One of the most challenging tasks for biological scale-free networks analysis is to assign a functional role to each node depending on its location in the network. The network structure allows for a natural combination of different scales: each node inherits its role in the system by its location in the network (top-down causation), while the global properties of the whole network depend upon the edges (bottom-up causation).īiological networks (e.g., protein-protein interaction networks, protein contact maps, gene expression networks, ) very often display a scale-free architecture lying halfway between random networks, whose wiring is assigned according to a Gaussian distribution of link probability, and regular networks, whose nodes all show the same degree (number of edges pertaining to a single node). Complex systems are, thus, easily represented by graphs, whose nodes are the system elements and edges represent the relation between them. The network paradigm helps modeling the multiscale character of biological systems: “networks” is the generic name for graphs, which represent a set of nodes linked by edges. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. All relevant data are within the paper and its Supporting Information files.įunding: The manuscript has been produced thanks to the financial support from SysBioNet, Italian Roadmap Research Infrastructures 2012 and from The Epigenomics Flagship Project (Progetto Bandiera Epigenomica) EPIGEN funded by Italian Ministry of Education, University and Research (MIUR) and the National Research Council of Italy (CNR). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The authors confirm that all data underlying the findings are fully available without restriction. Received: Accepted: JPublished: October 2, 2014Ĭopyright: © 2014 Cumbo et al. PLoS ONE 9(10):Įditor: Baldo Oliva, Universitat Pompeu Fabra, Barcelona Research Park of Biomedicine (PRBB), Spain Citation: Cumbo F, Paci P, Santoni D, Di Paola L, Giuliani A (2014) GIANT: A Cytoscape Plugin for Modular Networks.
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