctuation model 62
4.2.3 Subtelomeric association model 65
4.2.4 Visualization 67
4.3 RESULTS 68
4.3.3 Detection of the enrichment pattern of subtelomeric genes silencing 68
4.3.4 Identification of subtelomeric chromatin changes in nucleosome positioning and occupancy 71
4.3.5 Measurement of the physical interactions of NPC protein with RP genes and subtelomeric DNA 76
4.4 SUMMARY 83
CHAPTER 5 CONCLUSION AND FUTURE WORK 84
5.1 REMARKS 84
5.2 FUTURE WORK 86
5.2.1 Extending PhosphoChain and improvement of kinome network building 86
5.2.2 Extending NPCparser and providing feedback to biology 88
5.2.3 Multi-scale visualizations in multiple levels for big data 89
5.2.4 New statistical and mathematical methods for big data 90
REFERENCES 93
APPENDIX FIGURES 105
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