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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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Emergence of Soft Communities from Geometric Preferential Attachment

TL;DR: Geometric preferential attachment (GPA) as discussed by the authors was proposed to explain scale-free degree distributions, strong clustering, and community structure in growing networks, and has been validated against the Internet.
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Generating extractive summaries of scientific paradigms

TL;DR: C-LexRank is proposed, a model for summarizing single scientific articles based on citations, which employs community detection and extracts salient information-rich sentences and shows that citations have unique information amenable to creating a summary.
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Dynamical Systems Approach to Endothelial Heterogeneity

TL;DR: This work draws on the field of nonlinear dynamics to propose a dynamical systems framework for modeling multistability and its derivative properties, including robustness, memory, and plasticity in vascular biology.
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Expression dynamics of a cellular metabolic network

TL;DR: It is shown that basic topological motifs of the metabolic network exhibit statistically significant differences in coexpression behavior, and positive gene coexpression decreases monotonically with distance in the network, while negative coexpression is strongest at intermediate network distances.
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Cell surface receptors for signal transduction and ligand transport: a design principles study.

TL;DR: A generalized mathematical model of receptor–ligand binding and internalization is constructed to understand how receptor internalization dynamics encodes receptor function and regulation and reveals that the location of a receptor system in the avidity-consumption parameter space can be used to understand both its function and its regulation.
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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