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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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Annotating gene function by combining expression data with a modular gene network

TL;DR: This work presents a systematic method for gene clustering by combining totally different two types of data, particularly focusing on network modularity, a global feature of gene networks, which outperformed other four competing methods, including k-means and existing graph partitioning methods.
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A link prediction algorithm based on ant colony optimization

TL;DR: This paper proposes a link prediction algorithm based on ant colony optimization that employs artificial ants to travel on a logical graph and extends the method to solve the link prediction problem in networks with node attributes, and the method also can detect the missing or incomplete attributes of data.
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A protein network descriptor server and its use in studying protein, disease, metabolic and drug targeted networks

TL;DR: The usefulness of the PROFEAT computed network descriptors is illustrated by their literature-reported applications in studying the protein–protein, gene regulatory, gene co-expression, protein–drug and metabolic networks.
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A community integration strategy based on an improved modularity density increment for large-scale networks

TL;DR: This paper presents a community integration strategy for large-scale networks, based on pre-partitioning, followed by optimization of an improved modularity density increment ΔD, and shows how to use this as an objective function during the community integration optimization process.
Journal ArticleDOI

Comparative transcriptome profiling of longissimus muscle tissues from Qianhua Mutton Merino and Small Tail Han sheep.

TL;DR: The results suggested that some DEGs, including MRFs, GXP1 and STAC3, play crucial roles in muscle growth and development processes, and genome-wide transcriptome analysis of QHMM and STH muscle is reported for the first time.
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.
Journal ArticleDOI

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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