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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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Using graph theory to analyze biological networks

TL;DR: Att approaches, models and methods from the graph theory universe are demonstrated and ways in which they can be used to reveal hidden properties and features of a network are discussed to better understand the biological significance of the system.
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Extracting the hierarchical organization of complex systems.

TL;DR: An unsupervised method for extracting the hierarchical organization of complex biological, social, and technological networks is introduced and validated and an ensemble of hierarchically nested random graphs is defined, which is used to validate the method.

私の computer 環境

秀逸 原田
TL;DR: The longman elect new senior secondary theme book is a brand new task-based coursebook specially designed to meet the aims of the new high school curriculum for secondary 4 to 6 building on the solid foundation of knowledge skills values and attitudes laid down in the widely successful Longman elect junior secondary series as discussed by the authors.
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The structure of the protein universe and genome evolution

TL;DR: These findings suggest that genome evolution is driven by extremely general mechanisms based on the preferential attachment principle, and that protein folds and families encoded in diverse genomes show similar size distributions with notable mathematical properties.
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Popularity versus similarity in growing networks

TL;DR: It is shown that popularity is just one dimension of attractiveness; another dimension is similarity, and a framework in which new connections optimize certain trade-offs between popularity and similarity is developed, instead of simply preferring popular nodes.
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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