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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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Citations
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On the genre-fication of music: a percolation approach

TL;DR: An alternative objective way to classify music, that is based on the complex structure of the groups audience is discussed, and a link is drawn with the theory of hidden variables in complex networks.
Journal ArticleDOI

Gene Bionetwork Analysis of Ovarian Primordial Follicle Development

TL;DR: The relevant gene network associated with primordial follicle development was validated and the critical genes and pathways involved in this process were identified, one of the first applications of network analysis to a normal developmental process.
Journal ArticleDOI

Dynamic proteomics in modeling of the living cell. Protein-protein interactions.

TL;DR: It is proposed that finding of some proteins in different protein complexes can be explained by their multi-modular and polyfunctional properties; the different protein modules can be located in the nodes of protein interaction networks.
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A differential network approach to exploring differences between biological states: an application to prediabetes.

TL;DR: By performing the differential analysis of networks, several characteristic changes in lipoprotein metabolism known to be related to diabetic dyslipidemias were identified and demonstrate the applicability of the new approach to identify key molecular changes inaccessible to standard approaches.
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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