基于子图模式的巨强连通体局域结构特征(英文)

对代谢网络的"蝴蝶结"结构进行研究吸引了众多科研人员的关注。当前,基于复杂网络理论的全局拓扑结构特征已经被广泛用于研究这种"蝴蝶结"结构的组织原则,然而基于局域结构特征的研究相对较少。本文分析了10种有机物代谢网络及其相应的巨强连通体部分的网络模体(即频繁的子图)。结果显示代谢网络及其相应的巨强连通体部分的网络模体非常相似,这从局域结构特征的角度论证了代谢网络巨强连通体部分的重要性。     

基于RAST的离子束重组菌DOB981的代谢网络研究

为进一步深入理解低能离子注入对DOB的生理代谢产生的生物学意义,本研究基于DOB全基因组De novo测序数据,应用生物信息学方法对离子束重组菌DOB981及其原始菌株进行基因组结构和功能注释,构建基因尺度的代谢网络,并用Cytoscape对其进行可视化分析。研究表明,离子束重组菌株DOB981的基因组大小比原始菌株减少了223 268 bp,ORF减少了204个,功能基因减少了136个,生化反应的数量减少了10个,而生物反应的反应底物比原始菌株增加了3个;离子束重组菌株DOB981独有19个生化反应,比原始菌株减少了10个。Cytoscape可视化分析表明,离子束重组菌DOB981代谢网络中包含1 604个节点和3 733条连线,虽然比原始菌株减少了1个节点,但连线却增加了68条。基因尺度代谢网络拓扑属性分析表明,离子束重组菌DOB981与原始菌株的代谢网络均为无标度网络,具有小世界网络(SWN)特性,但重组菌DOB981的代谢网络的特征路径长度大于原始菌株,其总体结构相对松散,且密度低。本研究不仅对DOB的环境适应性机制的研究具有重要意义,也为DOB基因组代谢网络模拟构建提供了理论基础。     

正交氧化还原体系及其应用

氧化还原反应是最常见的代谢反应类型之一,其中绝大部分通过辅因子依赖型氧化还原酶催化实现.由于辅因子广泛参与细胞内氧化还原反应及其他生物学过程,因代谢途径改造而扰动辅因子水平的生物学效应尚难以预测.设计构建基于人工辅因子的正交体系,是减少人工代谢途径与内源代谢网络相互干扰、降低系统复杂度、提高调控代谢网络有效性的新策略.本文探讨了正交氧化还原体系的构建方法,并结合实例说明其对提高能量传递特异性和人工代谢途径效率的重要意义.     

代谢流量比率分析及其在代谢工程中的应用

细胞内代谢反应流量在系统理解细胞代谢特性和指导代谢工程改造等方面都起着重要的作用。由于代谢流量难以直接测量得到,在很多情况下通过跟踪稳定同位素在代谢网络中的转移并进行相应的模型计算能有效地定量代谢流量。代谢流量比率分析法能够高度体现系统的生物化学真实性、辨别细胞代谢网络的拓扑结构,并且能够相对简单快速地定量反应速率等,因此受到代谢工程研究者越来越多的重视。以下着重介绍并讨论了利用代谢物同位体分布信息分析关键代谢节点合成途径的流量比率、基于流量比率的代谢流量解析、以及应用于代谢工程等的相关原理、实验测量、数据分析、使用条件等,以期充分发挥代谢流量比率分析法的优势,并将其拓展推广至更多细胞体系的代谢特性阐明和代谢工程改造中去。     

细胞粘附分子群相互作用网络模型研究

通过相互作用网络可视化软件Cytoscape对整联蛋白粘合体156种成份及相互间的690个反应实现网络可视化,并通过网络分析软件network analyser得到该网络节点间的平均连接并不复杂是一个不可分割的整体网络,网络较稳定,信息传递很快等。直接可视化的网络通过节点的形状改变视图有助于我们在原始数据的基础上更好的认识这一复杂的作用网络的相关信息。     

一种基于代谢网络分析最小化基因组的方法及其在大肠杆菌中的应用

最小生命体的合成是合成生物学研究的重要方向。最小化基因组的同时而又不对细胞生长产生影响是代谢工程研究的一个重要目标。文中提出了一种从基因组尺度代谢网络模型出发,通过零通量反应删除及对非必需基因组合删除计算获得基因组最小化代谢网络模型的方法,利用该方法简化了大肠杆菌经典代谢网络模型iAF1260,由起始的1 260个基因简化得到了312个基因,而最优生物质生成速率保持不变。基因组最小化代谢网络模型预测了在细胞正常生长的前提下包含最少基因的代谢途径,为大肠杆菌获得最小基因组的湿实验设计提供了重要参考。     

