scispace - formally typeset
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

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

The noncoding RNA expression profile and the effect of lncRNA AK126698 on cisplatin resistance in non-small-cell lung cancer cell.

TL;DR: Cisplatin resistance in non-small-cell lung cancer cells may relate to the changes in noncoding RNAs as identified by microarray expression profiling of mRNAs, lncRNA and miRNA in A549 cells and cisplatin resistant A549/CDDP cells.
Journal ArticleDOI

Evolution of biomolecular networks: lessons from metabolic and protein interactions.

TL;DR: Despite only becoming popular at the beginning of this decade, biomolecular networks are now frameworks that facilitate many discoveries in molecular biology, and changes in the nodes and links in protein–protein interaction and metabolic networks are subject to different selection pressures.
Journal ArticleDOI

A method for the generation of standardized qualitative dynamical systems of regulatory networks

TL;DR: A set of equations that can be used to translate the graph of any regulatory network into a continuous dynamical system, and also find its steady stable states are developed.
Journal ArticleDOI

Revisiting "scale-free" networks.

TL;DR: The real surprise, if any, is that power-law distributions are easy to generate, and by a variety of mechanisms; the architecture that results is not universal, but particular; it is determined by the actual constraints on the system in question.
References
More filters
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.
Related Papers (5)