@article {2013,
	title = {Early phase of plasticity-related gene regulation and SRF dependent transcription in the hippocampus},
	journal = {PloS one. Volume 8, Issue 7, July 2013 : e68078},
	number = {PMID:23935853;},
	year = {2013},
	note = {The article is composed of 15 pages},
	publisher = {Public Library of Science},
	abstract = {Hippocampal organotypic cultures are a highly reliable in vitro model for studying neuroplasticity: in this paper, we analyze the early phase of the transcriptional response induced by a 20 {\textmu}M gabazine treatment (GabT), a GABA-Ar antagonist, by using Affymetrix oligonucleotide microarray, RT-PCR based time-course and chromatin-immuno-precipitation. The transcriptome profiling revealed that the pool of genes up-regulated by GabT, besides being strongly related to the regulation of growth and synaptic transmission, is also endowed with neuro-protective and pro-survival properties. By using RT-PCR, we quantified a time-course of the transient expression for 33 of the highest up-regulated genes, with an average sampling rate of 10 minutes and covering the time interval [10:90] minutes. The cluster analysis of the time-course disclosed the existence of three different dynamical patterns, one of which proved, in a statistical analysis based on results from previous works, to be significantly related with SRF-dependent regulation (p-value<0.05). The chromatin immunoprecipitation (chip) assay confirmed the rich presence of working CArG boxes in the genes belonging to the latter dynamical pattern and therefore validated the statistical analysis. Furthermore, an in silico analysis of the promoters revealed the presence of additional conserved CArG boxes upstream of the genes Nr4a1 and Rgs2. The chip assay confirmed a significant SRF signal in the Nr4a1 CArG box but not in the Rgs2 CArG box.},
	doi = {10.1371/journal.pone.0068078},
	url = {http://hdl.handle.net/1963/7287},
	author = {Giovanni Iacono and Claudio Altafini and Vincent Torre}
}
@article {2012,
	title = {Decompositions of large-scale biological systems based on dynamical properties},
	journal = {Bioinformatics (Oxford, England). 2012 Jan; 28(1):76-83},
	number = {PMID:22072388;},
	year = {2012},
	publisher = {Oxford University Press},
	abstract = {MOTIVATION: Given a large-scale biological network represented as an influence graph, in this article we investigate possible decompositions of the network aimed at highlighting specific dynamical properties.\\r\\nRESULTS: The first decomposition we study consists in finding a maximal directed acyclic subgraph of the network, which dynamically corresponds to searching for a maximal open-loop subsystem of the given system. Another dynamical property investigated is strong monotonicity. We propose two methods to deal with this property, both aimed at decomposing the system into strongly monotone subsystems, but with different structural characteristics: one method tends to produce a single large strongly monotone component, while the other typically generates a set of smaller disjoint strongly monotone subsystems.\\r\\nAVAILABILITY: Original heuristics for the methods investigated are described in the article.},
	doi = {10.1093/bioinformatics/btr620},
	url = {http://hdl.handle.net/1963/5226},
	author = {Nicola Soranzo and Fahimeh Ramezani and Giovanni Iacono and Claudio Altafini}
}
@article {2012,
	title = {Exploring the low-energy landscape of large-scale signed social networks},
	journal = {Physical Review E - Statistical, Nonlinear, and Soft Matter Physics. Volume 86, Issue 3, 26 September 2012, Article number036116},
	year = {2012},
	publisher = {SISSA},
	abstract = {Analogously to a spin glass, a large-scale signed social network is characterized by the presence of disorder, expressed in this context (and in the social network literature) by the concept of structural balance. If, as we have recently shown, the signed social networks currently available have a limited amount of true disorder (or frustration), it is also interesting to investigate how this frustration is organized, by exploring the landscape of near-optimal structural balance. What we obtain in this paper is that while one of the networks analyzed shows a unique valley of minima, and a funneled landscape that gradually and smoothly worsens as we move away from the optimum, another network shows instead several distinct valleys of optimal or near-optimal structural balance, separated by energy barriers determined by internally balanced subcommunities of users, a phenomenon similar to the replica-symmetry breaking of spin glasses. Multiple, essentially isoenergetic, arrangements of these communities are possible. Passing from one valley to another requires one to destroy the internal arrangement of these balanced subcommunities and then to reform it again. It is essentially this process of breaking the internal balance of the subcommunities which gives rise to the energy barriers.},
	doi = {10.1103/PhysRevE.86.036116},
	url = {http://hdl.handle.net/1963/6504},
	author = {Giuseppe Facchetti and Giovanni Iacono and Claudio Altafini}
}
@article {2011,
	title = {Computing global structural balance in large-scale signed social networks.},
	journal = {Proceedings of the National Academy of Sciences of the United States of America. Volume 108, Issue 52, 27 December 2011, Pages 20953-20958},
	number = {PMID:22167802;},
	year = {2011},
	note = {Free fulltext article in Pubmed Central},
	publisher = {National Academy of Sciences},
	abstract = {Structural balance theory affirms that signed social networks (i.e., graphs whose signed edges represent friendly/hostile interactions among individuals) tend to be organized so as to avoid conflictual situations, corresponding to cycles of negative parity. Using an algorithm for ground-state calculation in large-scale Ising spin glasses, in this paper we compute the global level of balance of very large online social networks and verify that currently available networks are indeed extremely balanced. This property is explainable in terms of the high degree of skewness of the sign distributions on the nodes of the graph. In particular, individuals linked by a large majority of negative edges create mostly \\\"apparent disorder,\\\" rather than true \\\"frustration.\\\"},
	keywords = {Combinatorial optimization},
	doi = {10.1073/pnas.1109521108},
	url = {http://hdl.handle.net/1963/6426},
	author = {Giuseppe Facchetti and Giovanni Iacono and Claudio Altafini}
}
@article {2010,
	title = {Monotonicity, frustration, and ordered response: an analysis of the energy landscape of perturbed large-scale biological networks},
	journal = {BMC Systems Biology 2010, 4:83},
	year = {2010},
	publisher = {BioMed Central},
	abstract = {Background. \\nFor large-scale biological networks represented as signed graphs, the index of frustration measures how far a network is from a monotone system, i.e., how incoherently the system responds to perturbations.\\nResults. \\nIn this paper we find that the frustration is systematically lower in transcriptional networks (modeled at functional level) than in signaling and metabolic networks (modeled at stoichiometric level). A possible interpretation of this result is in terms of energetic cost of an interaction: an erroneous or contradictory transcriptional action costs much more than a signaling/metabolic error, and therefore must be avoided as much as possible. Averaging over all possible perturbations, however, we also find that unlike for transcriptional networks, in the signaling/metabolic networks the probability of finding the system in its least frustrated configuration tends to be high also in correspondence of a moderate energetic regime, meaning that, in spite of the higher frustration, these networks can achieve a globally ordered response to perturbations even for moderate values of the strength of the interactions. Furthermore, an analysis of the energy landscape shows that signaling and metabolic networks lack energetic barriers around their global optima, a property also favouring global order.\\nConclusion. \\nIn conclusion, transcriptional and signaling/metabolic networks appear to have systematic differences in both the index of frustration and the transition to global order. These differences are interpretable in terms of the different functions of the various classes of networks.},
	doi = {10.1186/1752-0509-4-83},
	url = {http://hdl.handle.net/1963/4055},
	author = {Giovanni Iacono and Claudio Altafini}
}
