Identifying disease-specific genes based on their topological significance in protein networks

Background  

The identification of key target nodes within complex molecular networks remains a common objective in scientific research. The results of pathway analyses are usually sets of fairly complex networks or functional processes that are deemed relevant to the condition represented by the molecular profile. To be useful in a research or clinical laboratory, the results need to be translated to the level of testable hypotheses about individual genes and proteins within the condition of interest.     

基于植株拓扑结构的生物量分配的玉米虚拟模型

依据植物结构—功能相互作用机理,建立了能模拟玉米生长发育与形态结构建成的虚拟模型。该模型的重要部分为基于植株拓扑结构的生物量分配模块。叙述了该模块的构建原理,以2000年田间试验数据提取了玉米的发育、生物量生产和生物量分配参数。模型模拟了2001年的玉米生长发育与生物量分配过程,模拟结果与田间试验结果比较吻合。应用该模型模拟了2001年玉米不同生育阶段植株的生物量分配和各器官生物量积累动态。     

Fully automated protein complex prediction based on topological similarity and community structure

To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.In addressing and solving these problems we present a novel random walk based algorithm that converts the incomplete and binary PPI network into a protein-protein topological similarity matrix (PP-TS matrix). We believe that if two proteins share some high-order topological similarities they are likely to be interacting with each other. Using the obtained PP-TS matrix, we constructed and used weighted networks to further study and analyze the interaction among proteins. Specifically, we applied a fully automated community structure finding algorithm (Auto-HQcut) on the obtained weighted network to cluster protein complexes. We then analyzed the protein complexes for significance in biological processes. To help visualize and analyze these protein complexes we also developed an interface that displays the resulting complexes as well as the characteristics associated with each complex.Applying our approach to a yeast protein-protein interaction network, we found that the predicted protein-protein interaction pairs with high topological similarities have more significant biological relevance than the original protein-protein interactions pairs. When we compared our PPI network reconstruction algorithm with other existing algorithms using gene ontology and gene co-expression, our algorithm produced the highest similarity scores. Also, our predicted protein complexes showed higher accuracy measure compared to the other protein complex predictions.     

Computing topological parameters of biological networks

Rapidly increasing amounts of molecular interaction data are being produced by various experimental techniques and computational prediction methods. In order to gain insight into the organization and structure of the resultant large complex networks formed by the interacting molecules, we have developed the versatile Cytoscape plugin NetworkAnalyzer. It computes and displays a comprehensive set of topological parameters, which includes the number of nodes, edges, and connected components, the network diameter, radius, density, centralization, heterogeneity, and clustering coefficient, the characteristic path length, and the distributions of node degrees, neighborhood connectivities, average clustering coefficients, and shortest path lengths. NetworkAnalyzer can be applied to both directed and undirected networks and also contains extra functionality to construct the intersection or union of two networks. It is an interactive and highly customizable application that requires no expert knowledge in graph theory from the user. AVAILABILITY: NetworkAnalyzer can be downloaded via the Cytoscape web site: http://www.cytoscape.org     

Interaction energy based protein structure networks

The three-dimensional structure of a protein is formed and maintained by the noncovalent interactions among the amino-acid residues of the polypeptide chain. These interactions can be represented collectively in the form of a network. So far, such networks have been investigated by considering the connections based on distances between the amino-acid residues. Here we present a method of constructing the structure network based on interaction energies among the amino-acid residues in the protein. We have investigated the properties of such protein energy-based networks (PENs) and have shown correlations to protein structural features such as the clusters of residues involved in stability, formation of secondary and super-secondary structural units. Further we demonstrate that the analysis of PENs in terms of parameters such as hubs and shortest paths can provide a variety of biologically important information, such as the residues crucial for stabilizing the folded units and the paths of communication between distal residues in the protein. Finally, the energy regimes for different levels of stabilization in the protein structure have clearly emerged from the PEN analysis.     

A new family of Cayley graph interconnection networks based on wreath product and its topological properties

This paper introduces a new type of Cayley graphs for building large-scale interconnection networks, namely WG_n^2m\mathit{WG}_{n}^{2m}, whose vertex degree is m+3 when n≥3 and is m+2 when n=2. A routing algorithm for the proposed graph is also presented, and the upper bound of the diameter is deduced as ⌊5n/2⌋. Moreover, the embedding properties and maximal fault tolerance are also analyzed. Finally, we compare the proposed networks with some other similar network topologies. It is found that WG_n^2m\mathit{WG}_{n}^{2m} is superior to other interconnection networks because it helps to construct large-scale networks with lower cost.     

