轉錄調控是生物學家解開細胞對外界刺激反應機制的重要問題，而現今有不同的高通量實驗方法提供對細胞轉錄調控不同面向的了解。高通量的轉錄因子剔除微陣列提供了基因調控的資訊，另一方面，染色質免疫沉澱的高通量實驗提供了對轉錄因子與基因結合的全面性量測，這兩個方法分別提供了對轉錄調控不同層次的實驗資訊。然而，這兩個實驗方式皆有其天生的問題與不足，在此論文中，我們將對這兩種實驗結果提供進一步的分析方法，並進一步重建基因轉錄調控網路。在論文的第一個部分，我們由生物上的觀點提出了一個能說明由轉錄因子剔除微陣列所得到的轉錄因子基因調控因果對之間分子機制的方法。我們將這個方法實作到 Reimand 等人所分析出的轉錄因子基因調控因果對上，發現我們的方法能進一步的找出更具有生物性意義的轉錄因子基因對，同時也提供了其背後可能的分子調控機制。在這些可能的分子機制，大約有七百左右的假說已在不同的文獻中由實驗所驗證。在本論文的第二個部分，我們提出了一新穎的方法來找出由染色質免疫沉澱的高通量實驗所得到轉錄因子基因結合對中真正在細胞中有執行生理功能的轉錄因子基因結合對。相較於之前相關的研究，我們的方法有較佳的生物性結果，且我們的方法也能分析染色質免疫沉澱次世代定序實驗的結果，也驗證了在不同的生物物種間的可行性。在論文的第三部分，我們重建了酵母菌中的基因轉錄調控網路。不同的實驗狀況下的每一個轉錄因子基因調控對，在兩個不同信賴度的基因調控網路中，所有可能的轉錄調控路徑都由我們所發展的圖形資料探勘的方法自動地找出。這些轉錄調控路徑提供了酵母菌細胞中基因調控網路的靜態全貌。總合這三個部分，此論文中所提出的方法以及重建的轉錄調控網路，將讓生物學家能進一步設計其分析下游的生化實驗，來揭開細胞中更全面的調控機制。

Transcriptional regulation is important for cellular responses to external stimuli. The high throughput transcription factor knockout microarrays (TFKMs) provide useful information about gene regulation. Besides, high throughput chromatin immunoprecipitation (ChIP) experiments are now the most comprehensive approaches for identifying the binding sites of transcription factors (TFs) to their target genes. These two methods unravel different aspects of transcriptional regulatory networks and have their own natural defects and insufficiency. In this dissertation, we first propose a method based on the biological knowledge to elucidate the molecular mechanisms for the causative TF-gene pairs identified by high throughput TFKMs. The proposed method was applied to the TFKM analysis results of Reimand {em et al.} We then demonstrate the biological significance of our refined (i.e., biologically interpretable) TF knockout targets by assessing their functional enrichment, expression coherence, and the prevalence of protein-protein interactions. Our refined TF knockout targets outperformed the original TF knockout targets across all measures. About seven hundred hypotheses of molecular mechanisms for the causative TF-gene pairs generated by our methods have been experimentally validated in the literature. In the second part of the dissertation, we provide a novel algorithm to extract functional TF-gene binding pairs from the results of high throughput ChIP experiments. Compared with previous related works, our method outperformed three existing methods. The identified functional targets of TFs also showed statistical significance over the randomly assigned TF-gene pairs. We also demonstrate that our method is dataset independent and can apply to ChIP-seq data and the {em E. coli} genome. In the third part of this dissertation, we reconstruct the transcriptional regulatory networks in yeast. For each TF-gene regulatory pair under different experimental conditions, all possible transcriptional regulatory pathways in two underlying networks (constructed using experimentally verified TF-gene binding pairs and TF-gene regulatory pairs from the literature) for the specified experimental conditions were automatically enumerated by TRP mining procedures developed from the graph theory. The transcriptional regulatory pathways mined out by our graph data mining procedures provide the panoramic static view on the yeast transcriptional regulatory network. The proposed methods and results in this dissertation can facilitate biologists to design and analyse downstream experiments on the cellular regulatory mechanisms.

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