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GIANLUCA D'ADDESE

Dottorando presso: Dipartimento di Scienze Fisiche, Informatiche e Matematiche


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Pubblicazioni

2021 - A Fast and Effective Method to Identify Relevant Sets of Variables in Complex Systems [Articolo su rivista]
D’Addese, Gianluca; Casari, Martina; Serra, Roberto; Villani, Marco
abstract

In many complex systems one observes the formation of medium-level structures, whose detection could allow a high-level description of the dynamical organization of the system itself, and thus to its better understanding. We have developed in the past a powerful method to achieve this goal, which however requires a heavy computational cost in several real-world cases. In this work we introduce a modified version of our approach, which reduces the computational burden. The design of the new algorithm allowed the realization of an original suite of methods able to work simultaneously at the micro level (that of the binary relationships of the single variables) and at meso level (the identification of dynamically relevant groups). We apply this suite to a particularly relevant case, in which we look for the dynamic organization of a gene regulatory network when it is subject to knock-outs. The approach combines information theory, graph analysis, and an iterated sieving algorithm in order to describe rather complex situations. Its application allowed to derive some general observations on the dynamical organization of gene regulatory networks, and to observe interesting characteristics in an experimental case


2021 - Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems [Articolo su rivista]
D'Addese, Gianluca; Sani, Laura; La Rocca, Luca; Serra, Roberto; Villani, Marco
abstract

The identification of emergent structures in complex dynamical systems is a formidable challenge. We propose a computationally efficient methodology to address such a challenge, based on modeling the state of the system as a set of random variables. Specifically, we present a sieving algorithm to navigate the huge space of all subsets of variables and compare them in terms of a simple index that can be computed without resorting to simulations. We obtain such a simple index by studying the asymptotic distribution of an information-theoretic measure of coordination among variables, when there is no coordination at all, which allows us to fairly compare subsets of variables having different cardinalities. We show that increasing the number of observations allows the identification of larger and larger subsets. As an example of relevant application, we make use of a paradigmatic case regarding the identification of groups in autocatalytic sets of reactions, a chemical situation related to the origin of life problem.


2020 - Exploring the Dynamic Organization of Random and Evolved Boolean Networks [Articolo su rivista]
d’Addese, Gianluca; Magrì, Salvatore; Serra, Roberto; Villani, Marco
abstract

The properties of most systems composed of many interacting elements are neither determined by the topology of the interaction network alone, nor by the dynamical laws in isolation. Rather, they are the outcome of the interplay between topology and dynamics. In this paper, we consider four different types of systems with critical dynamic regime and with increasingly complex dynamical organization (loosely defined as the emergent property of the interactions between topology and dynamics) and analyze them from a structural and dynamic point of view. A first noteworthy result, previously hypothesized but never quantified so far, is that the topology per se induces a notable increase in dynamic organization. A second observation is that evolution does not change dramatically the size distribution of the present dynamic groups, so it seems that it keeps track of the already present organization induced by the topology. Finally, and similarly to what happens in other applications of evolutionary algorithms, the types of dynamic changes strongly depend upon the used fitness functio


2020 - The detection of dynamical organization in cancer evolution models [Capitolo/Saggio]
Sani, L.; D'Addese, G.; Graudenzi, A.; Villani, M.
abstract

Many systems in nature, society and technology are composed of numerous interacting parts. Very often these dynamics lead to the formation of medium-level structures, whose detection could allow a high-level description of the dynamical organization of the system itself, and thus to its understanding. In this work we apply this idea to the “cancer evolution” models, of which each individual patient represents a particular instance. This approach - in this paper based on the RI methodology, which is based on entropic measures - allows us to identify distinct independent cancer progression patterns in simulated patients, planning a road towards applications to real cases.


2018 - An Integration-Based Approach to Pattern Clustering and Classification [Relazione in Atti di Convegno]
Sani, L.; D'Addese, G.; Pecori, R.; Mordonini, M.; Villani, M.; Cagnoni, S.
abstract

Methods based on information theory, such as the Relevance Index (RI), have been employed to study complex systems for their ability to detect significant groups of variables, well integrated among one another and well separated from the others, which provide a functional block description of the system under analysis. The integration (or zI in its standardized form) is a metric that can express the significance of a group of variables for the system under consideration: the higher the zI, the more significant the group. In this paper, we use this metric for an unusual application to a pattern clustering and classification problem. The results show that the centroids of the clusters of patterns identified by the method are effective for distance-based classification algorithms. We compare such a method with other conventional classification approaches to highlight its main features and to address future research towards the refinement of its accuracy and computational efficiency.