乳腺癌转移相关基因表达调控网络的线性微分方程模型的初步研究

初步构建乳腺癌转移相关基因表达调控网络的线性微分方程模型,并分析模型的可靠性和生物学意义. 采用基因芯片技术,分别对30例伴有淋巴结转移的乳腺癌组织及其相应淋巴结转移癌组织进行基因表达谱的比较,选择差异基因通过线性微分数学方法构建表达调控网络模型. 差异表达基因共27个,其中Ratio > 3的明显上调基因14个,而Ratio < 0.33的明显下调基因13个. 比较伴有淋巴结转移的乳腺癌组织和其相应淋巴结转移癌组织,分析筛选了27个表达差异基因,应用数学线性微分方程方法初步构建乳腺癌转移相关基因表达调控网络的线性微分方程模型,通过分析模型中重要节点、通路的生物学意义,判定网络的数学特性,初步表明,调控网络的可靠性和乳腺癌转移的形成是与多基因、多通路异常引起的细胞恶性转化相关.     

时间延迟基因调控网络重构的决策树方法研究

基因调控网络的重构是功能基因组中最具挑战性的课题之一. 针对基因间转录调控的时间延迟性, 提出了一种寻找时间延迟调控关系的方法: 多点延迟调控网络算法, 简称TdGRN (time-delayed gene regulatory networking). 该方法根据时间序列基因表达谱数据, 构建时间延迟基因表达矩阵, 利用有监督决策树分类器方法和随机重排技术挖掘基因之间的时间延迟调控关系, 从而构建时间延迟的基因调控网络. 该方法是一种不依赖模型的基因网络重建方法, 相对于目前采用的基于模型的网络重建方法有显著优势, 可直接利用连续的基因表达谱数据发现延迟任一时间单位差的基因表达调控关系, 并避免了目前一些研究方法中需要人为设定基因的最大调控子数目(k)的问题. 将该方法应用于酿酒酵母细胞周期的基因表达谱数据, 并构建时间延迟的基因调控网络, 结果发现多数时间延迟调控关系获得了已有知识的支持.     

基于多目标评价方法的乳腺癌易感基因排序及其网络表达研究

目的利用已有的研究结果和数据,采用多目标评价方法建立乳腺癌易感基因评价模型,对与已知乳腺癌基因关系密切的其它基因进行分析和排序,并给出结果的网络表达模式。方法通过分析已有的文献,并利用有关的基因数据库和已有文献中的数据,提炼出乳腺癌易感基因的多目标评价体系,构建基于加权和法的乳腺癌易感基因评价模型,并利用Cytoscape软件进行评价结果计算和评价结果的网络模式表达。结果利用多目标模型所得到的评价结果,与已有的研究结果一致。其中,乳腺癌易感基因TopBP1排名第二,已知乳腺癌候选易感基因HMMR排名第六。结论文章提出的多目标评价模型能够准确评价被选基因与乳腺癌易感性之间的关系,所提出的评价方法与相关软件结合使用,将成为癌症易感基因研究方面有效的分析方法和途径。     

主成分分析方法在基因调控网中的应用研究

利用基因表达值推断基因调控网是生物学中的一个重要领域,在疾病治疗领域和药物研究领域具有很好的效用.状态空间模型是一种新型优化的模型,在基因调控网中给出系统理论的分析和研究,较动态贝叶斯网络模型在平行的精度下具有极低的运行时间.本论文在前人工作基础上,研究主成分分析方法在状态空间模型中的应用,通过对调控基因关系矩阵取和方法找出主导基因,从而优化之前的线性空间模型,得到较优的结果.     

基因芯片表达在乳腺癌转移过程中的聚类分析

利用基因芯片可以得到不同基因在不同生命过程中的表达,因此在医学诊断与病变分析中受到重视,并开始大量应用．经测定发现,不同基因在病变过程的不同阶段中的表达是不相同的,由此可以得到在病变过程的不同基因的表达特征．在本文中,我们给出了乳腺癌在转移过程中的基因表达特征的聚类分析法分析,并改进了k-means聚类算法,使之具有自动搜索聚类数的功能,并且有助于改善k-means算法的聚类结果陷入局部最小值的状况．通过对平均聚类误差指标的比较,kr—means要优于k-means算法．本文所得到的结果可供乳腺癌诊断与病变分析参考,同时可以应用于小型基因检测芯片的制备,也可以用于构建基因网络调控图．     

