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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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Weighted assortative and disassortative networks model

TL;DR: By studying the weighted assortativity coefficient, it is found that both topologically assortative and disassortative networks generated by the model are in fact weightedAssortative.
Journal ArticleDOI

The origin and evolution of modern metabolism.

TL;DR: How structural and functional genomics has impacted the authors' knowledge of evolution of modern metabolism is reviewed and some approaches that merge evolutionary and structural genomics with advances in bioinformatics are described.
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The interaction networks of structured RNAs

TL;DR: All pairwise interactions occurring between bases which could be detected in three-dimensional structures of crystallized RNA molecules are annotated on new planar diagrams, revealing structural similarities as well as the conserved patterns and distances between motifs which are present within the interaction networks of folded RNAs of similar or unrelated functions.
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Social inheritance can explain the structure of animal social networks

TL;DR: This model considers the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly, and shows that it can generate networks with properties such as those observed in real social systems.
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The Emergence of Modularity in Biological Systems

TL;DR: This review discusses modularity and hierarchy in biological systems and reviews theories to explain modular organization of biology, with a focus on explaining how biology may spontaneously organize to a structured form.
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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