【摘要】：基于生物分子網(wǎng)絡(luò)分析的系統(tǒng)生物學(xué)研究策略是當(dāng)前生物學(xué)研究的主流范式。生物技術(shù)的迅猛發(fā)展和由此產(chǎn)生的海量生物學(xué)數(shù)據(jù),以及系統(tǒng)生物學(xué)研究手段的日趨成熟,使得基于生物分子網(wǎng)絡(luò)分析的系統(tǒng)生物學(xué)研究成為可能,并在生物學(xué)研究的各個(gè)領(lǐng)域中得以廣泛應(yīng)用。本論文中,我將詳細(xì)介紹生物分子網(wǎng)絡(luò)分析在癌癥生物學(xué)和演化生物學(xué)中的研究應(yīng)用工作。早期的診斷發(fā)現(xiàn)對(duì)癌癥的預(yù)防和治療起著至關(guān)重要的作用,因而準(zhǔn)確有效的診斷標(biāo)志物的鑒定具有極其重要的意義。這里,通過整合mi RNA和m RNA的基因表達(dá)譜信息,以及mi RNA-m RNA調(diào)控網(wǎng)絡(luò)的拓?fù)鋵W(xué)信息,我們開發(fā)了一個(gè)可用于預(yù)測(cè)癌癥診斷mi RNA生物標(biāo)志物的生物信息學(xué)算法,并采用Java和R兩種計(jì)算機(jī)語言對(duì)該算法進(jìn)行了計(jì)算機(jī)程序?qū)崿F(xiàn)。隨后,我們成功將該算法應(yīng)用于前列腺癌診斷mi RNA生物標(biāo)志物的鑒定中,后續(xù)的低通量實(shí)驗(yàn)以及多種系統(tǒng)生物學(xué)分析證實(shí)了預(yù)測(cè)結(jié)果的可靠性。通過對(duì)算法的完善更新,我們將該預(yù)測(cè)算法延伸應(yīng)用到了包括腎透明細(xì)胞癌在內(nèi)的多種癌癥的研究分析中,并取得了較為理想的研究結(jié)果�；蚧プ骶W(wǎng)絡(luò)的演化過程研究是演化生物學(xué)中的一個(gè)重要問題,同時(shí)也是我們研究生物表型的演化乃至物種的起源等問題的重要手段。本研究中,我們從新基因的角度來探索了哺乳動(dòng)物(人和小鼠)中基因互作網(wǎng)絡(luò)的演化模式。我們發(fā)現(xiàn)新基因加入原有基因互作網(wǎng)絡(luò)是一個(gè)時(shí)間依賴型的演化過程:隨著基因年齡的增長,基因逐漸獲得更多的連通邊,從而自基因互作網(wǎng)絡(luò)的邊緣區(qū)域逐漸進(jìn)入網(wǎng)絡(luò)核心部分。與新基因加入原有基因互作網(wǎng)絡(luò)過程相一致,我們發(fā)現(xiàn)新基因的功能演化同樣是一個(gè)時(shí)間依賴型的過程。隨著基因年齡的增長,基因逐步獲得多效性生物功能以及機(jī)體必需功能。通過結(jié)合基因表達(dá)信息,基因互作信息以及文獻(xiàn)數(shù)據(jù),我們鑒定得到了4個(gè)可能和大腦發(fā)育功能相關(guān)的人類特有的Hub基因。最后,我們?cè)敿?xì)探討了驅(qū)動(dòng)基因互作網(wǎng)絡(luò)演化的多種潛在的機(jī)制。
[Abstract]:The strategy of system biology research based on biomolecular network analysis is the mainstream paradigm of current biological research. The rapid development of biotechnology and the resulting mass of biological data, as well as the increasing maturity of research methods in system biology, make it possible to study systems biology based on biomolecular network analysis. It has been widely used in various fields of biological research. In this paper, I will introduce in detail the application of biomolecular network analysis in cancer biology and evolutionary biology. Early diagnosis plays an important role in the prevention and treatment of cancer, so the accurate and effective identification of diagnostic markers is of great significance. Here, by integrating the gene expression profiles of mi RNA and m-RNA, as well as the topological information of mi RNA-m RNA regulatory networks, we have developed a bioinformatics algorithm that can be used to predict biomarkers of mi RNA in cancer diagnosis. Java and R are used to realize the algorithm. Subsequently, we have successfully applied this algorithm to the identification of mi RNA biomarkers for prostate cancer diagnosis. Subsequent low-throughput experiments and a variety of systems biological analysis have confirmed the reliability of the prediction results. By updating the algorithm, we extend the prediction algorithm to the research and analysis of many kinds of cancers, including renal clear cell carcinoma, and obtain satisfactory results. The study of evolution process of gene interaction network is an important problem in evolutionary biology, and it is also an important means to study the evolution of biological phenotype and even the origin of species. In this study, we explored the evolutionary pattern of gene interaction networks in mammals (human and mouse) from the perspective of new genes. We found that the addition of new genes to the existing gene interaction network is a time-dependent evolution process: as the gene grows older, the gene gradually gains more connected edges. Thus, the edge region of the self-gene interaction network gradually enters the core part of the network. It is found that the functional evolution of the new gene is also a time-dependent process, consistent with the process of adding the new gene to the original gene interaction network. With the increase of gene age, genes gradually obtain multiple biological functions as well as essential functions of the body. By combining gene expression information, gene interaction information and literature data, we identified four human-specific Hub genes that may be related to brain development. Finally, we discuss in detail many potential mechanisms that drive the evolution of gene interaction networks.
【學(xué)位授予單位】：蘇州大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：R730.4

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