本文選題：復雜疾病 + 轉錄因子　； 參考：《西安理工大學》2017年碩士論文

【摘要】：復雜疾病的發(fā)生受到多個基因的調控,一直是生物醫(yī)學研究的重點和難點�，F代生物學和實驗技術的不斷發(fā)展,為深入研究基因調控機制創(chuàng)造了條件。研究復雜疾病的基因調控網絡,對于揭示復雜疾病內部復雜的生命現象和調控規(guī)律,診斷和治療復雜疾病具有較大的推動作用。本文遵循系統(tǒng)生物學和分子生物學的思想,采用生物信息學的方法,研究轉錄因子(Transcription Factor,TF)和microRNA (miRNA)參與調控的基因網絡的構建以及基因網絡的動力學機制。首先,介紹了構建復雜疾病相關的轉錄因子和microRNA共調控基因網絡的生物信息學方法、原理以及相關數據庫。然后,討論了兩種轉錄過程的動力學模型,提出一種轉錄因子和microRNA共調控的前饋環(huán)動力學模型。最后,將各種方法、數據庫和前饋環(huán)動力學模型應用于胰腺癌數據。本課題利用倍數分析和精確檢驗對患病和正常兩種樣本數據進行差異表達分析。通過加權基因關聯網絡分析獲得差異基因和差異microRNA的共表達,來預測microRNA對基因的調控關系。利用差異基因和位置權重模型匹配,預測調控基因的轉錄因子。對TransmiR和ENCODE數據庫中調控差異microRNA的轉錄因子關系取并集。整合得到的調控關系,構建轉錄因子和microRNA共調控的基因網絡,獲得134個前饋環(huán)模體。使用微分方程組對前饋環(huán)的轉錄機制進行建模和定量分析。采用高斯過程描述隱轉錄因子的表達活性,基于貝葉斯框架對前饋環(huán)動力學模型進行推導,分別采用單目標文化遺傳算法和同時考慮基因表達值及其梯度的多目標文化遺傳算法,對動力學模型的參數和核函數的超參數進行迭代優(yōu)化求解。仿真實驗結果表明,本課題提出的方法可以較好地估計模型參數以及隱轉錄因子的活性。改進的多目標優(yōu)化算法相較于單目標優(yōu)化算法魯棒性更強,降低了模型參數的估計誤差,提高了隱轉錄因子的估計精度。
[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.
【學位授予單位】：西安理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R3416

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