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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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Mapping determinants of variation in energy metabolism, respiration and flight in Drosophila.

TL;DR: In this article, the authors employed quantitative trait locus (QTL) mapping to dissect the genetic architecture of a hierarchy of functionally related physiological traits, including metabolic enzyme activity, metabolite storage, metabolic rate, and free-flight performance in recombinant inbred lines of Drosophila melanogaster.
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The road to non-enzymatic molecular networks.

TL;DR: This Minireview gives an overview of recent progress in the design and analysis of chemical systems that utilize template-directed autocatalytic and cross-catalytic processes as a means of wiring dynamically interacting molecules.
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VisANT: an integrative framework for networks in systems biology

TL;DR: VisANT is a visual mining software which integrates, mines and displays hierarchical information to capture functional hierarchies and adaptation to environmental change and to discover pathways and processes embedded in known data, but not currently recognizable.
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Semi-supervised clustering algorithm for community structure detection in complex networks

TL;DR: An equivalence of the objective functions of the symmetric nonnegative matrix factorization (SNMF) and the maximum optimization of modularity density is discussed and a new algorithm, named the so-called SNMF-SS, is developed by combining SNMF and a semi-supervised clustering approach.
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Measure the structure similarity of nodes in complex networks based on relative entropy

TL;DR: In the new method, each node’s structure feature can be quantified as a special kind of information and the value of relative entropy between each pair of nodes is used to measure nodes’ structure similarity in complex 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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