乳腺癌致病microRNA调控网络的识别与生物信息学分析

目的:构建并解析乳腺癌致病microRNA(miRNA)调控网络,探究其在乳腺癌发生发展中的调控机制。方法:整合TCGA、ENCODE、Fantom等公共数据库资源,得到miRNA、转录因子和基因候选调控关系数据,结合差异表达、变异系数与PCA,构建乳腺癌miRNA调控网络,解析调控网络的度中心性与聚类系数,使用DAVID进行功能富集分析,构建Cox回归模型作生存曲线。结果:共识别miRNA调控网络262个,其中包含5个显著差异表达miRNA,8个转录因子和130个基因。通过功能富集分析发现这些miRNA靶基因显著参与细胞周期、细胞分化、细胞生长、转移等转录后调控的肿瘤生物进程,并与FoxO信号通路、p53信号通路、基因监测通路等信号通路高度相关。通过分析生存曲线发现hsa-mir-144与hsa-mir-133a-2显著与乳腺癌患者生存相关。结论:识别的乳腺癌致病miRNA调控网络中miRNA之间有相互作用,且网络整体功能不仅受hub网络影响,也受元件自身特性影响,这些miRNA靶基因显著富集于肿瘤相关生物学进程与信号通路中。     

基于miRNA-mRNA联合分析的卵巢癌调控网络构建

研究表明微小RNA(microRNA,miRNA)通过影响转录后基因表达来调节机体功能,并与肿瘤的发生有密切关系。然而目前癌症致病过程的转录调控网络重构大多致力于转录层面的基因表达数据的处理和分析,如何整合转录及转录后不同类型的生物数据以挖掘它们的共调控机制是目前的研究热点之一。基于此,本研究利用联合非负矩阵分解算法融合卵巢癌miRNA数据和基因表达数据形成共模块,其次对特征模块中miRNA的靶基因进行预测分析,最后对mi RNA-mRNA共模块进行转录及转录后共调控网络构建。仿真结果及分子生物学分析表明,通过联合矩阵分析方法所提取的共模块显示出了与卵巢癌致病具有显著的生物相关性和潜在的联系,此外,GO生物过程分析也进一步的揭示了所提取的共模块中miRNA靶基因的生物学功能与卵巢癌致病密切相关。     

采用系统生物学方法分析乳腺癌转移中Runx2对细胞外基质重塑的调节机制

摘要细胞外基质(extracellular matrix,ECM)重塑是癌细胞迁移的关键步骤.本研究基于乳腺癌组织的基因表达谱数据,采用系统生物学方法推测乳腺癌转移中Runx2对细胞外基质重塑的调节机制.采用相关性分析程序分析49例乳腺原发癌和15例淋巴结转移癌组织的基因表达谱数据,筛选与Runx2呈相关性表达的基因,结果得到与ECM重塑相关的候选基因52个,包括ECM成分11个,ECM降解酶及其抑制剂8个,细胞信号分子33个.利用转录调节因子结合序列数据库搜索候选基因启动子区的Runx2结合模序,筛选其中Runx2转录调控的ECM重塑相关基因,并判断可能调节Runx2的上游信号分子;文献检索实验证实的与Runx2有相互调节关系的基因,并基于Runx2上游调控信号分子和下游转录调节基因的分析,构建得到以Runx2为中心的ECM重塑的生物学调控网络.WNT和TGF/BMPs是启动Runx2表达的主要信号通路,Runx2通过转录调节ECM组分、ECM降解酶及其抑制剂和信号分子调节ECM重塑,促进癌细胞完成转移的生物学过程.     

基于微阵列数据构建基因调控网络

随着微阵列数据的快速增长, 微阵列基因表达数据日益成为生物信息学研究的重要数据源。利用微阵列基因表达数据构建基因调控网络也成为一个研究热点。通过构建基因调控网络, 可以解读复杂的调控关系, 发现细胞内的调控模式, 并进而在系统尺度上理解生物学进程。近年来, 人们引入了多种算法来利用基因芯片数据构建基因调控网络。文章回顾了这些算法的发展历史, 尤其是其在理论和方法上的改进, 给出了一些相关的软件平台, 并预测了该领域可能的发展趋势。     

Learning Gene Networks under SNP Perturbations Using eQTL Datasets

The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs) that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network identified computationally by our method under SNP perturbations is well supported by the results from experimental perturbation studies related to DNA replication stress response.     

