系統生物學是研究一個生物系統中「基因、RNA、蛋白質、與分子元件」的構成，以及不同元件間交互影響的科學。後基因體時代，首要任務是如何從大量的基因資訊中整理出其對生物體組織結構發展的影響，因而系統生物學科已引起學者高度重視。此學科中，以逆向工程方式從表現資料建構並描繪基因表現的基因調控網路的研究被視為能了解不同基因元件的互動過程；其中，基因表現資料是基因轉錄成mRNA時所量測的資料，理解這些數據資料將有助於瞭解基因網路複雜的結構與調控流程。本研究以逆向工程演算法為基礎探討推導調控網路的研究中，如何有效結合生物知識與演化式演算法建構基因調控網路之模型，並以結構性方式賦予計算模型最適當的生物意涵，而後能使產生出的結果回饋予生物學家進一步的研究發想。基於研究目的，本論文主要分為三大部分完成：（1）從現有文獻中，改良一套更有效率的演化式演算法推導基因調控網路；（2）探討若真實生物網路的調控關係尚未明確時，是否能以計算模型先行判斷調控網路中重要的調控參數，從而輔助生物學者解讀生物系統。此階段以參數敏感度分析、蒙特卡羅模擬法，結合演化式演算法進行實驗；實驗結果已與真實網路系統「SOS DNA repair system」驗證，證明計算模型與基因調控網路的參數對應關係；以及（3）從已知生物知識建立可數學模型化的調控關係，並進一步探討如何從已知的生物知識（Prior knowledge）獲得基因之間的調控規則，並將調控規則帶入計算模型中，限定演化式演算法的解空間搜尋，以求得同時符合計算與原調控模型之參數值。實驗結果證明本論文提出的研究架構與演算法能有效結合基因表現資料與生物知識並用以賦予計算模型適當的生物意涵，以及將其推導結果提供生物學家後續進行研究的參考資訊。

In the emergence of post-genomic research, one of the most important themes is to uncover the complex biological mechanisms involved in genetic regulation. The regulatory interactions controlled by cis-regulatory DNA modules provide clues about the development of biological processes. These regulatory links can be represented as network-like architectures, i.e. gene regulatory networks (GRNs), which indicate the causal gene expression relationships between instructional inputs and functional outputs of genes. Modeling GRNs, therefore, is essential for conceptualizing how genes express themselves as well as influence others. Thanks to modern measurement techniques for gene expression, researchers can investigate phenotypic behavior of a living being by reconstructing GRNs from expression data. Typically a reverse engineering approach is employed; it is an effective strategy to reproduce possible fitting models of GRNs. Under this strategy, however, two daunting tasks must be undertaken. One is to optimize the accuracy of inferred network behaviors; the other is to designate valid biological topologies for target networks. Though existing studies have addressed the two tasks for years, few are able to satisfy both requirements simultaneously.
To cope with the difficulties, this thesis proposes an integrative modeling framework which consists of three aspects. First, a novel reverse engineering algorithm is developed to tackle the issue of efficiently optimizing network behaviors for GRNs. Second, a proposed sensitivity analysis approach coupling with the optimization algorithm is designed to identify critical regulatory interactions under the situation where biological knowledge is unavailable. Finally, an integrated modeling approach combining knowledge-based and data-driven input sources is constructed to conduct biological topologies with corresponding network behaviors. For each aspect, a series of experiments are performed. The results reveal that the proposed framework can successfully infer solutions that are satisfactory for both requirements of network behaviors and biological structures, and thus the outcomes are exploitable for future in vivo experimental design.

CHAPTER 1 INTRODUCTION 1
CHAPTER 2 BACKGROUND AND RELATED WORK 6
2.1 GENE REGULATORY NETWORKS 6
2.2 MICROARRAY GENE EXPRESSION DATA 7
2.3 COMPUTATIONAL MODELING METHODS FOR GENE REGULATORY NETWORKS 8
CHAPTER 3 INFERRING GENE REGULATORY NETWORKS BY A PROPOSED EVOLUTIONARY ALGORITHM – A HYBRID GA-PSO APPROACH 10
3.1 INTRODUCTION 10
3.2 COMPUTATIONAL MODEL 13
3.2.1 Evaluation function for the computational model15
3.3 AN ADAPTIVE GA-PSO HYBRID APPROACH 17
3.4 EXPERIMENT RESULTS AND DISCUSSION 23
3.4.1 Performance evaluation for inferring networks 24
3.4.2 Robustness test 33
3.4.3 Comparison with recent evolutionary algorithms 36
3.5 CHAPTER DISCUSSION AND SUMMARY 41
CHAPTER 4 A DATA-DRIVEN MODELING APPROACH BY A PROPOSED SENSITIVITY-BASED INCREMENTAL EVOLUTION METHOD 42
4.1 INTRODUCTION 42
4.2 NETWORK ROBUSTNESS FOR GRNS MODELING 43
4.3 SENSITIVITY ANALYSIS 45
4.4 PROPOSED PARAMETER SENSITIVITY ANALYSIS 46
4.4.1 Sensitivity analysis algorithm 48
4.4.1.1 A walkthrough example of m-MPSA 50
4.5 PROPOSED SENSITIVITY-BASED INCREMENTAL EVOLUTION ALGORITHM FOR GRNS INFERENCE 54
4.6 EXPERIMENT RESULTS AND DISCUSSION 58
4.6.1 Performance evaluation of sensitivity analysis in network modeling 59
4.6.2 Evaluation of network robustness 62
4.6.2.1 Evaluation of network robustness – a walkthrough example 63
4.6.3 Evaluation of the proposed approach on the SOS repair system in Ecoli67
4.6.3.1 Analysis of critical parameters of the SOS repair system 72
4.6.3.2 Patterns derived from inferred models 74
4.7 CHAPTER DISCUSSION AND SUMMARY 79
CHAPTER 5 A KNOWLEDGE-DRIVEN MODELING APPROACH FOR BIOLOGICAL NETWORK INFERENCE 80
5.1 INTRODUCTION 80
5.2 PRELIMINARY RESEARCH – ANALYSIS OF CONSTRAINT-BASED INFERENCE84
5.2.1 Evolution of knowledge-based constraints 86
5.3 THE PROPOSED KNOWLEDGE-DRIVEN MODELING FRAMEWORK 92
5.3.1 The source of PKDs selection and construction 92
5.3.2 Data pre-processing and integration 93
5.3.3 Computational model 95
5.3.4 Mapping prior knowledge onto S-system 95
5.3.5 PKDs modeling algorithm using the reverse-engineering approach 97
5.4 EXPERIMENT RESULTS AND DISCUSSION 101
5.4.1 The performance of proposed framework on the artificial dataset 102
5.4.2 Evaluation of the PKDs modeling algorithm on yeast Scerevisiae 107
5.5 CHAPTER DISCUSSION AND SUMMARY 113
CHAPTER 6 CONCLUSION AND FUTURE WORK 114
6.1 CONCLUSION 114
6.2 FUTURE WORK 116
APPENDIX 118
REFERENCE 125

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