IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-05-21T20:38:43ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2012-02-27T10:32:54Z2012-02-27T10:32:54Zhttp://eprints.imtlucca.it/id/eprint/1190This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/11902012-02-27T10:32:54ZNetworks in cell biologyThe science of complex biological networks is transforming research in areas ranging from evolutionary biology to medicine. This is the first book on the subject, providing a comprehensive introduction to complex network science and its biological applications. With contributions from key leaders in both network theory and modern cell biology, this book discusses the network science that is increasingly foundational for systems biology and the quantitative understanding of living systems. It surveys studies in the quantitative structure and dynamics of genetic regulatory networks, molecular networks underlying cellular metabolism, and other fundamental biological processes. The book balances empirical studies and theory to give a unified overview of this interdisciplinary science. It is a key introductory text for graduate students and researchers in physics, biology and biochemistry, and presents ideas and techniques from fields outside the reader's own area of specialization.Mark BuchananGuido Caldarelliguido.caldarelli@imtlucca.itPaolo De Los RiosFrancesco RaoMichele Vendruscolo2012-02-01T14:07:02Z2014-12-18T15:51:42Zhttp://eprints.imtlucca.it/id/eprint/1102This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/11022012-02-01T14:07:02ZUncovering the topology of configuration space networksThe configuration space network (CSN) of a dynamical system is an effective approach to represent the ensemble of configurations sampled during a simulation and their dynamic connectivity. To elucidate the connection between the CSN topology and the underlying free-energy landscape governing the system dynamics and thermodynamics, an analytical solution is provided to explain the heavy tail of the degree distribution, neighbor connectivity, and clustering coefficient. This derivation allows us to understand the universal CSN topology observed in systems ranging from a simple quadratic well to the native state of the beta3s peptide and a two-dimensional lattice heteropolymer. Moreover, CSNs are shown to fall in the general class of complex networks described by the fitness model.David GfellerDavid Morton de LachapellePaolo De Los RiosGuido Caldarelliguido.caldarelli@imtlucca.itFrancesco Rao2012-02-01T11:47:12Z2012-02-01T11:47:52Zhttp://eprints.imtlucca.it/id/eprint/1097This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/10972012-02-01T11:47:12ZTaxonomy and clustering in collaborative systems: the case of the on-line encyclopedia WikipediaIn this paper we investigate the nature and structure of the relation between imposed classifications and real clustering in a particular case of a scale-free network given by the on-line encyclopedia Wikipedia. We find a statistical similarity in the distributions of community sizes both by using the top-down approach of the categories division present in the archive and in the bottom-up procedure of community detection given by an algorithm based on the spectral properties of the graph. Regardless of the statistically similar behaviour, the two methods provide a rather different division of the articles, thereby signaling that the nature and presence of power laws is a general feature for these systems and cannot be used as a benchmark to evaluate the suitability of a clustering method.Andrea CapocciFrancesco RaoGuido Caldarelliguido.caldarelli@imtlucca.it2012-02-01T10:59:01Z2018-03-08T17:05:00Zhttp://eprints.imtlucca.it/id/eprint/1091This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/10912012-02-01T10:59:01ZOn the rich-club effect in dense and weighted networksFor many complex networks present in nature only a single instance, usually of large size, is available. Any measurement made on this single instance cannot be repeated on different realizations. In order to detect significant patterns in a real-world network it is therefore crucial to compare the measured results with a null model counterpart. Here we focus on dense and weighted networks, proposing a suitable null model and studying the behaviour of the degree correlations as measured by the rich-club coefficient. Our method solves an existing problem with the randomization of dense unweighted graphs, and at the same time represents a generalization of the rich-club coefficient to weighted networks which is complementary to other recently proposed ones. Vinko ZlaticGinestra BianconiAlbert Díaz-GuileraDiego Garlaschellidiego.garlaschelli@imtlucca.itFrancesco RaoGuido Caldarelliguido.caldarelli@imtlucca.it