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

Ma, Hongwu; Zhao, Xiao‐Fan; Yang, Yuan; Zeng, An‐Ping

doi:10.1093/bioinformatics/bth167

Cited by 137 publications

(127 citation statements)

References 27 publications

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“…3d), where the latter two modifications, when iterated, lead to positive and negative degree correlations, respectively [see 23, for an explanation of the corresponding algorithms]. In order to retain the modular structure of the network we identify modules with a path length algorithm similar to the one described in [38] and randomize solely links within or between single modules. Both modifications reveal an average entropy increase compared to the original metabolic networks (Fig.…”

Section: Resultsmentioning

confidence: 99%

Regularizing capacity of metabolic networks

2007

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Despite their topological complexity almost all functional properties of metabolic networks can be derived from steady-state dynamics. Indeed, many theoretical investigations (like flux-balance analysis) rely on extracting function from steady states. This leads to the interesting question, how metabolic networks avoid complex dynamics and maintain a steady-state behavior. Here, we expose metabolic network topologies to binary dynamics generated by simple local rules. We find that the networks' response is highly specific: Complex dynamics are systematically reduced on metabolic networks compared to randomized networks with identical degree sequences. Already small topological modifications substantially enhance the capacity of a network to host complex dynamic behavior and thus reduce its regularizing potential. This exceptionally pronounced regularization of dynamics encoded in the topology may explain, why steady-state behavior is ubiquitous in metabolism.

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Section: Resultsmentioning

confidence: 99%

Regularizing capacity of metabolic networks

2007

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“…Understanding such structure feature may help to design more efficient network algorithms specifically for metabolic networks. For example, when designing an algorithm to decompose the metabolic network into modules [34] , we decomposed the GSC first and then expand the partition of GSC to other parts by a "majority rule" [24] . The sub-networks got from this algorithm exhibit a highly modularized bow-tie topological pattern similar to that of the global metabolic networks, while these small bow-ties are hierarchically nested into larger ones and collectively integrated into a large metabolic network.…”

Section: Spread Bow-tie Of Ecoli Metabolic Networkmentioning

confidence: 99%

“…They concluded that large-scale organizational frameworks such as the bow-tie are necessary starting points for higher-resolution modeling of complex biologic processes [23] . As the bow-tie structure of networks has drawn so much attention [15,[23][24][25][26] , clear visualization method of this architecture is being expected because of the large number of nodes and arcs, while GSC, the central part of bow-tie, deserves more detailed exploration. The decomposition of a network into k-cores [27] allows us to classify nodes simultaneously by both their connectivity and central placement in the network.…”

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confidence: 99%

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

Zhao

Lin

et al. 2007

Chin. Sci. Bull.

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Exploring the structural topology of genome-based large-scale metabolic network is essential for investigating 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.Keywords: bioinformatics; metabolic network; bow-tie; random network.As the results of various genome projects, the genome sequences of many organisms are available and the organism specific metabolic networks can be faithfully reconstructed from genome information [1][2][3][4][5][6] . Thus the prediction of function from the metabolic networks has become an essential step in the post-genomic era [4,[7][8][9][10][11] . However, before it is possible to investigate the potential relationship between structure and functionality of a metabolic network, it is necessary to study how the metabolic networks are actually constructed.In recent years there has been a strongly increasing passion in investigating the intricate structures of link topologies in metabolic networks [12][13][14][15][16][17][18][19][20][21] . Many researchers have applied methods from graph theory to treat the metabolic networks as a directed graph consisting of nodes denoting metabolites connected by directed arcs representing reactions. An important finding is that metabolic networks, as well as other real-world complex networks, have topologies that differ markedly from those found in simple randomly connected networks [22] , which suggests that their non-random structures could imply significant organizing principles of metabolic networks.From the computational analysis of genome-based metabolic networks of 65 organisms, Ma and Zeng discovered that the global metabolic network is organized in the form of a bow-tie [15] . On the other hand, from the view of material flows and information flows in the metabolic system, Csete and Doyle described metabolism as several nested bow-ties [23] . It was pointed out that bow-tie architectures facilitate robust biologic function, and based on their design, also have

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“…Reaction graph: The nodes correspond to reactions. There is an edge between two reactions if a compound is both a product of one reaction and a substrate of the other one [14,19] . 3.…”

Section: Reconstruction Of Metabolic Network From Genome Informationmentioning

confidence: 99%

Complex networks theory for analyzing metabolic networks

Zhao

Luo

et al. 2006

CHINESE SCI BULL

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One of the main tasks of post-genomic informatics is to systematically investigate all molecules and their interactions within a living cell so as to understand how these molecules and the interactions between them relate to the function of the organism, while networks are appropriate abstract description of all kinds of interactions. In the past few years, great achievement has been made in developing theory of complex networks for revealing the organizing principles that govern the formation and evolution of various complex biological, technological and social networks. This paper reviews the accomplishments in constructing genome-based metabolic networks and describes how the theory of complex networks is applied to analyze metabolic networks. Keywords: bioinformatics, systems biology, metabolic network, network topology, network decomposition, network robustness.The completion of the Human Genome Project started the post-genomic era. The hot topic of biological research is now shifting from the study of single genes or proteins to whole genome analyses. All kinds of "omics" technologies such as genomics, transcriptomics, proteomics and metabonomics gradually bring molecular biology into the era of system biology, and bioinformatics into that of post-genome informatics. Since genes and proteins tend to function through interacting in networks, the interaction networks must be analyzed for the purpose of studying biological functions. It is very important to analyze the biological functions in terms of the network of interacting molecules and genes, such as genetic regulatory network, signal tranduction network, protein interaction network, and metabolic network, with the aim of understanding how a biological system is organized from its individual building blocks. It represents an integration of biological knowledge from genomic information towards the understanding of the basic principles of life for biomedical applications [1] . Metabolic process is essential for the maintenance of life. In metabolism some materials are broken down to yield energy for vital processes while other substances, necessary for life, are synthesized. The metabolic network, a characteristic complex network including all metabolites and enzyme catalyzed reactions occurring within a living cell, as well as the interactions between the reactants and enzymes, is an abstract representation of cellular metabolism. The analysis of metabolic networks can help to understand and utilize cellular metabolic process in order to * Corresponding authors:.

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

Abstract: http://genome.gbf.de/bioinformatics/

Cited by 137 publications

References 27 publications

Regularizing capacity of metabolic networks

Regularizing capacity of metabolic networks

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

Complex networks theory for analyzing metabolic networks

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