title: Organization principles of biological networks: An explorative study.
abstract: The definition of general topological principles allowing for graph characterization is an important pre-requisite for investigating structure-function relationships in biological networks. Here we approached the problem by means of an explorative, data-driven strategy, building upon a size-balanced data set made of around 200 distinct biological networks from seven functional classes and simulated networks coming from three mathematical graph models. A clear link between topological structure and biological function did emerge in terms of class membership prediction (average 67% of correct predictions, p<0.0001) with a varying degree of 'peculiarity' across classes going from a very low (25%) recognition efficiency for neural and brain networks to the extremely high (80%) peculiarity of amino acid-amino acid interaction (AAI) networks. We recognized four main dimensions (principal components) as main organization principles of biological networks. These components allowed for an efficient description of network architectures and for the identification of 'not-physiological' (in this case cancer metabolic networks acting as test set) wiring patterns. We highlighted as well the need of developing new theoretical generative models for biological networks overcoming the limitations of present mathematical graph idealizations.
authors: Department of Biology, Faculty of Natural Science, University of Tabriz, Tabriz 5166616471, Iran. Electronic address: ha_koohestani90@ms.tabrizu.ac.ir.
authors: Havva
authors: H
authors: Kohestani
authors: Environment and Health Dept., Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Roma, Italy. Electronic address: alessandro.giuliani@iss.it.
authors: Alessandro
authors: A
authors: Giuliani
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D009415:Nerve Net; D025341:Principal Component Analysis; D060066:Protein Interaction Maps; D015203:Reproducibility of Results
keywords: Biocomplexity; Graph theory; Principal component analysis; Systems biology; Topology