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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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Efficient estimation of graphlet frequency distributions in protein--protein interaction networks

TL;DR: It is shown that both PPI and their model geometric random networks, have defined boundaries that are sparser than the 'inner parts' of the networks, and these networks exhibit 'uniformity' of local structure inside the networks.
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Revealing static and dynamic modular architecture of the eukaryotic protein interaction network

TL;DR: It is found that constitutively expressed and dynamically co‐regulated proteins cluster in distinct functionally specialized network neighborhoods to form static and dynamic functional modules, respectively, and it is shown that whereas dynamic modules are mainly responsible for condition‐dependent regulation of cell behavior, static modules provide robustness to the cell against genetic perturbations or protein expression noise.
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Hubs in biological interaction networks exhibit low changes in expression in experimental asthma.

TL;DR: This analysis suggests that a combination of differential gene expression plus topological characteristics of the interaction network provides enhanced understanding of the biology in the authors' model of experimental asthma.
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Hierarchical modularity of nested bow-ties in metabolic networks.

TL;DR: In this paper, the structural topology and organizing principles of genome-based large-scale metabolic networks are investigated from a topological point of view, showing that almost all of the sub-networks exhibit a highly modularized bow-tie topological pattern similar to that of the global metabolic networks.
Journal ArticleDOI

Visibility graph approach to exchange rate series

TL;DR: In this paper, a visibility graph is used to obtain reliable values of the Hurst exponents of the series of the exchange rate of EURO to Japanese Yen, and it is found that the series convert into scale-free and hierarchically structured networks.
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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