Bow-tie topological features of metabolic networks and the functional significance

Exploring the structural topology of genome-based large-scale metabolic network is essential for in- vestigating possible relations between structure and functionality.Visualization would be helpful for obtaining immediate information about structural organization.In this work,metabolic networks of 75 organisms were investigated from a topological point of view.A spread bow-tie model was proposed to give a clear visualization of the bow-tie structure for metabolic networks.The revealed topological pattern helps to design more efficient algorithm specifically for metabolic networks.This coarse- grained graph also visualizes the vulnerable connections in the network,and thus could have important implication for disease studies and drug target identifications.In addition,analysis on the reciprocal links and main cores in the GSC part of bow-tie also reveals that the bow-tie structure of metabolic networks has its own intrinsic and significant features which are significantly different from those of random networks.     

Error and attack tolerance of complex networks

Many complex systems display a surprising degree of tolerance against errors. For example, relatively simple organisms grow, persist and reproduce despite drastic pharmaceutical or environmental interventions, an error tolerance attributed to the robustness of the underlying metabolic network. Complex communication networks display a surprising degree of robustness: although key components regularly malfunction, local failures rarely lead to the loss of the global information-carrying ability of the network. The stability of these and other complex systems is often attributed to the redundant wiring of the functional web defined by the systems' components. Here we demonstrate that error tolerance is not shared by all redundant systems: it is displayed only by a class of inhomogeneously wired networks, called scale-free networks, which include the World-Wide Web, the Internet, social networks and cells. We find that such networks display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected even by unrealistically high failure rates. However, error tolerance comes at a high price in that these networks are extremely vulnerable to attacks (that is, to the selection and removal of a few nodes that play a vital role in maintaining the network's connectivity). Such error tolerance and attack vulnerability are generic properties of communication networks.     

Global organization of metabolic fluxes in the bacterium Escherichia coli

Cellular metabolism, the integrated interconversion of thousands of metabolic substrates through enzyme-catalysed biochemical reactions, is the most investigated complex intracellular web of molecular interactions. Although the topological organization of individual reactions into metabolic networks is well understood, the principles that govern their global functional use under different growth conditions raise many unanswered questions. By implementing a flux balance analysis of the metabolism of Escherichia coli strain MG1655, here we show that network use is highly uneven. Whereas most metabolic reactions have low fluxes, the overall activity of the metabolism is dominated by several reactions with very high fluxes. E. coli responds to changes in growth conditions by reorganizing the rates of selected fluxes predominantly within this high-flux backbone. This behaviour probably represents a universal feature of metabolic activity in all cells, with potential implications for metabolic engineering.     

Complex networks theory for analyzing metabolic networks

The completion of the Human Genome Project started the post-genomic era. The hot topic of biologi- cal research is now shifting from the study of single genes or proteins to whole genome analyses. All kinds of “omics” technologies such as genomics, tran…     

Local interactions predict large-scale pattern in empirically derived cellular automata.

An important unanswered question in ecology is whether processes such as species interactions that occur at a local scale can generate large-scale patterns seen in nature. Because of the complexity of natural ecosystems, developing an adequate theoretical framework to scale up local processes has been challenging. Models of complex systems can produce a wide array of outcomes; therefore, model parameter values must be constrained by empirical information to usefully narrow the range of predicted behaviour. Under some conditions, spatially explicit models of locally interacting objects (for example, cells, sand grains, car drivers, or organisms), variously termed cellular automata or interacting particle models, can self-organize to develop complex spatial and temporal patterning at larger scales in the absence of any externally imposed pattern. When these models are based on transition probabilities of moving between ecological states at a local level, relatively complex versions of these models can be linked readily to empirical information on ecosystem dynamics. Here, I show that an empirically derived cellular automaton model of a rocky intertidal mussel bed based on local interactions correctly predicts large-scale spatial patterns observed in nature.     

Functional cartography of complex metabolic networks

High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that we can find functional modules in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a 'cartographic representation' of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability. We use our method to analyse the metabolic networks of twelve organisms from three different superkingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module.     

A systems biology analysis of the Drosophila phagosome

Phagocytes have a critical function in remodelling tissues during embryogenesis and thereafter are central effectors of immune defence. During phagocytosis, particles are internalized into 'phagosomes', organelles from which immune processes such as microbial destruction and antigen presentation are initiated. Certain pathogens have evolved mechanisms to evade the immune system and persist undetected within phagocytes, and it is therefore evident that a detailed knowledge of this process is essential to an understanding of many aspects of innate and adaptive immunity. However, despite the crucial role of phagosomes in immunity, their components and organization are not fully defined. Here we present a systems biology analysis of phagosomes isolated from cells derived from the genetically tractable model organism Drosophila melanogaster and address the complex dynamic interactions between proteins within this organelle and their involvement in particle engulfment. Proteomic analysis identified 617 proteins potentially associated with Drosophila phagosomes; these were organized by protein-protein interactions to generate the 'phagosome interactome', a detailed protein-protein interaction network of this subcellular compartment. These networks predicted both the architecture of the phagosome and putative biomodules. The contribution of each protein and complex to bacterial internalization was tested by RNA-mediated interference and identified known components of the phagocytic machinery. In addition, the prediction and validation of regulators of phagocytosis such as the 'exocyst', a macromolecular complex required for exocytosis but not previously implicated in phagocytosis, validates this strategy. In generating this 'systems-based model', we show the power of applying this approach to the study of complex cellular processes and organelles and expect that this detailed model of the phagosome will provide a new framework for studying host-pathogen interactions and innate immunity.     

