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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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Journal ArticleDOI

Disease-Aging Network Reveals Significant Roles of Aging Genes in Connecting Genetic Diseases

TL;DR: A network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases.
BookDOI

Molecular aspects of the stress response : chaperones, membranes and networks

TL;DR: Protein Misassembly, Cellular "Networking" of Mammalian Hsp27, and Trehalose As a "Chemical Chaperone" - Chaperones As Part of Immune Networks.
Journal ArticleDOI

Expression profile analysis of long noncoding RNA in HER-2-enriched subtype breast cancer by next-generation sequencing and bioinformatics.

TL;DR: The functions of LncRNAs are analyzed and provide useful information for exploring candidate therapeutic targets and new molecular biomarkers for HER-2-enriched subtype breast cancer.
Book ChapterDOI

Analyzing terrorist networks: a case study of the global salafi jihad network

TL;DR: This study employed several advance network analysis techniques ranging from social network analysis to Web structural mining on a Global Salafi Jihad network dataset collected through a large scale empirical study and demonstrated the effectiveness and usefulness of advanced network techniques in terrorist network analysis domain.
Journal ArticleDOI

Directed clustering in weighted networks: A new perspective

TL;DR: A new local clustering coefficient for this kind of networks is provided, starting from those already existing in the literature for the weighted and undirected case, and extracting from this coefficient four specific components, in order to separately consider different link patterns of triangles.
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.
Journal ArticleDOI

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.
Journal ArticleDOI

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.
Journal ArticleDOI

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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