城市碳代谢过程研究进展

碳代谢过程分析是城市代谢研究的重要环节,而通过土地利用/覆盖的空间调整优化城市碳代谢过程已成为区域可持续发展的关键。利用城市代谢思想,本文综述了城市碳代谢过程核算、碳代谢网络模拟、碳代谢过程与土地利用/覆盖变化关系分析、碳代谢空间格局演替等方面的内容,并指出了当前研究中存在着空间属性表达缺乏、核算/模拟结果较难直接应用于实践调控、自然和社会经济代谢过程难以并重考虑等问题。在此基础上,提出了此领域未来发展预期:(1)基于土地流转,将碳排放/碳吸收垂向流映射到碳存量变化的水平流,以"存量"变化推导出网络"流量"分布,实现节点、流互动关系的空间表达,构建时空维度碳代谢网络模型;(2)强调自然节点在城市碳代谢网络中的重要作用,形成社会经济节点与自然节点并重的生态网络模型,有效服务于城市规划及设计。     

联合TCGA和GEO数据库构建和分析胃癌中circRNA相关的ceRNA网络

环状RNA (circular RNA, circRNA)是一类呈闭环状结构的竞争性内源RNA (competing endogenous RNA, ceRNA),其通过竞争性地吸附微RNA (microRNA, mi RNA)来调节基因表达。circRNA失调与癌细胞的增殖、分化和侵袭等密切相关。本研究基于生物信息学分析和多数据库的联合应用,以ceRNA为切入点探析胃癌中circRNA潜在的致病机制,以期为胃癌提供潜在的临床诊疗靶点,并为胃癌的科学研究提供新思路。首先,通过挖掘胃癌差异表达的circRNA、mi RNA和m RNA构建circRNA-miRNA-mRNA ceRNA网络;随后,利用网络拓扑属性分析确定核心的节点,并根据这些核心的节点从原始的ceRNA网络中提取子网;最后,对子网进行功能学分析和生存分析,分析网络行使的生物学功能并挖掘预后相关的基因。结果显示:共挖掘了6个核心节点(hsa_circ_0008468、hsa_circ_0005822、hsa_circ_0025842、hsa-miR-940、hsa-miR-944及hsa-miR-515-5p);从原始的ceRNA网络中提取了包含8对circRNA-miRNA和539个mi RNA-m RNA关系对的子网络。功能富集结果表明子网涉及癌症相关的多个生物学过程,包括代谢途径、cAMP信号通路、pathways in cancer信号通路等,生存分析发现子网中ACO2、E2F8、GHR、ITIH5等14基因与预后显著相关,这表明3个核心circRNA介导的ceRNA网络与胃癌的发生发展及预后密切相关。     

肌苷发酵过程中后期的代谢流节点分析

应用代谢流分析方法,实验测定副产物的积累速率,将数据输入计算机,应用MArllAB软件计算肌苷发酵中后期代谢流分布。通过分析代谢网络中重要的节点,提出了优化肌苷生物合成途径的建议。     

全基因组范围代谢网络的构建和最小基因组研究

全基因组范围代谢网络（genome-scale metabolic network,GSMN）的构建是合成生物学研究的一个重要研究手段。通过整合各种组学数据和借助计算机进行模拟分析,将基因型与表型的关系进行定量关联,从而为从全局的角度探索和揭示生物代谢机制,进而对生物进行合理的重新设计和工程改造提供了有效的框架。该方法在最小基因组研究中也有着突出的优势,通过计算机辅助的基因组最小化模拟与分析,能够系统鉴定微生物基因组基因的必需性。迄今为止,已有近百个基因组范围的代谢网络发表,覆盖的生物包括原核生物、真核生物和古生生物,并广泛应用于医药、能源、环境、工业和农业等多个领域,展现出了广阔的应用前景。将对全基因组范围代谢网络构建的方法、应用,特别是其在最小基因组研究中的应用作简要的综述。     

Structural analysis of metabolic networks based on flux centrality

Metabolic reactions are fundamental to living organisms, and a large number of reactions simultaneously occur at a given time in living cells transforming diverse metabolites into each other. There has been an ongoing debate on how to classify metabolites with respect to their importance for metabolic performance, usually based on the analysis of topological properties of genome scale metabolic networks. However, none of these studies have accounted quantitatively for flux in metabolic networks, thus lacking an important component of a cell’s biochemistry.We therefore analyzed a genome scale metabolic network of Escherichia coli by comparing growth under 19 different growth conditions, using flux balance analysis and weighted network centrality investigation. With this novel concept of flux centrality we generated metabolite rankings for each particular growth condition. In contrast to the results of conventional analysis of genome scale metabolic networks, different metabolites were top-ranking dependent on the growth condition. At the same time, several metabolites were consistently among the high ranking ones. Those are associated with pathways that have been described by biochemists as the most central part of metabolism, such as glycolysis, tricarboxylic acid cycle and pentose phosphate pathway. The values for the average path length of the analyzed metabolite networks were between 10.5 and 12.6, supporting recent findings that the metabolic network of E. coli is not a small-world network.     

