基于微阵列数据的基因网络预测方法研究进展

DNA微阵列技术可同时定量测定成千上万个基因在生物样本中的表达水平,从这一技术获得的全基因组范围表达数据为揭示基因间复杂调控关系提供了可能。研究人员试图通过数学和计算方法来构建遗传互作的模型,这些基因调控网络模型有聚类法、布尔网络、贝叶斯网络、微分方程等。文章对网络重建计算方法的研究现状进行了较为全面的综述,比较了不同模型的优缺点,并对该领域进一步的研究趋势进行了展望。     

Cluster Viz:集成于Cytoscape的聚类可视化插件

生物网络中的聚类分析是功能模块识别及蛋白质功能预测的重要方法,聚类结果的可视化对于快速有效地分析生物网络结构也具有重要作用。通过分析生物网络显示和分析平台Cytoscape的架构,设计了一个使用方便的聚类分析和显示插件ClusterViz。这是一个可扩展的聚类算法的集成平台,可以不断增加其中的聚类算法,并对不同算法的结果进行比较分析,目前已实现了三种典型的算法实例。该插件能够成为蛋白质相互作用网络机理研究的一个有效工具。     

microRNA调控的生物网络

生物网络是生物体内各种分子通过相互作用来完成各种复杂的生物功能的一个体系。网络水平的研究,有助于我们从整体上理解生物体内各种复杂事件发生的内在机制。microRNA（miRNA）是一类在转录后水平调控基因表达的小RNA分子。研究结果表明,miRNA调控的靶基因分布范围很广,因此必然与目前所研究的生物网络有着各种各样的联系。对这种关系的揭示,将对阐明miRNA的调控规律起到重要的作用。本文重点讨论了miRNA调控的基因调控网络、蛋白质相互作用网络以及细胞信号传导网络的特征。此外,还总结了miRNA调控的网络模体（motif）和miRNA协同作用网络的特征。     

基于Cytoscape的蛋白质网络可视化聚类分析插件

蛋白质网络聚类是识别功能模块的重要手段,不仅有利于理解生物系统的组织结构,对预测蛋白质功能也具有重要的意义.聚类结果的可视化分析是实现蛋白质网络聚类的有效途径.本论文基于开源的Cytoscape平台,设计并实现了一个蛋白质网络聚类分析及可视化插件CytoCluster.该插件集成了MCODE,FAG-EC,HC-PIN,OH-PIN,IPCA,EAGLE等六种典型的聚类算法;实现了聚类结果的可视化,将分析所得的clusters以缩略图列表的形式直观地显示出来,对于单个cluster,可显示在原网络中的位置,并能生成相应的子图单独显示;可对聚类结果进行导出,记录了算法名称、参数、聚类结果等信息.该插件具有良好的扩展性,提供了统一的算法接口,可不断添加新的聚类算法.     

蛋白质网络聚类算法分析平台的设计与实现

蛋白质网络聚类是识别功能模块的重要手段,不仅有利于理解生物系统的组织结构,对预测蛋白质功能也具有重要的意义。针对目前蛋白质网络聚类算法缺乏有效分析软件的事实,本文设计并实现了一个新的蛋白质网络聚类算法分析平台ClusterE。该平台实现了查全率、查准率、敏感性、特异性、功能富集分析等聚类评估方法,并且集成了FAG-EC、Dpclus、Monet、IPC-MCE、IPCA等聚类算法,不仅可以对蛋白质网络聚类分析结果进行可视化,并且可以在不同聚类分析指标下对多个聚类算法进行可视化比较与分析。该平台具有良好的扩展性,其中聚类算法以及聚类评估方法都是以插件形式集成到系统中。     

基于聚类系数的DNA序列性质的复杂网络研究

利用一种新的基于图论理论的DNA序列(片段)分析的方法,即通过复杂网络研究生物体的拓扑结构,主要通过测量聚类系数(集团系数)构建网络的拓扑结构。依据DNA序列的前缀、后缀关联性质构造了所选取DNA序列(片段)的相关网络,发现该网络分布满足幂率特征,有较大的聚类系数。结果表明构建得到的网络同时满足小世界网络和无尺度网络的特征,证明DNA序列不全是随机的序列,而是有随机扰动的确定结构的序列。     

基于蛋白质网络功能模块的蛋白质功能预测

在破译了基因序列的后基因组时代,随着系统生物学实验的快速发展,产生了大量的蛋白质相互作用数据,利用这些数据寻找功能模块及预测蛋白质功能在功能基因组研究中具有重要意义.打破了传统的基于蛋白质间相似度的聚类模式,直接从蛋白质功能团的角度出发,考虑功能团间的一阶和二阶相互作用,提出了模块化聚类方法(MCM),对实验数据进行聚类分析,来预测模块内未知蛋白质的功能.通过超几何分布P值法和增、删、改相互作用的方法对聚类结果进行预测能力分析和稳定性分析.结果表明,模块化聚类方法具有较高的预测准确度和覆盖率,有很好的容错性和稳定性.此外,模块化聚类分析得到了一些具有高预测准确度的未知蛋白质的预测结果,将会对生物实验有指导意义,其算法对其他具有相似结构的网络也具有普遍意义.     

