表觀遺傳調控網路之數據分析與模型建構; Analysis and Modeling of Epigenetic Networks

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Title:	表觀遺傳調控網路之數據分析與模型建構;Analysis and Modeling of Epigenetic Networks
Authors:	王孫崇
Contributors:	系統生物與生物資訊研究所
Keywords:	表觀基因體學;DNA 甲基化;微陣列生物晶片;網絡分析;epigenomics;DNAmethylation;microarray;network;graph;genome growthmodel;生物科學類;資訊科學--軟體
Date:	2010-07-01
Issue Date:	2010-06-21 11:54:22 (UTC+8)
Publisher:	行政院國家科學委員會
Abstract:	如果我們將基因體序列看成是一本生命的語言,有些基因產物的作用像是名詞,有些像是動詞｡名詞的例子有那些構造性、功能性蛋白質,如血紅素或脂蛋白｡動詞的例子是轉錄因子,它們的作用是激發或是抑制基因的轉錄｡許多心力已經投入於這些名詞與動詞的研究,我們稱這類研究為基因體學｡然而有些基因產物的作用像是形容詞｡最近這些具有修飾效果的形容詞基因也被逐漸重視｡研究這些修飾作用的領域稱之為表觀基因體學｡在三種已知的主要修飾當中之一是DNA 甲基化｡本計劃是要用系統生物學的方法來研究不同基因體區域其DNA 甲基化程度之間的關係｡這些複雜的關係將以網絡圖的方式來表示與分析｡首先我們用DNA 甲基化微陣列生物晶片,量到病人與正常人組織樣品裡之DNA 甲基化程度(這部分數據已由我們在多倫多大學的合作者提供)｡這些數據讓我們建構出DNA 甲基化的關係網絡｡由於微陣列生物晶片量到全基因體之DNA 甲基化程度, 我們預期網絡之建構工作須要寫一些平行化計算的程式｡由於在我們初步的次基因體小網絡分析中發現病人的網絡拓樸特性不同於正常人的網絡,適當的網絡比較方法能夠幫助我們做疾病的診斷｡我們於是接著會發展一套方法,以有效的比較網絡拓樸的差異｡我們也會發展網絡變形的方法,用最少的步驟將病人的網絡變化成正常人的網絡｡這種變形方法的意義在於用作疾病的治療｡最後我們將發展一套基因體演化模型,來解釋所發現的網絡拓樸｡這模型的用處在於了解疾病的原因｡簡言之,我們的計劃對研究者來講是一尖端的研究,對學生而言是一絕佳的系統生物學訓練｡ others for verbs. Examples of the nouns include such structural/functional proteins as globins and lipoproteins. Examples of verbs are those transcription factors which activate or repress gene activity. A lot of efforts have been devoted to the identification of nouns and verbs and the field of study is called genomics. Meanwhile, the modifying effect of genes that code for adjectives starts to be recognized and study of the modifications is called epigenomics. One of the major DNAmodifications is DNAmethylation.We propose to study epigenomics in the paradigm of systems biology by casting the relations between effects of DNAmethylation in a network or graph. We will first obtain networks by DNA methyalation microarrays (a high throughput technique for whole genome methylation profiling) on sperm and brain samples from patients and controls. (Data has been provided by our collaborators at University of Toronto.)With the genome scale measurement by microarrays, a network will consist of 12,192 nodes and we expect a bit programming effort to parallelize the analysis algorithms.We will then go on to develop metrics for network comparisons. Our sub-genomic scale preliminary study showed difference in the topologies of networks between diseased and healthy samples. A proper metric is therefore crucial for the identification of meaningful signature for the purpose of clinical diagnosis.We will also develop algorithms to morph networks from a diseased topology to healthy topology with minimum operations. Implications for such morphing lie in its potential for disease treatment. Finally, we plan to develop a genome growth model to explain the topologies of both diseased and healthy networks with an eye on a better understanding of the disease mechanisms. In brief, the proposed project represents a forefront research for the investigator and a systems biology training for students. 研究期間：9808 ~ 9907
Relation:	財團法人國家實驗研究院科技政策研究與資訊中心
Appears in Collections:	[Institute of Systems Biology and Bioinformatics] Research Project

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