基因調控網路為一網路表示基因與調控因子之間交互作用的分子機制。隨著高產量實驗方法的成熟與推陳出新，配合系統生物學整合不同層次信息來了解生物間不同層次的相互作用與關係。蛋白質交互作用在許多生物現象中扮演重要的角色，然而高產量的生物實驗累積了許多基因表現與蛋白質交互作用的資料，可以透過建構出完整的基因調控網路以及蛋白質調控網路，藉此更深入了解細胞的生理與分子機制。
微型核醣核酸 (MicroRNA, 簡稱miRNA) 在基因調控中扮演一個重要角色，在動植物與病毒中被發現，長度大約21到24個鹼基的小片段非編碼核醣核酸，藉由與3端非編碼區 (3’UTR) 的序列結合，破壞或抑制信使核醣核酸 (message RNA, 簡稱mRNA) 轉譯成蛋白質。過去的研究中指出，miRNA 藉由在後轉錄階段調控基因的表現量，在分化、生長以及發育的過程中扮演重要的角色，且與老化以及腫瘤形成有某種關聯；此外， miRNA 與基因轉錄因子 (transcription factor, 簡稱TF) 之間存在一些特殊的相關性。縱然在miRNA 已被大量研究，其分子機制也逐漸明朗，但仍著重於單一調控機制。參與細胞的生理與分子機制的蛋白質種類及數目眾多， miRNA 在複雜生物反應過程中與 TF 合作調控的機制與扮演的角色，以及在蛋白質調控網路間的關係仍有待釐清。
首先，我們整合大規模的實驗資料，建立出果蠅基因調控網路，以描述細胞內各個基因的調控關係。接著進一步將miRNA 與目標基因的調控關係加入，產生新的調控網路。此外，藉由拓樸分析方法，說明miRNA 以及TF 共同調控組合與蛋白質交互作用網路之間的相關性。最後，藉由比較miRNA、TF 以及共同調控組合的特徵以及拓樸性質上的變化趨勢，說明共同調控的機制與扮演的角色。
基於我們所預測的共同調控關係以及兩個網路的分析，結果顯示受到越多TF 或者miRNA 調控的基因越可能本身就是TF，而且在3’UTR 的長度也有如此的趨勢。過去的研究指出，受到miRNA 調控的基因的3’UTR 長度較沒有受miRNA 調控的長，也許是受到miRNA 是藉由與3’UTR 的序列結合來調控目標基因的影響。此外，在結果發現共同調控的目標基因3’UTR 長度也會比單一調控的目標基因來的長。在網路上扮演調控者角色的TF 以及miRNA，我們發現在基因上結合位置的個數、共同調控的因子個數與目標基因是TF 的個數三者之間呈現正相關。這與共同調控的組合有著一定的關係，並且可能在TF 與miRNA 之間的關係扮演某種角色。最後，在拓樸性質分析中，受到越多TF 調控的目標基因的連結度 (degree) 越高，但在 miRNA 的目標基因上卻沒有發現。也許，受到較多TF 調控的目標基因在蛋白質交互作用網路上扮演了中心或者瓶頸的角色。

The expression of gene information is mainly controlled by a number of specialized DNA-binding proteins known as transcription factors (TFs). In recent years, a new class of small molecules called microRNA (miRNA) has emerged as another important regulator on gene expression at post-transcriptional level. It is suggested that miRNAs may regulate a majority of protein-coding genes, responsible for a variety of biological processes. However, the cooperative regulation activity of TFs and miRNAs is still unclear.
In this study, we first built up an integrative transcriptional regulatory network and protein interaction network involving TFs and microRNA genes of Drosophila melanogaster. In these integrative networks, we found many gene groups were extensively co-regulated by transcription factors and miRNAs in various combination; more importantly, these gene groups seemed to form a variety of functional modules, suggesting that each biological process may be specifically co-regulated by different collocation of TFs and miRNAs.
In addition, topological analysis of these subnetworks revealed significant roles of miRNA cross regulation. We found that the co-regulated gene dyads preferentially interacted at the protein level, and they had higher probability to participate in common biological processes. Furthermore, TFs tended to have higher miRNA binding complexity, suggesting that cross-regulation might be a widespread mechanism among gene regulators.
In conclusion, this work uncovers the co-regulation principles between miRNAs and TFs in cellular networks, and proposes that coordination may enhance the control of regulation patterns to influence biological functions in a great diversity.

中文摘要 i
Abstract iii
Contents iv
List of Figures vi
List of Tables viii
Chapter 1: Introduction 1
1.1 Transcriptional Regulatory Networks 1
1.2 MicroRNA Regulation 1
1.3 Combinatorial Regulation 3
Chapter 2: Materials and Methods 4
2.1 Transcription factors and target genes 4
2.2 MicroRNAs and target gene 4
2.3 Co-regulation target gene 4
2.4 Features of target genes 5
2.4.1 3’UTR 5
2.4.2 Transcription factor binding site 5
2.4.3 Protein-protein interaction network 6
Topological Properties 6
Statistical Analysis 8
Chapter3: Results 9
3.1 Target gene distribution 9
3.2 Factors regulate TFs 12
3.3 TFs and miRNAs 16
3.3.1 Regulator binding site on target gene 16
3.3.2 miRNA family binding on target genes 19
3.3.3 Co-regulation factor 21
3.3.4 Correlation with each feature pair 23
3.4 TFs and miRNAs target genes 26
3.4.1 Regulator 26
3.4.2 Regulator binding site on target gene 28
3.4.3 3’UTR length 31
3.4.4 PPI property 33
3.5 Co-regulation target gene 35
3.5.1 Significant Co-regulation Pair 35
3.5.2 Regulator binding site on target gene 37
3.5.3 3’UTR length 44
3.5.4 PPI property 47
Chapter 4: Discussion and Conclusions 51
References 53
Appendixes 55
A. List of Transcription Factors 55
B. Correlation of Each Feature 59
B.1 Transcription Factors 59
B.2 Transcription Factors Target genes 66
B.3 MicroRNAs 70
B.4 MicroRNAs Target genes 76

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