【作者】 刘辉；

【导师】 周水庚；

【作者基本信息】 复旦大学 ， 计算机软件与理论， 2009， 博士

【摘要】 高等真核生物的基因在不同发育阶段、组织分化、环境变化等过程中表现出不同的表达模式,这是基因调控网络根据各个细胞的功能要求,以一种系统的、协调的方式调控每一个基因的表达,精确地控制着每种蛋白质的水平,从而使生物体实现“预定”的、有序的生长发育过程,并使各个器官组织在一定环境条件下保持正常的生理功能。基因调控网络相当于是细胞的神经中枢,指挥和控制着每个基因的时空特异性表达。高等真核生物的基因调控网络是一个复杂的、动态的、多层次反馈系统,构建基因调控网络是后基因组时代一项意义重大而又艰巨的任务,在系统生物学与计算生物学领域掀起了一股研究热潮。本文致力于利用基因表达数据,以建模的方法来学习基因调控网络,主要内容包括以下三个方面:1.提出一种新的贝叶斯网络学习算法并应用它从基因表达数据中学习基因调控网络。现有的贝叶斯网络学习算法常因为基因表达数据包含的基因数目大实验样本少而失败,本文提出一种分治算法,将待学习的贝叶斯网络分解成若干小的网络,独立地学习这些小的网络,之后将他们组合起来得到最终的贝叶斯网络。通过将网络进行分解,降低了网络规模和计算复杂度,有效地解决了基因数目大而实验样本少的问题。将其应用于学习酵母的基因调控网络,并与生化实验的结果进行比较,结果表明我们提出的方法具有很好的性能;2.学习量化的转录调控网络。大部分现有方法偏重于识别基因之间是否存在定性的调控关系,忽略了转录调控过程的定量关系。本文从酶动力学角度,在假设基因的转录与mRNA降解处于平衡状态的基础上,提出一种产生式模型。该模型用转录因子的浓度、转录因子与结合位点的结合能和其他动力学参量来描述基因的表达水平,具有丰富的表达能力。在酵母数据集上的实验结果表明该模型能准确地推测出转录因子的浓度,并预测转录因子的调控方向(诱导或抑制);3.预测microRNA的靶基因。人们越来越认识到microRNA(miRNA)是一类和转录因子几乎同等重要的转录后调控子,是基因调控网络不可或缺的成员。构建包含miRNA在内的基因调控网络具有重要的意义,而识别miRNA靶基因便是其中最重要的环节。本文提出了两种miRNA靶基因识别算法:基于二分图邻近传播的方法和基于监督距离学习的方法。第一种方法先通过数据分析表明miRNA主要参与了两类调控环路,然后提出一种二分图上的邻近传播算法来识别miRNA的靶基因。在人类数据集上的实验结果表明,这种方法大大提高了miRNA靶基因预测的准确率;第二种方法则将miRNA靶基因的识别视为一个二分图学习问题,进而转化为基于核的监督距离学习问题。采用实验验证过的人类miRNA-mRNA调控关系作为训练集,实验结果表明该方法具有比现有大多数方法更好的预测能力。本文提出的四种方法相继用于从基因表达数据中学习定性的、定量的、以及包含miRNA在内的基因调控网络,形成了一套系统的方法,为研究基因调控网络提供了有力工具。更多还原

【Abstract】 The genes of higher eukaryotic organisms often exhibit different expression patterns at different developmental stages, in different tissues and environments, This is attributed to the underlying genetic regulatory networks, which direct and regulates the expression of each gene in a systematic and cooperative manner according to the functional demand of each cell, and controls accurately the quantity of each type of protein, consequently achieving the growth and development of the organisms in a scheduled sequence, and keeping the normal physiological function of the cell in some limited environmental conditions. A genetic regulatory network functions as the nerve center or brain in terms of the cell level, it conducts the space-time specific expression of each gene. Generally speaking, a genetic regulatory network is a complex, dynamic feedback system with multiple regulatory levels. Construction of genetic regulatory network is an important but challenging task in the post-genome era, and has raised a research upsurge in the systems biology and computational biology fields. This thesis addresses the genetic regulatory networks via probabilistic modeling from microarray data, major contributions are as follows:1. A new algorithm for learning Bayesian networks (BNs) is proposed and applied to infer the genome-wide genetic regulatory network of yeast. The proposed method employs a divide and conquer scheme, which first decomposes the task of learning a large BN into learning some relatively small BNs, and then builds the final BN by reunifying these learnt small BNs. Our method can greatly reduce the scale of the network and computational complexity through the decomposition of network, and effectively solves the problem that the number of genes is very large but the number of samples is small.. We apply the proposed method to building genetic regulatory network from yeast microarray data and compare the learning result with that of biochemical experiments, which shows that our method can learn high-quality genetic regulatory networks.2. A new method for building quantitative transcriptional regulatory network is developed. Most existing methods focus on identifying the qualitative regulatory relationship among genes, while ignoring the quantitative aspects of the transcription regulation, which are critical to understand the function of regulators. By assuming that the transcription rate of a gene and the degeneration of its mRNA reach an equilibrium state, this paper proposes a generative model that integrates the concentration of TFs, binding energy between TFs and binging sites, and other kinetic parameters into a general regulation function, and thus has strong expressive ability. The experimental results on the yeast microarray data show that our model can accurately learn the concentrations of transcriptional factors and predict whether a transcription factor is a activator or a repressor.3. A novel approach for predicting the target genes of microRNAs is introduced. Considering that there are increasing number of evidences suggesting that microRNAs(miRNAs) are not only a type of post-transcriptional regulators as important as transcriptional factors, but also the indispensable members of the genetic regulatory network, it is great significance to building miRNAs-mediated genetic regulatory network. However, identifying the bona fide target genes of miRNAs is the stepping-stone of the goal mentioned above. This thesis proposes two methods for identification of miRNA targets, appMirTar and superMir-Tar. appMirTar first shows that miRNAs mainly mediate two types regulatory circuits, and then proposes a variant of affinity propagation algorithm customized for bipartite graph, and combines the results of sequence-based prediction algorithms and microarray data of both miRNAs and mRNAs to predict the target miRNAs. Experiments on human dataset demonstrate that our method greatly improves the prediction accuracy; superMirTar tries to identify miRNA targets in terms of bipartite graph learning, which can be further reformulated as the problem of kernel-based supervised distance metric learning. Taking the experimentally supported miRNA-mRNA interactions as training set, our experimental results show that superMirTar achieves better performance than most existing methods.The four methods proposed in this thesis has been successively used to build qualitative, quantitative and miRNAs- mediated genetic regulatory networks. They form a set of systematically analytic methods that provide powerful tools for investigating genetic regulatory networks.更多还原