细胞黏附分子互作网络的构建与分析

借助网络分析可对基因调控、蛋白质互作和信号转导等细胞活动进行全局和局部性质分析.以细胞黏附的蛋白质相互作用为对象,通过数据挖掘和可视化软件构建了整合蛋白介导的黏附分子互作网络,该分子互作网络由156种蛋白质通过690种相互作用相连,其平均节点度为8.66、平均聚集系数为0.24,平均路径长度为2.6.黏附分子互作网络中包含数个功能模块,这些模块涉及网络内部多种分子相互作用的启动与停止,并进一步影响细胞的黏附、迁移和骨架组织.对黏附分子网络进行模体筛选和比较,发现一些数量相对较少、以三元复合物为主要结构的关键模体,同时对各网络模块和模体对细胞黏附的调控作用进行了探讨.     

城市生态网络分析研究进展

自生态网络分析方法提出40多年来,其理论发展和应用实践不断拓展,但直至21世纪才不断引入到城市生态系统研究中,用以分析城市内部多个主体和多种生态流构成的关联网络。目前,城市生态网络分析集中于生态网络分析方法与指标的拓展及多尺度的应用研究,而生态网络分析方法又形成了上升性分析和环境元分析两大分支,多尺度应用涵盖了城市镶嵌的区域背景尺度和城市内部产业部门之间的细节尺度。然而,当前研究仍存在着多尺度融合、多种生态网络分析方法集成不足等问题,这限制了城市生态网络分析方法在城市规划设计中的应用。未来城市生态网络分析研究集中于如下3点:(1)开展多尺度城市生态网络分析,包括城市群-城市-园区/社区等,构建多级嵌套生态网络模型;(2)集成上升性与环境元分析方法,提出由外在表征到内在过程的城市生态系统评价模式及模拟方法;(3)强调自然节点在城市生态网络中的重要作用,形成社会经济节点与自然节点并重的生态网络模型,并强调构建多精度的生态网络模型服务于不同的研究目的。     

网络药理学与药物发现研究进展

将生物学网络与药物作用网络整合,分析药物在网络中与节点或网络模块的关系,由寻找单一靶点转向综合网络分析,就形成了网络药理学.通过系统生物学的研究方法进行网络药理学分析,能够在分子水平上更好的理解细胞以及器官的行为,加速药物靶点的确认以及发现新的生物标志物.这使得我们有可能系统地预测和解释药物的作用,优化药物设计,发现影响药物作用有效性和安全性的因素,从而设计多靶点药物或药物组合.本文综述了网络药理学的新近研究进展,介绍在生物学网络的各个层面上网络药理学的研究和应用,展望网络药理未来的发展方向,对药物发现具有重要意义.     

传粉网络的研究进展:网络的结构和动态

植物与传粉者之间相互作用,构成了复杂的传粉网络。近年来,社会网络分析技术的发展使得复杂生态网络的研究成为可能。从群落水平上研究植物与传粉者之间的互惠关系,为理解群落的结构和动态以及花部特征的演化提供了全新的视角。传粉网络的嵌套结构说明自然界的传粉服务存在冗余,而且是相对泛化的物种主导了传粉。在多年或者多季度的传粉网络中,虽然有很高的物种替换率,但是其网络结构仍然保持相对稳定,说明传粉网络对干扰有很强的抗性。尽管有关网络结构和动态的研究逐渐增多,但传粉网络维持的机制仍不清楚。网络结构可以部分由花部特征与传粉者的匹配来解释,也受到系统发生的制约,影响因素还包括群落构建的时间和物种多样性,以及物种在群落中的位置。开展大尺度群落动态的研究,为探索不同时间尺度、不同物种多样性水平上的传粉网络的生态学意义提供了条件。但已有的研究仍存在不足,比如基于访问观察的网络无法准确衡量传粉者的访问效率和植物间的花粉流动,以及结果受到调查精度区域研究不平衡的制约等。目前的研究只深入到传粉者携带花粉构成成分的水平,传粉者访问植物的网络不能代表植物的整个传粉过程。因此,研究应当更多地深入到物种之间关系对有性生殖的切实影响上。     

生态网络分析方法研究综述

生态网络分析方法是分析生态系统作用关系、辨识系统内在、整体属性的一种有效的系统分析方法。总结了生态网络分析方法的主要研究成果:网络结构特性、网络稳定性、网络上升性、网络效能等;介绍了构建生态网络模型过程和群落构建规则;以德国西部城市诺伊斯河口氮循环为例,介绍David K是如何运用生态网络分析方法来揭示网络中的微动力流循环规律。生态网络分析方法的主要贡献:(1)对人们凭经验感知的生态系统分室间的关联关系,采用了严密的数学模型和推导进行了描述和证明;(2)为生态系统的微动力流循环的研究提供了方法,对生态系统中物质流的间接循环作用进行了科学论证;(3)不仅为分析生态系统提供了一种科学的数学方法,而且,它为探索生态系统提供了不同与牛顿世界观的崭新的认识论。总结与回顾生态网络分析方法,有益于该方法的运用和进一步完善。     

