酵母菌基因調控網路之研究

21世紀的生物學界不再只研究生物體的部分組成，而是想要直接研究整個生物體系統。這種研究生物學的策略形成了一個新學門"系統生物學"。系統生物學的一個主要目標就是想要有系統地研究基因調控網路：基因之間怎麼動態地互相作用來調控彼此的表現以形成一個有高度協同性的生理系統使生物體能夠順利發育及應付各種內外在刺激所引起的生理變化。基因晶片及免疫沈析晶片是兩種新發展出來的高通量生物技術。這兩種技術非常適合用來研究基因調控網路。基因晶片可以測量出整個基因體裡面每個基因在某個時間點的基因表現。免疫沈析晶片可以測量出每個轉錄因子在整個基因體裡面的所有可結合的基因或是整個基因體裡面每個基因上所有可能結合的轉錄因子。在本論文?堙A我們結合基因晶片及免疫沈析晶片的資料來研究酵母菌的基因調控網路。首先，我們發展出了一個名為”時間關連性偵測(TRIA)”的計算方法。TRIA主要的原理是：由免疫沈析晶片的資料，我們可以得知每個轉錄因子可能結合的基因。然後再根據基因晶片的資料來輔助我們從這些可能結合的基因之中，分辨出真正會被轉錄因子調控的基因。也就是我們想利用基因晶片的資料來剔除那些會被轉錄因子結合但是不會被它調控的基因。找出轉錄因子真正會調控的目標基因對於瞭解基因調控機制非常有幫助。TRIA非常適合用來達到這個目的。另外我們發展出了一個名為”模組偵測(MOFA)”的計算方法。主要的原理是：結合基因晶片及免疫沈析晶片的資料來重建有關酵母菌細胞週期的轉錄調控模組。所謂的轉錄調控模組指的是由共同的轉錄因子們所調控的基因群。生物體利用把它的基因體分成很多轉錄調控模組的方式來使得參與共同生理反應的基因群能夠同時被調控，以產生所需的蛋白質來執行生理功能。因此，找出這些轉錄調控模組對於瞭解生物體如何在各種內外在環境的刺激下，維持正常的生理系統非常有幫助。MOFA非常適合用來達到這個目的。我們相信發展能結合各種不同生物技術資料的計算方法對於研究複雜的生物系統非常有幫助。特別是有越來越多高通量的生物技術被發展出來測量基因體、轉錄體、蛋白體、作用體等，更使得我們這種研究策略非常具有未來性。

關鍵字

酵母菌；基因調控網路

並列摘要

The turn of the 21st century has been marked by a resurgence of interest in achieving a systems-level understanding of biology. One of the long-term goals of a newly emerged research field, called systems biology, aims at a comprehensive understanding of the genetic regulatory networks: how the genes dynamically interact and regulate each other to form highly coherent and coordinated physiological systems during the organism's development and in response to homeostatic challenges. DNA microarray and ChIP-chip are two high-throughput biotechnologies that provide complementary information of genetic regulatory networks. DNA microarray can simultaneously measure the mRNA level of each gene in a genome at a specific time point of a biological process being studied. ChIP-chip can simultaneously determine what are the target genes that a transcription factor (TF) may bind and what are the TFs that may bind to a gene. In this thesis, we integrate DNA microarray and ChIP-chip data to study the genetic regulatory networks of the yeast. First, we develop a computational method, called Temporal Relationship Identification Algorithm (TRIA), which uses DNA microarray data to identify a TF’s regulatory targets from its binding targets inferred from ChIP-chip data. TRIA can be thought of as a “refinement” process of ChIP-chip data to filter out the binding but non-regulatory targets of a TF. Finding the regulatory targets of TFs is important for understanding gene regulation. TRIA is helpful for achieving this purpose. Second, we develop a computational method, called MOdule Finding Algorithm (MOFA), which integrates DNA microarray and the “refined” ChIP-chip data (derived from applying TRIA to the noisy ChIP-chip data) to reconstruct the transcriptional regulatory modules (TRMs) of the yeast cell cycle process. A TRM is a set of genes that is regulated by a common set of TFs. By organizing the genome into TRMs, a living cell can coordinate the activities of many genes that are needed for a cellular process and carry out complex functions. Therefore, identifying TRMs is useful for understanding cellular responses to internal and external signals. MOFA is helpful for achieving this purpose. We believe that computational analysis of multiple types of data will be a powerful approach to studying complex biological systems when more and more genomic resources such as genome-wide protein activity data and protein-protein interaction data become available.

並列關鍵字

yeast ； genetic regulatory network

參考文獻

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酵母菌基因調控網路之研究

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