A multiorganism based method for Bayesian gene network estimation

The primary goal of this article is to infer genetic interactions based on gene expression data. A new method for multiorganism Bayesian gene network estimation is presented based on multitask learning. When the input datasets are sparse, as is the case in microarray gene expression data, it becomes difficult to separate random correlations from true correlations that would lead to actual edges when modeling the gene interactions as a Bayesian network. Multitask learning takes advantage of the similarity between related tasks, in order to construct a more accurate model of the underlying relationships represented by the Bayesian networks. The proposed method is tested on synthetic data to illustrate its validity. Then it is iteratively applied on real gene expression data to learn the genetic regulatory networks of two organisms with homologous genes.     

The formulation of the control of an expression pattern in a gene network by propositional calculus

In this study we model a gene network as a continuous-time switching network. In this model, each gene has a binary state which indicates if the gene expresses or not. We propose a method to control a sequence of expression patterns in the gene network model by adding another continuous-time switching network. By using propositional calculus, we will show that the control problem can be formulated as a mixed-integer linear programming problem with linear constraints.     

Dynamic models of gene expression and classification

Powerful new methods, like expression profiles using cDNA arrays, have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole genome expression. In this approach, a self-consistent calculation is performed that involves both linear and non-linear response terms for interrelating gene expression levels. This calculation uses singular value decomposition (SVD) not as a statistical tool but as a means of inverting noisy and near-singular matrices. The linear transition matrix that is determined from this calculation can be used to calculate the underlying network reflected in the data. This suggests a direct method of classifying genes according to their place in the resulting network. In addition to providing a means to model such a large multivariate system this approach can be used to reduce the dimensionality of the problem in a rational and consistent way, and suppress the strong noise amplification effects often encountered with expression profile data. Non-linear and higher-order Markov behavior of the network are also determined in this self-consistent method. In data sets from yeast, we calculate the Markov matrix and the gene classes based on the linear-Markov network. These results compare favorably with previously used methods like cluster analysis. Our dynamic method appears to give a broad and general framework for data analysis and modeling of gene expression arrays. Electronic Publication     

Structural systems identification of genetic regulatory networks

MOTIVATION: Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit. RESULTS: In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints. AVAILABILITY: The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.     

Ensemble learning of genetic networks from time-series expression data

MOTIVATION: Inferring genetic networks from time-series expression data has been a great deal of interest. In most cases, however, the number of genes exceeds that of data points which, in principle, makes it impossible to recover the underlying networks. To address the dimensionality problem, we apply the subset selection method to a linear system of difference equations. Previous approaches assign the single most likely combination of regulators to each target gene, which often causes over-fitting of the small number of data. RESULTS: Here, we propose a new algorithm, named LEARNe, which merges the predictions from all the combinations of regulators that have a certain level of likelihood. LEARNe provides more accurate and robust predictions than previous methods for the structure of genetic networks under the linear system model. We tested LEARNe for reconstructing the SOS regulatory network of Escherichia coli and the cell cycle regulatory network of yeast from real experimental data, where LEARNe also exhibited better performances than previous methods. AVAILABILITY: The MATLAB codes are available upon request from the authors.     

The use of nonmetric variation in estimating human population admixture: a test case with Brazilian blacks, whites, and mulattos

Measurements in populations which serve as valid indicators of biological relationship should be proportional to genetic distance. In order to test the utility of discrete cranial traits for estimating genetic distances among populations, estimates of admixture are obtained for gene frequency data and nonmetric cranial data in São Paulo mulattos (M). The gene frequency data serve as a control that the three populations are related as stated: estimates of admixture are obtained by using São Paulo whites (W) and blacks (B) as parental populations and by estimating the parameter of admixture, m, in the model p_M = (1 ? m) p_W + mp_B (Elston, 1971) where the p's are either gene frequencies or nonmetric trait frequencies. A test of goodness of fit of the model provides a means of ascertaining whether or not the data fit this linear model. While the gene frequency data indicate distances among the three populations which are highly compatible with the linear model of admixture, the nonmetric data show significant deviations from the model. This implies that the frequencies of the nonmetric traits in the populations used in this analysis are not a linear function of genetic distance. This discourages the use of nonmetric traits in making quantitative conclusions about genetic relationships. It also suggests the need for investigation of the use of other skeletal characters for estimating genetic distance, as well as approaches for such investigations through the study of hybrid individuals.