本文選題：復(fù)雜疾病 + 轉(zhuǎn)錄因子�。� 參考：《西安理工大學(xué)》2017年碩士論文

【摘要】：復(fù)雜疾病的發(fā)生受到多個基因的調(diào)控,一直是生物醫(yī)學(xué)研究的重點和難點�，F(xiàn)代生物學(xué)和實驗技術(shù)的不斷發(fā)展,為深入研究基因調(diào)控機制創(chuàng)造了條件。研究復(fù)雜疾病的基因調(diào)控網(wǎng)絡(luò),對于揭示復(fù)雜疾病內(nèi)部復(fù)雜的生命現(xiàn)象和調(diào)控規(guī)律,診斷和治療復(fù)雜疾病具有較大的推動作用。本文遵循系統(tǒng)生物學(xué)和分子生物學(xué)的思想,采用生物信息學(xué)的方法,研究轉(zhuǎn)錄因子(Transcription Factor,TF)和microRNA (miRNA)參與調(diào)控的基因網(wǎng)絡(luò)的構(gòu)建以及基因網(wǎng)絡(luò)的動力學(xué)機制。首先,介紹了構(gòu)建復(fù)雜疾病相關(guān)的轉(zhuǎn)錄因子和microRNA共調(diào)控基因網(wǎng)絡(luò)的生物信息學(xué)方法、原理以及相關(guān)數(shù)據(jù)庫。然后,討論了兩種轉(zhuǎn)錄過程的動力學(xué)模型,提出一種轉(zhuǎn)錄因子和microRNA共調(diào)控的前饋環(huán)動力學(xué)模型。最后,將各種方法、數(shù)據(jù)庫和前饋環(huán)動力學(xué)模型應(yīng)用于胰腺癌數(shù)據(jù)。本課題利用倍數(shù)分析和精確檢驗對患病和正常兩種樣本數(shù)據(jù)進行差異表達分析。通過加權(quán)基因關(guān)聯(lián)網(wǎng)絡(luò)分析獲得差異基因和差異microRNA的共表達,來預(yù)測microRNA對基因的調(diào)控關(guān)系。利用差異基因和位置權(quán)重模型匹配,預(yù)測調(diào)控基因的轉(zhuǎn)錄因子。對TransmiR和ENCODE數(shù)據(jù)庫中調(diào)控差異microRNA的轉(zhuǎn)錄因子關(guān)系取并集。整合得到的調(diào)控關(guān)系,構(gòu)建轉(zhuǎn)錄因子和microRNA共調(diào)控的基因網(wǎng)絡(luò),獲得134個前饋環(huán)模體。使用微分方程組對前饋環(huán)的轉(zhuǎn)錄機制進行建模和定量分析。采用高斯過程描述隱轉(zhuǎn)錄因子的表達活性,基于貝葉斯框架對前饋環(huán)動力學(xué)模型進行推導(dǎo),分別采用單目標(biāo)文化遺傳算法和同時考慮基因表達值及其梯度的多目標(biāo)文化遺傳算法,對動力學(xué)模型的參數(shù)和核函數(shù)的超參數(shù)進行迭代優(yōu)化求解。仿真實驗結(jié)果表明,本課題提出的方法可以較好地估計模型參數(shù)以及隱轉(zhuǎn)錄因子的活性。改進的多目標(biāo)優(yōu)化算法相較于單目標(biāo)優(yōu)化算法魯棒性更強,降低了模型參數(shù)的估計誤差,提高了隱轉(zhuǎn)錄因子的估計精度。
[Abstract]:The occurrence of complex diseases is regulated by multiple genes, which has been the focus and difficulty of biomedical research. The continuous development of modern biology and experimental technology has created conditions for further study of gene regulation mechanism. The study of gene regulation network of complex diseases is helpful to reveal the complex life phenomena and regulation rules within complex diseases and to diagnose and treat complex diseases. In accordance with the ideas of systems biology and molecular biology, this paper studies the construction of gene network and the dynamic mechanism of gene network regulated by transcription factor (TFF) and microRNA miRNAs by means of bioinformatics. Firstly, the bioinformatics methods, principles and related databases for the construction of complex disease-related transcription factors and microRNA coregulatory gene networks are introduced. Then, the kinetic models of two kinds of transcription processes are discussed, and a feedforward loop kinetic model of co-regulation of transcription factors and microRNA is proposed. Finally, various methods, databases and feedforward loop dynamics models are applied to pancreatic cancer data. In this paper, we use multiple analysis and accurate test to analyze the differential expression of two kinds of sample data. The co-expression of differentially expressed genes and differential microRNA was obtained by weighted gene association network analysis to predict the regulatory relationship of microRNA to genes. The transcriptional factors of regulatory genes were predicted by matching differential gene and position weight model. The transcriptional factor relationships in TransmiR and ENCODE databases regulating differential microRNA were merged. The gene network of transcription factor and microRNA was constructed, and 134 feedforward ring motifs were obtained. The transcription mechanism of feedforward loop is modeled and quantitatively analyzed by differential equations. Gao Si process is used to describe the expression activity of hidden transcription factors, and the dynamic model of feedforward loop is derived based on Bayesian framework. The single-objective cultural genetic algorithm and the multi-objective cultural genetic algorithm considering the gene expression value and its gradient are used to optimize the parameters of the dynamic model and the super-parameters of the kernel function. The simulation results show that the proposed method can estimate the model parameters and the activity of hidden transcription factors. The improved multi-objective optimization algorithm is more robust than the single-objective optimization algorithm, which reduces the estimation error of the model parameters and improves the estimation accuracy of the hidden transcription factors.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R3416

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