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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

Computational Strategies for a System-Level Understanding of Metabolism

TL;DR: This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks and describing some computational approaches to gain new biological knowledge or to formulate hypotheses.
Journal ArticleDOI

Phylogenetic distances are encoded in networks of interacting pathways

TL;DR: An original metabolome representation as network of overlapping metabolic pathways, termed Network of Interacting Pathways or NIP, with a combination of graph theoretical and machine learning strategies captures sufficient information about the underlying evolutionary events leading to the formation of metabolic networks and species phylogeny.
Book ChapterDOI

Introduction to Philosophy of Complex Systems

Cliff Hooker
TL;DR: The complex systems revolution is currently exploding through science, transforming its concepts, principles, methods and conclusions as discussed by the authors, and transforming its disciplinary structure, both creating new, distinctive "complexity" disciplines, such as climate science, systems and synthetic biology and self-assembling/repairing and social robotics, and transforming older disciplinary relations, eg between developmental biology, psychology and sociology.
Journal ArticleDOI

Protein interaction networks of Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster: Large‐scale organization and robustness

TL;DR: A systematic analysis of topological structure and robustness was performed on theprotein interaction networks of Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster, showing that the three protein interaction networks have a scale‐free and high‐degree clustering nature as the consequence of their hierarchical organization.
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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