基因共表达网络分析揭示马铃薯薯块发育特征及原始抗性

转录组测序(RNA-seq)技术提供的全基因组数据信息已广泛应用于研究多个样本之间的基因表达模式和调控机制.通过构建种间或种内基因共表达网络(GCNs)挖掘的表达相关基因在功能上通常是相似的.对于马铃薯(Solanum tuberosum)而言,目前有大量的公共转录组测序数据,但是缺乏针对这些高通量数据构建的GCN网络,因此也无法探索在不同基因型、不同组织以及不同环境条件下基因的表达模式及规律.本研究选取16个公共转录组测序数据库构建了 GCN网络,这些数据库涵盖了来自全球各地的11个马铃薯栽培种.基于两两间基因表达相关性,我们在GCN网络中发现了一些具有特定生物学意义的基因模块.该网络共由14个基因模块组成并富集到植物光合形态建成、薯块休眠解除等多个生理过程,其中一个模块的134个基因在原始栽培种(ssp.Andigena)中特异性高表达,且通过功能富集发现这些基因与马铃薯病害和逆境的抗性相关.该结果揭示了在马铃薯人工驯化期间基因进化压力出现遗传漂移.本研究中基于GCN网络分析揭示了马铃薯种间和种内基因共表达模块的聚类以及不同模块基因间在进化上的分化,为马铃薯基因功能研究提供了新的视角.     

网络分析在宿主植物与菌根真菌共生关系研究中的应用

菌根共生体是生物界最广泛的互惠共生体,共生关系多样性是生物多样性的重要组成部分,当前群落尺度菌根共生关系研究才刚刚起步,但发展迅速。网络分析作为生态学研究的重要手段逐渐在菌根共生关系中得以应用,网络分析为群落尺度探究菌根真菌多样性分布规律、共生机制研究提供新观点和途径,对菌根真菌群落结构、生态功能研究具有重要意义。本文总结了网络分析方法在单点式、双点式和多点式菌根共生关系网络研究中的优势和局限性,同时还阐述了零模型选择和构建网络大小对关系网络度量指数的影响,为菌根真菌群落结构、生态功能研究提供新思路,为后续群落尺度菌根共生关系格局研究提供借鉴。     

浅谈网络分析法及其在古生物学中的应用

网络分析作为一种新的数据可视化途径和定量方法, 能简化复杂系统而发现元素间的关系模式。它在定量社会科学、计算机科学与机器学习等领域均有众多应用实例。近年来, 在古生物学, 特别是古生物地理学相关研究中逐渐受到关注。本文介绍了网络的基本构成、常见网络类型及其数据储存方式、以及网络分析中的重要参数及其定义, 同时给出了两种实现网络分析的方法及相关工具, 即Gephi软件与R语言平台。通过分析比较两种方法的步骤及结果, 发现Gephi虽成图简洁美观, 但算法功能有限, 且无法完成成图前的数据处理以及成图后的多元分析, 因而最终推荐使用R语言编程进行网络分析。本文以奥陶纪末大灭绝后复苏期全球腕足动物数据为例, 详细展现了利用R语言及其应用包“igraph”编程进行网络分析的过程, 并实现了古生物地理学数据资料的处理以及网络分析图件的绘制。希望对即将接触此类工具的古生物学科研人员在进行网络分析时提供借鉴与参考。     

信号转导网络的生物信息学分析

研究信号转导是了解生命活动过程的重要途径。随着实验方法的改进和实验数据的积累,很多信号转导通路的作用机制已经被揭示,对于已有信号转导数据的分析和利用已成为热点问题。本文综述了最近几年生物信息学在信号转导网络分析方面取得的最新进展,简要介绍了信号转导的特点和作用机制,并对网上相关的数据库资源进行总结,给出了信号转导网络的结构分析方法,包括网络的拓扑属性分析、结构模块搜索及信号通路的自动生成,重点对信号转导网络的建模和仿真方法进行了讨论,分析了该领域的研究现状及可能的发展方向。总体而言,对于信号转导网络的研究已经从小规模的实验研究向大规模的网络分析方向发展,对于网络的动态模拟更加接近真实系统。随着对信号转导的研究更加广泛和深入,对于信号转导网络的生物信息学分析将具有广阔的发展和应用前景。     