北京东灵山森林植物多样性的网络结构特征

一个群落可以看作是由物种相互连接的复杂系统,刻画其网络结构有助于深入揭示系统性质以及结构与功能之间的关系。在生态学中,复杂网络理论已被成功应用于食物网与互利网络的结构研究,但尚未检验其刻画生物多样性格局的能力。采用复杂网络理论研究了北京东灵山森林乔木层、灌木层、草本层植物关联关系的网络结构特征及其差异。结果表明,植物物种的共同出现是非随机的,并表现出一定的小世界模式;乔木层、灌木层、草本层植物共同出现的网络在结构特征上存在明显差异,草本层网络比乔木层和灌木层网络更松散且平均路径更长,灌木层和草本层网络的聚类系数高于乔木层且存在度的幂律分布。皆表明复杂网络理论具备反映不同层植物多样性格局差异的能力。     

基因表达谱聚类/分类技术研究及展望

随着人类及多种模式生物全基因组测序基本完成,人类基因组计划的研究进入后基因组时代.后基因组时代研究的焦点已经从测序转向功能研究。聚类/分类技术作为分析基因表达谱和识别基因功能的重要工具之一,近年来获得很大的发展。对目前基因表达谱聚类/分类技术及它们的发展,进行了综述性的研究,分析了它们的优缺点,结合我们的研究,提出了解决问题的思路和方法,为基因表达谱的进一步研究提供了新的途径。     

利用cvTree方法构建及分析tRNA序列的亲缘关系进化网络

本文利用cvTree和复杂网络理论相结合的方法,构建了3 420条tRNA序列亲缘关系进化网络.计算了相关网络的平均度、平均聚类系数和平均最短路径随进化距离Dis变化的关系.同时还分析和讨论了tRNA序列之间的亲缘关系及其进化机制,观察到一个与其它方法不同的结果:随着亲缘距离Dis的增加,网络的度分布先是从power-law分布转变为Gussian分布,然后又从Gussian分布转变为反power-law分布.本研究结果说明由cvTree方法和复杂网络理论相结合的方法研究tRNA基因序列的进化的结果也许更加符合其进化历程.     

Revisiting the variation of clustering coefficient of biological networks suggests new modular structure

ABSTRACT: A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. RESULTS: We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn't show dependence of degree. CONCLUSIONS: Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to "deterministic model" of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.     

Understanding biological functions through molecular networks

The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.     

Theory of interface: Category theory,directed networks and evolution of biological networks

Biological networks have two modes. The first mode is static: a network is a passage on which something flows. The second mode is dynamic: a network is a pattern constructed by gluing functions of entities constituting the network. In this paper, first we discuss that these two modes can be associated with the category theoretic duality (adjunction) and derive a natural network structure (a path notion) for each mode by appealing to the category theoretic universality. The path notion corresponding to the static mode is just the usual directed path. The path notion for the dynamic mode is called lateral path which is the alternating path considered on the set of arcs. Their general functionalities in a network are transport and coherence, respectively. Second, we introduce a betweenness centrality of arcs for each mode and see how the two modes are embedded in various real biological network data. We find that there is a trade-off relationship between the two centralities: if the value of one is large then the value of the other is small. This can be seen as a kind of division of labor in a network into transport on the network and coherence of the network. Finally, we propose an optimization model of networks based on a quality function involving intensities of the two modes in order to see how networks with the above trade-off relationship can emerge through evolution. We show that the trade-off relationship can be observed in the evolved networks only when the dynamic mode is dominant in the quality function by numerical simulations. We also show that the evolved networks have features qualitatively similar to real biological networks by standard complex network analysis.     

