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Hierarchical Organization of Modularity in Metabolic Networks

TLDR
It is shown that the metabolic networks of 43 distinct organisms are organized into many small, highly connected topologic modules that combine in a hierarchical manner into larger, less cohesive units, with their number and degree of clustering following a power law.
Abstract
Spatially or chemically isolated functional modules composed of several cellular components and carrying discrete functions are considered fundamental building blocks of cellular organization, but their presence in highly integrated biochemical networks lacks quantitative support Here, we show that the metabolic networks of 43 distinct organisms are organized into many small, highly connected topologic modules that combine in a hierarchical manner into larger, less cohesive units, with their number and degree of clustering following a power law Within Escherichia coli, the uncovered hierarchical modularity closely overlaps with known metabolic functions The identified network architecture may be generic to system-level cellular organization

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The role of indirect connections in gene networks in predicting function

TL;DR: Co-expression networks are used to show that most exploited network structure simply reconstructs the original correlation matrices from which the co-expression network was obtained, and that these methods perform comparably to much more sophisticated gene function prediction algorithms.
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Insights into the organization of biochemical regulatory networks using graph theory analyses.

TL;DR: Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules, which shows that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks.
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Modular analysis of signal transduction networks

TL;DR: The mechanisms that cells have developed to process information are described, and the decomposition of signalling networks into subsystems is discussed, and some simple criteria for the analysis of the resulting units are introduced.
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Community detection in graphs using singular value decomposition.

TL;DR: A spectral algorithm for community detection that automatically reveals the number of clusters, their respective sizes and overlaps, and hierarchical modular organization on unipartite or bipartite unweighted or weighted networks is presented.
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Transcriptome analysis during seed germination of elite Chinese bread wheat cultivar Jimai 20

TL;DR: The first comprehensive dynamic transcriptome analysis during different seed germination stages of elite Chinese bread wheat cultivar Jimai 20 using the Affymetrix Wheat Genome Array is performed, providing new insight into metabolic changes during seeds germination and interactions between some significant genes.
References
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Journal ArticleDOI

Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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Emergence of Scaling in Random Networks

TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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Statistical mechanics of complex networks

TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
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Cluster analysis and display of genome-wide expression patterns

TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
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Community structure in social and biological networks

TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
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