基于斑马鱼模型和动态分子对接技术探讨西洋参抗缺氧作用及潜在靶点CSCD

本研究利用斑马鱼模型和动态分子对接技术研究西洋参抗缺氧(hypoxia)的作用及潜在靶点。以AB系斑马鱼为实验动物,无水硫酸钠为造模剂诱导形成斑马鱼幼鱼缺氧模型,综合评价西洋参的抗缺氧作用。借助网络药理学技术筛选西洋参活性成分以及缺氧相关共有靶点;并使用STRING平台和Cytoscape 3.8.2软件构建蛋白-蛋白作用网络图,寻找西洋参抗缺氧可能的潜在靶点;利用动态分子对接技术验证活性成分与关键靶点的结合能力和稳定性。斑马鱼体内实验显示,西洋参提取物可明显增加斑马鱼在缺氧条件下的存活率,减轻因缺氧导致的神经行为状态(P<0.05),且呈剂量依赖性相关。共筛选获得西洋参7个潜在活性成分和5个抗缺氧潜在靶点;动态分子对接结果显示,西洋参关键活性成分与靶点之间有良好的结合能力,其中TNF、HSP90AA1与活性成分稳定结合,可能为西洋参抗缺氧的关键潜在靶点。综上,本实验建立的斑马鱼幼鱼抗缺氧模型可以快速简便地评价西洋参样品的抗缺氧活性,动态分子对接的结果显示该作用可能通过TNF以及HSP90AA1等靶点发挥作用,为后续西洋参抗缺氧机制研究提供了参考依据。     

Network biology bridges the gaps between quantitative genetics and multi-omics to map complex diseases

With advances in high-throughput sequencing technologies, quantitative genetics approaches have provided insights into genetic basis of many complex diseases. Emerging in-depth multi-omics profiling technologies have created exciting opportunities for systematically investigating intricate interaction networks with different layers of biological molecules underlying disease etiology. Herein, we summarized two main categories of biological networks: evidence-based and statistically inferred. These different types of molecular networks complement each other at both bulk and single-cell levels. We also review three main strategies to incorporate quantitative genetics results with multi-omics data by network analysis: (a) network propagation, (b) functional module-based methods, (c) comparative/dynamic networks. These strategies not only aid in elucidating molecular mechanisms of complex diseases but can guide the search for therapeutic targets.     

Enabling dynamic network analysis through visualization in TVNViewer

ABSTRACT: BACKGROUND: Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development of TVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis. RESULTS: In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets. CONCLUSIONS: TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.     

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.     

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid (Y2H), mass spectrometry (MS), co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks.     

Identification of potential targets in biological signalling systems through network perturbation analysis

The network-based representation and analysis of biological systems contributes to a greater understanding of their structures and functions at different levels of complexity. These techniques can also be used to identify potential novel therapeutic targets based on the characterisation of vulnerable or highly influential network components. There is a need to investigate methods for estimating the impact of molecular perturbations. The prediction of high-impact or critical targets can aid in the identification of novel strategies for controlling the level of activation of specific, therapeutically relevant genes or proteins. Here, we report a new computational strategy for the analysis of the vulnerability of cellular signalling networks based on the quantitative assessment of the impact of large-scale, dynamic perturbations. To show the usefulness of this methodology, two complex signalling networks were analysed: the caspase-3 and the adenosine-regulated calcium signalling systems. This allowed us to estimate and rank the perturbation impact of the components defining these networks. Testable hypotheses about how these targets could modify the dynamic operation of the systems are provided. In the case of the caspase-3 system, the predictions and rankings were in line with results obtained from previous experimental validations of computational predictions generated by a relatively more computationally complex technique. In the case of the adenosine-regulated calcium system, we offer new testable predictions on the potential effect of different targets on the control of calcium flux. Unlike previous methods, the proposed approach provides perturbation-specific scores for each network component. The proposed perturbation assessment methodology may be applied to other systems to gain a deeper understanding of their dynamic operation and to assist the discovery of new therapeutic targets and strategies.     

Discovering functions and revealing mechanisms at molecular level from biological networks

With the increasingly accumulated data from high-throughput technologies, study on biomolecular networks has become one of key focuses in systems biology and bioinformatics. In particular, various types of molecular networks (e.g., protein-protein interaction (PPI) network; gene regulatory network (GRN); metabolic network (MN); gene coexpression network (GCEN)) have been extensively investigated, and those studies demonstrate great potentials to discover basic functions and to reveal essential mechanisms for various biological phenomena, by understanding biological systems not at individual component level but at a system-wide level. Recent studies on networks have created very prolific researches on many aspects of living organisms. In this paper, we aim to review the recent developments on topics related to molecular networks in a comprehensive manner, with the special emphasis on the computational aspect. The contents of the survey cover global topological properties and local structural characteristics, network motifs, network comparison and query, detection of functional modules and network motifs, function prediction from network analysis, inferring molecular networks from biological data as well as representative databases and software tools.     

Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition(i.e. multiomics data)and analysis(i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.     

Modularization of biochemical networks based on classification of Petri net t-invariants

Background  

Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.     

Competing to coordinate cell fate decisions: the MST2-Raf-1 signaling device

How do biochemical signaling pathways generate biological specificity? This question is fundamental to modern biology, and its enigma has been accentuated by the discovery that most proteins in signaling networks serve multifunctional roles. An answer to this question may lie in analyzing network properties rather than individual traits of proteins in order to elucidate design principles of biochemical networks that enable biological decision-making. We discuss how this is achieved in the MST2/Hippo-Raf-1 signaling network with the help of mathematical modeling and model-based analysis, which showed that competing protein interactions with affinities controlled by dynamic protein modifications can function as Boolean computing devices that determine cell fate decisions. In addition, we discuss areas of interest for future research and highlight how systems approaches would be of benefit.