Controlling nosocomial infection based on structure of hospital social networks

Nosocomial infection (i.e. infection in healthcare facilities) raises a serious public health problem, as implied by the existence of pathogens characteristic to healthcare facilities such as methicillin-resistant Staphylococcus aureus and hospital-mediated outbreaks of influenza and severe acute respiratory syndrome. For general communities, epidemic modeling based on social networks is being recognized as a useful tool. However, disease propagation may occur in a healthcare facility in a manner different from that in a urban community setting due to different network architecture. We simulate stochastic susceptible-infected-recovered dynamics on social networks, which are based on observations in a hospital in Tokyo, to explore effective containment strategies against nosocomial infection. The observed social networks in the hospital have hierarchical and modular structure in which dense substructure such as departments, wards, and rooms, are globally but only loosely connected, and do not reveal extremely right-skewed distributions of the number of contacts per individual. We show that healthcare workers, particularly medical doctors, are main vectors (i.e. transmitters) of diseases on these networks. Intervention methods that restrict interaction between medical doctors and their visits to different wards shrink the final epidemic size more than intervention methods that directly protect patients, such as isolating patients in single rooms. By the same token, vaccinating doctors with priority rather than patients or nurses is more effective. Finally, vaccinating individuals with large betweenness centrality (frequency of mediating connection between pairs of individuals along the shortest paths) is superior to vaccinating ones with large connectedness to others or randomly chosen individuals, which was suggested by previous model studies.     

神经网络在蛋白质二级结构预测中的应用

介绍了蛋白质二级结构预测的研究意义,讨论了用在蛋白质二级结构预测方面的神经网络设计问题,并且较详尽地评述了近些年来用神经网络方法在蛋白质二级结构预测中的主要工作进展情况,展望了蛋白质结构预测的前景。     

Functional and topological characterization of protein interaction networks

The elucidation of the cell's large-scale organization is a primary challenge for post-genomic biology, and understanding the structure of protein interaction networks offers an important starting point for such studies. We compare four available databases that approximate the protein interaction network of the yeast, Saccharomyces cerevisiae, aiming to uncover the network's generic large-scale properties and the impact of the proteins' function and cellular localization on the network topology. We show how each database supports a scale-free, topology with hierarchical modularity, indicating that these features represent a robust and generic property of the protein interactions network. We also find strong correlations between the network's structure and the functional role and subcellular localization of its protein constituents, concluding that most functional and/or localization classes appear as relatively segregated subnetworks of the full protein interaction network. The uncovered systematic differences between the four protein interaction databases reflect their relative coverage for different functional and localization classes and provide a guide for their utility in various bioinformatics studies.     

Vavien: an algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks

Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health and disease. An important challenge is to narrow down the set of candidate genes that are implicated by these analyses. Protein-protein interaction (PPI) networks are useful in extracting the functional relationships between known disease and candidate genes, based on the principle that products of genes implicated in similar diseases are likely to exhibit significant connectivity/proximity. Information flow?based methods are shown to be very effective in prioritizing candidate disease genes. In this article, we utilize the topology of PPI networks to infer functional information in the context of disease association. Our approach is based on the assumption that PPI networks are organized into recurrent schemes that underlie the mechanisms of cooperation among different proteins. We hypothesize that proteins associated with similar diseases would exhibit similar topological characteristics in PPI networks. Utilizing the location of a protein in the network with respect to other proteins (i.e., the "topological profile" of the proteins), we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. We test the resulting algorithm, Vavien, via systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance in Man (OMIM) database. Vavien outperforms other network-based prioritization algorithms as shown in the results and is available at www.diseasegenes.org.     

Mx基因稀有密码子和mRNA结构及大肠杆菌表达 优化

通过对稀有密码子和mRNA翻译起始区二级结构的分析, 构建了4种重组表达菌株BL21(DE3)/pET-Mx, Rosseta(DE3)/pET-Mx, BL21(DE3)/pGEX-Mx和Rosseta(DE3)/pGEX-Mx, 在大肠杆菌中进行Mx基因的表达, Rosseta(DE3)/pET-Mx和Rosseta(DE3)/pGEX-Mx重组表达菌中都获得了表达, Western blotting检测到了特异的75 kDa表达产物。实验结果证明稀有密码子和mRNA翻译起始区二级结构对Mx 蛋白表达都有影响, 选择适用于稀有密码子表达的菌株Rosetta(DE3)有利于Mx蛋白的表达, 同时翻译起始区二级结构能值较低的表达载体pGEX-Mx获得的表达量明显增高。实验中首次获得了重组表达鸡全长Mx蛋白的大肠杆菌重组菌。     

Analyzing network topological characteristics of eco-industrial parks from the perspective of resilience: A case study

