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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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Parcellating connectivity in spatial maps.

TL;DR: The parcellation method is specifically designed to respect spatial layout and identify locally-connected clusters, corresponding to plausible coherent units such as strings of adjacent DNA base pairs, subregions of the brain, animal communities, or geographic ecosystems.
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Analytical solution of average path length for Apollonian networks

TL;DR: With the help of recursion relations derived from the self-similar structure, the rigorous solution shows that the average path length grows logarithmically as d[over ]_(t) proportional, variantln N_(T) in the infinite limit of network size N_( t) .
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Transcriptome analysis reveals key differentially expressed genes involved in wheat grain development

TL;DR: The authors performed dynamic transcriptome microarray analysis of an elite Chinese bread wheat cultivar (Jimai 20) during grain development using the GeneChip Wheat Genome Array and found that the period of 11 to 15 days post-anthesis (DPA) was a key stage for the synthesis and accumulation of seed starch.
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Community Structure, Dynamics and Interactions of Bacteria, Archaea and Fungi in Subtropical Coastal Wetland Sediments.

TL;DR: Redundancy analysis revealed temperature as the major driver of the seasonal patterns of bacterial and fungal communities but also highlighted the importance of interkingdom biotic factors in shaping the community structure of all three kingdoms, facilitating current understanding of the microbial ecology of subtropical coastal wetlands.
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Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding.

TL;DR: This analysis integrates gene co-expression network analysis, gene-trait correlations and differential expression to provide new candidate regulators of Salmonella shedding in pigs.
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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