Analysis of correlations between protein complex and protein-protein interaction and mRNA expression

Protein-protein interaction is a physical interaction of two proteins in living cells. In budding yeast Saccharomyces cerevisiae, large-seale protein-protein interaction data have been obtained through high-throughput yeast two-hybrid systems (Y2H) and protein complex purification techniques based on mass-spectrometry. Here, we collect 11855 interactions between total 2617 proteins. Through seriate genome-wide mRNA expression data, similarity between two genes could be measured. Protein complex data can also be obtained publicly and can be translated to pair relationship that any two proteins can only exist in the same complex or not. Analysis of protein complex data, protein-protein interaction data and mRNA expression data can elucidate correlations between them. The results show that proteins that have interactions or similar expression patterns have a higher possibility to be in the same protein complex than randomized selected proteins, and proteins which have interactions and similar expression patterns are even more possible to exist in the same protein complex. The work indirates that comprehensive integration and analysis of public large-seale bioinformatical data, such as protein complex data, protein-protein interaction data and mRNA expression data, may help to uncover their relationships and common biological information underlying these data. The strategies described here may help to integrate and analyze other functional genomic and proteomic data, such as gene expression profiling, protein-localization mapping and large-scale phenotypic data, both in yeast and in other organisms.     

Relationship between topology and functions in metabolic network evolution

What is the relationship between the topological connections among enzymes and their functions during metabolic network evolution? Does this relationship show similarity among closely related organisms? Here we investigated the relationship between enzyme connectivity and functions in metabolic networks of chloroplast and its endosymbiotic ancestor, cyanobacteria (Synechococcus sp. WH8102). Also several other species, including E. coli, Arabidopsis thaliana and Cyanidioschyzon merolae, were used for the comparison. We found that the average connectivity among different functional pathways and enzyme classifications (EC) was different in all the species examined. However, the average connectivity of enzymes in the same functional classification was quite similar between chloroplast and one representative of cyanobacteria, syw. In addition, the enzymes in the highly conserved modules between chloroplast and syw, such as amino acid metabolism, were highly connected compared with other modules. We also discovered that the isozymes of chloroplast and syw often had higher connectivity, corresponded to primary metabolism and also existed in conserved module. In conclusion, despite the drastic re-organization of metabolism in chloroplast during endosymbiosis, the relationship between network topology and functions is very similar between chloroplast and its precursor cyanobacteria, which demonstrates that the relationship may be used as an indicator of the closeness in evolution.     

Complex Network Classification with Convolutional Neural Network

Classifying large-scale networks into several categories and distinguishing them according to their fine structures is of great importance to several real-life applications.However, most studies on complex networks focus on the properties of a single network and seldom on classification, clustering, and comparison between different networks, in which the network is treated as a whole.Conventional methods can hardly be applied on networks directly due to the non-Euclidean properties of data.In this paper, we propose a novel framework of Complex Network Classifier(CNC) by integrating network embedding and convolutional neural network to tackle the problem of network classification.By training the classifier on synthetic complex network data, we show CNC can not only classify networks with high accuracy and robustness but can also extract the features of the networks automatically.We also compare our CNC with baseline methods on benchmark datasets, which shows that our method performs well on large-scale networks.     

Network Visualization in Cell Biology

Many of the processes known to take place in biological cells are analyzed in the form of different types of network.The complexity of these networks increases along with our knowledge of these processes,making their analysis more difficult.Network visualization is a powerful analysis method that will have to be developed further to deal with this complexity.This survey provides a brief overview of network visualization in general,followed by an in-depth discussion of its application to three network types specific to cell biology,namely gene regulatory,protein interaction,and metabolic networks.Finally,we discuss the difficulty of visually integrating these network types and trying to compare networks of cells that belong to different organisms.     

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth

Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.     

Uncovering the overlapping community structure of complex networks in nature and society

Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins, industrial sectors and groups of people) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.     

融合节点影响力与多标签传播的重叠社区发现算法

重叠社区发现是复杂网络分析研究的重要目标之一。针对传统多标签传播算法存在的社区发现结果具有随机性、不稳定性,以及忽视节点影响力对标签传播的影响等问题,提出一种基于节点影响力与多标签传播的能够生成稳定社区的重叠社区发现算法。算法在节点影响力的计算、排序和核心节点识别基础上,通过邻居节点初始标签的再处理和基于平衡系数的节点标签异步更新策略,实现复杂网络重叠社区的有效识别。在真实数据集和人工数据集上的实验综合表明,算法性能优于各对比算法,适用于大规模复杂网络。     

Hydrolytic stability of biomolecules at high temperatures and its implication for life at 250 degrees C

The upper temperature at which a living system can exist is limited by the hydrolytic breakdown rate of its chemical constituents. The peptide bonds of proteins, the phosphodiester and N-glycosyl bonds in RNA and DNA, and the pyrophosphate and N-glycosyl bonds in nucleotides such as ATP and NAD are among the more important bonds that will undergo hydrolysis. The decomposition of biomolecules via non-hydrolytic pathways such as decarboxylations and dehydrations may also be critical factors in determining this upper temperature limit. Baross and Deming recently reported 'black smoker' bacteria, which they isolated from deep-sea hydrothermal vents, growing at 250 degrees C. Here I have attempted to establish the rates for the hydrolysis and/or decomposition of critical biomolecules to determine their ability to exist at this temperature. My results clearly indicate that if these organisms exist, and if their metabolic reactions occur in an aqueous environment, they could not survive at this temperature if they were composed of biomolecules such as proteins and nucleic acids, due to the very rapid rate of decomposition of such molecules.