Realizing the stable operation of an eco-industrial park (EIP) as a complex system consisting of a variety of the enterprises and embedded relations is challenging. The topological structure plays an important role to understand the balance of network resilience and eco-efficiency in the operation process of a given EIP. In this paper, Ningdong Coal Chemical Eco-industrial Park (Ningdong CCEIP) is used as a case study in Ningxia Hui Autonomous Region of China. Based on complex network theory, we focus on topological characteristics analysis of symbiotic network from the perspective of resilience. Results reveal that Ningdong CCEIP has scale-free characteristics as well as the small world ones. Compared with the node-level metrics, the important degree of node considering ecological factor is a more crucial index measuring the importance of a particular node in the network. The removal of top 10% node contributes to 60% decrease of network efficiency, which indicates the decline of resilience in the studied case. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of EIPs. This study can help us better understand the strategies for avoiding disruptions, improving the resilience of EIP and safeguarding the stable operation.     

核糖体RNA二级结构对拓扑结构准确性的影响

探讨了核糖体小亚基二级结构对真菌系统发育分析的影响。对用不同方法构建的系统发育树进行比较,结果表明结合二级结构信息的分析方法较传统方法产生了更为合理的拓扑结构。二级结构信息除用于优化序列比对外,还需整合到核酸替代模型中;恰当的序列比对方法、进化模型和建树运算法则有助于更加准确地揭示类群之间的亲缘关系。     

Succinctly assessing the topological importance of species in flower–pollinator networks

The topological importance of species within networks is an important way of bringing a species-level consideration to the study of whole ecological networks. There are many different indices of topological importance, including centrality indices, but it is likely that a small number are sufficient to explain variation in topological importance. We used 14 indices to describe topological importance of plants and pollinators in 12 quantitative mutualistic (plant–pollinator) networks. The 14 indices varied in their consideration of interaction strength (weighted versus unweighted indices) and indirect interactions (from the local measure of degree to meso-scale indices). We use principal components approximation to assess how well every combination of 1–14 indices approximated to the results of principal components analysis (PCA). We found that one or two indices were sufficient to explain up to 90% of the variation in topological importance in both plants and pollinators. The choice of index was crucial because there was considerable variation between the best and the worst approximating subsets of indices. The best single indices were unweighted degree and unweighted topological importance (Jordán's TI index) with two steps (a measurement of apparent competition). The best pairs of indices consisted of a measure of a TI index and one of closeness centrality (weighted or unweighted) or d′ (a standardised species-level measure of partner diversity). Although we have found indices that efficiently explain variation in topological importance, we recommend further research to discover the real-world relevance of different aspects of topological importance to species in ecological networks.     

一种基于基因拓扑重要性的通路识别方法

癌症相关通路的识别是认识癌症发生发展过程机制的生物学基础。已有的通路识别方法很少考虑基因在通路中的拓扑重要性。重叠基因降权(PADOG)方法在基因集分析(GSA)方法的基础上融入了基因特异性的影响,提高了癌症相关通路的识别性能。为进一步提高癌症相关通路的识别性能,首先统计了KEGG通路数据集中基因出度的分布情况,根据基因出度的大小定义了基因的重要性。最后将基因的特异性和重要性融合在一起,提出了一种基于基因重要性和特异性的通路分析方法 PAGIS。在结肠癌、肺癌和胰腺癌3个数据集上的实验结果表明,PAGIS方法比PADOG能够提高很多癌症相关的排名,从而提高癌症相关通路的识别效果。     

The effects of density on the topological structure of the mitochondrial DNA from trypanosomes

Trypanosomatida parasites, such as trypanosoma and lishmania, are the cause of deadly diseases in many third world countries. A distinctive feature of these organisms is the three dimensional organization of their mitochondrial DNA into maxi and minicircles. In some of these organisms minicircles are confined into a small disk volume and are topologically linked, forming a gigantic linked network. The origins of such a network as well as of its topological properties are mostly unknown. In this paper we quantify the effects of the confinement on the topology of such a minicircle network. We introduce a simple mathematical model in which a collection of randomly oriented minicircles are spread over a rectangular grid. We present analytical and computational results showing that a finite positive critical percolation density exists, that the probability of formation of a highly linked network increases exponentially fast when minicircles are confined, and that the mean minicircle valence (the number of minicircles that a particular minicircle is linked to) increases linearly with density. When these results are interpreted in the context of the mitochondrial DNA of the trypanosome they suggest that confinement plays a key role on the formation of the linked network. This hypothesis is supported by the agreement of our simulations with experimental results that show that the valence grows linearly with density. Our model predicts the existence of a percolation density and that the distribution of minicircle valences is more heterogeneous than initially thought.