Disease spread in small-size directed networks: Epidemic threshold, correlation between links to and from nodes, and clustering

Network epidemiology has mainly focused on large-scale complex networks. It is unclear whether findings of these investigations also apply to networks of small size. This knowledge gap is of relevance for many biological applications, including meta-communities, plant–pollinator interactions and the spread of the oomycete pathogen Phytophthora ramorum in networks of plant nurseries. Moreover, many small-size biological networks are inherently asymmetrical and thus cannot be realistically modelled with undirected networks. We modelled disease spread and establishment in directed networks of 100 and 500 nodes at four levels of connectance in six network structures (local, small-world, random, one-way, uncorrelated, and two-way scale-free networks). The model was based on the probability of infection persistence in a node and of infection transmission between connected nodes. Regardless of the size of the network, the epidemic threshold did not depend on the starting node of infection but was negatively related to the correlation coefficient between in- and out-degree for all structures, unless networks were sparsely connected. In this case clustering played a significant role. For small-size scale-free directed networks to have a lower epidemic threshold than other network structures, there needs to be a positive correlation between number of links to and from nodes. When this correlation is negative (one-way scale-free networks), the epidemic threshold for small-size networks can be higher than in non-scale-free networks. Clustering does not necessarily have an influence on the epidemic threshold if connectance is kept constant. Analyses of the influence of the clustering on the epidemic threshold in directed networks can also be spurious if they do not consider simultaneously the effect of the correlation coefficient between in- and out-degree.     

网络理论在入侵生物学中的应用研究现状

生物入侵是一个动态有序的过程,其发生和危害存在异质性,通常由来源地、入侵地和它们之间的连接构成的系统中的自然、生物、社会等因素所决定。网络理论是研究复杂系统的一种新方法,本质是从复杂的信息中抽象出规律、揭示系统的结构特征共性。近20年,网络理论已被应用于生物入侵研究。本研究综述了网络理论在生物入侵研究中的应用进展,明确了主要的研究方向和前沿热点,认为:2000年以来国际上已开展的研究集中在评估外来物种入侵风险和入侵后对生态系统影响2个方面;外来物种随运输网络入侵的风险评估和景观连接性对入侵物种扩散的影响、外来物种入侵对本地物种间互作网络的影响及生态群落可入侵性是网络理论应用的热点;研究热点具有明显的时间发展特征,2013年以前多是对生态系统的影响,近10年来主要是风险评估。我国利用网络理论研究外来物种入侵较少且集中于对生态系统的危害,未来应加强对外来物种的时空定量传入和扩散风险评估,为我国制定和提升外来入侵物种早期监测预警、阻止新的入侵、抑制进一步扩散的管理措施提供依据。     

The dichotomy in degree correlation of biological networks

Most complex networks from different areas such as biology, sociology or technology, show a correlation on node degree where the possibility of a link between two nodes depends on their connectivity. It is widely believed that complex networks are either disassortative (links between hubs are systematically suppressed) or assortative (links between hubs are enhanced). In this paper, we analyze a variety of biological networks and find that they generally show a dichotomous degree correlation. We find that many properties of biological networks can be explained by this dichotomy in degree correlation, including the neighborhood connectivity, the sickle-shaped clustering coefficient distribution and the modularity structure. This dichotomy distinguishes biological networks from real disassortative networks or assortative networks such as the Internet and social networks. We suggest that the modular structure of networks accounts for the dichotomy in degree correlation and vice versa, shedding light on the source of modularity in biological networks. We further show that a robust and well connected network necessitates the dichotomy of degree correlation, suggestive of an evolutionary motivation for its existence. Finally, we suggest that a dichotomous degree correlation favors a centrally connected modular network, by which the integrity of network and specificity of modules might be reconciled.     

FunMod: A Cytoscape Plugin for Identifying Functional Modules in Undirected Protein–Protein Networks

The characterization of the interacting behaviors of complex biological systems is a primary objective in protein–protein network analysis and computational biology. In this paper we present FunMod, an innovative Cytoscape version 2.8 plugin that is able to mine undirected protein–protein networks and to infer sub-networks of interacting proteins intimately correlated with relevant biological pathways. This plugin may enable the discovery of new pathways involved in diseases. In order to describe the role of each protein within the relevant biological pathways, FunMod computes and scores three topological features of the identified sub-networks. By integrating the results from biological pathway clustering and topological network analysis, FunMod proved to be useful for the data interpretation and the generation of new hypotheses in two case studies.     

MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets

Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology ('MCAM') employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems.     

Exploring biological network structure using exponential random graph models

MOTIVATION: The functioning of biological networks depends in large part on their complex underlying structure. When studying their systemic nature many modeling approaches focus on identifying simple, but prominent, structural components, as such components are easier to understand, and, once identified, can be used as building blocks to succinctly describe the network. RESULTS: In recent social network studies, exponential random graph models have been used extensively to model global social network structure as a function of their 'local features'. Starting from those studies, we describe the exponential random graph models and demonstrate their utility in modeling the architecture of biological networks as a function of the prominence of local features. We argue that the flexibility, in terms of the number of available local feature choices, and scalability, in terms of the network sizes, make this approach ideal for statistical modeling of biological networks. We illustrate the modeling on both genetic and metabolic networks and provide a novel way of classifying biological networks based on the prevalence of their local features.     

Integrated analysis of multiple data sources reveals modular structure of biological networks

It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks.