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Open AccessJournal ArticleDOI

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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Detecting network communities: a new systematic and efficient algorithm

TL;DR: An efficient and relatively fast algorithm for the detection of communities in complex networks is introduced that exploits spectral properties of the graph Laplacian matrix combined with hierarchical clustering techniques, and includes a procedure for maximizing the 'modularity' of the output.
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Complex networks: Augmenting the framework for the study of complex systems

TL;DR: How complexity requires both new tools and an augmentation of the conceptual framework--including an expanded definition of what is meant by a “quantitative prediction” is discussed.
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Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways.

TL;DR: It is found that global network properties of the brain transcriptome are highly preserved between species, and all modules of highly coexpressed genes identified in mouse were identified in human, with those related to conserved cellular functions showing the strongest between-species preservation.
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Microbial ecology of expanding oxygen minimum zones

TL;DR: Current efforts to explore the fundamental factors that control the ecological and microbial biodiversity in oxygen-starved regions of the ocean, termed oxygen minimum zones are described and new insights into coupled biogeochemical processes in the ocean are provided.
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Assessing experimentally derived interactions in a small world

TL;DR: This work exploits the neighborhood cohesiveness property of small-world networks to assess confidence for individual protein–protein interactions and promises to improve the quality of inference from protein– protein interaction networks in particular and small- world networks in general.
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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