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph

MOTIVATION: Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. RESULTS: In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level. SUPPLEMENTARY INFORMATION: http://genome.gbf.de/bioinformatics/     

The connectivity structure,giant strong component and centrality of metabolic networks

MOTIVATION: Structural and functional analysis of genome-based large-scale metabolic networks is important for understanding the design principles and regulation of the metabolism at a system level. The metabolic network is conventionally considered to be highly integrated and very complex. A rational reduction of the metabolic network to its core structure and a deeper understanding of its functional modules are important. RESULTS: In this work, we show that the metabolites in a metabolic network are far from fully connected. A connectivity structure consisting of four major subsets of metabolites and reactions, i.e. a fully connected sub-network, a substrate subset, a product subset and an isolated subset is found to exist in metabolic networks of 65 fully sequenced organisms. The largest fully connected part of a metabolic network, called 'the giant strong component (GSC)', represents the most complicated part and the core of the network and has the feature of scale-free networks. The average path length of the whole network is primarily determined by that of the GSC. For most of the organisms, GSC normally contains less than one-third of the nodes of the network. This connectivity structure is very similar to the 'bow-tie' structure of World Wide Web. Our results indicate that the bow-tie structure may be common for large-scale directed networks. More importantly, the uncovered structure feature makes a structural and functional analysis of large-scale metabolic network more amenable. As shown in this work, comparing the closeness centrality of the nodes in the GSC can identify the most central metabolites of a metabolic network. To quantitatively characterize the overall connection structure of the GSC we introduced the term 'overall closeness centralization index (OCCI)'. OCCI correlates well with the average path length of the GSC and is a useful parameter for a system-level comparison of metabolic networks of different organisms. SUPPLEMENTARY INFORMATION: http://genome.gbf.de/bioinformatics/     

Drug-target network

The global set of relationships between protein targets of all drugs and all disease-gene products in the human protein-protein interaction or 'interactome' network remains uncharacterized. We built a bipartite graph composed of US Food and Drug Administration-approved drugs and proteins linked by drug-target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to Anatomical Therapeutic Chemical classification. Topological analyses of this network quantitatively showed an overabundance of 'follow-on' drugs, that is, drugs that target already targeted proteins. By including drugs currently under investigation, we identified a trend toward more functionally diverse targets improving polypharmacology. To analyze the relationships between drug targets and disease-gene products, we measured the shortest distance between both sets of proteins in current models of the human interactome network. Significant differences in distance were found between etiological and palliative drugs. A recent trend toward more rational drug design was observed.     

Physiologically Shrinking the Solution Space of a Saccharomyces cerevisiae Genome-Scale Model Suggests the Role of the Metabolic Network in Shaping Gene Expression Noise

Sampling the solution space of genome-scale models is generally conducted to determine the feasible region for metabolic flux distribution. Because the region for actual metabolic states resides only in a small fraction of the entire space, it is necessary to shrink the solution space to improve the predictive power of a model. A common strategy is to constrain models by integrating extra datasets such as high-throughput datasets and C¹³-labeled flux datasets. However, studies refining these approaches by performing a meta-analysis of massive experimental metabolic flux measurements, which are closely linked to cellular phenotypes, are limited. In the present study, experimentally identified metabolic flux data from 96 published reports were systematically reviewed. Several strong associations among metabolic flux phenotypes were observed. These phenotype-phenotype associations at the flux level were quantified and integrated into a Saccharomyces cerevisiae genome-scale model as extra physiological constraints. By sampling the shrunken solution space of the model, the metabolic flux fluctuation level, which is an intrinsic trait of metabolic reactions determined by the network, was estimated and utilized to explore its relationship to gene expression noise. Although no correlation was observed in all enzyme-coding genes, a relationship between metabolic flux fluctuation and expression noise of genes associated with enzyme-dosage sensitive reactions was detected, suggesting that the metabolic network plays a role in shaping gene expression noise. Such correlation was mainly attributed to the genes corresponding to non-essential reactions, rather than essential ones. This was at least partially, due to regulations underlying the flux phenotype-phenotype associations. Altogether, this study proposes a new approach in shrinking the solution space of a genome-scale model, of which sampling provides new insights into gene expression noise.     

Fast Reconstruction of Compact Context-Specific Metabolic Network Models

Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.     

Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks

Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable.