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Dive into the research topics where Irene Poli is active.

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Featured researches published by Irene Poli.


Journal of the American Statistical Association | 1994

A Neural Net Model for Prediction

Irene Poli; R. D. Jones

Abstract In this article we introduce a neural net designed for nonlinear statistical prediction. The net is based on a stochastic model featuring a multilayer feedforward architecture with random connections between units and noisy response functions. A Bayesian inferential procedure for this model, based on the Kalman filter, is derived. The resulting learning algorithm generalizes the so-called onedimensional Newton method, an updating algorithm currently popular in the neural net literature. A numerical study concerning the prediction of a noisy chaotic time series is presented, and the greater predictive accuracy of the new algorithm with respect to the Newton algorithm is exhibited.


Journal of Theoretical Biology | 2008

Sufficient conditions for emergent synchronization in protocell models.

T. Carletti; Roberto Serra; Irene Poli; Marco Villani; Alessandro Filisetti

In this paper, we study general protocell models aiming to understand the synchronization phenomenon of genetic material and container productions, a necessary condition to ensure sustainable growth in protocells and eventually leading to Darwinian evolution when applied to a population of protocells. Synchronization has been proved to be an emergent property in many relevant protocell models in the class of the so-called surface reaction models, assuming both linear- and non-linear dynamics for the involved chemical reactions. We here extend this analysis by introducing and studying a new class of models where the relevant chemical reactions are assumed to occur inside the protocell, in contrast with the former model where the reaction site was the external surface. While in our previous studies, the replicators were assumed to compete for resources, without any direct interaction among them, we here improve both models by allowing linear interaction between replicators: catalysis and/or inhibition. Extending some techniques previously introduced, we are able to give a quite general analytical answer about the synchronization phenomenon in this more general context. We also report on results of numerical simulations to support the theory, where applicable, and allow the investigation of cases which are not amenable to analytical calculations.


evoworkshops on applications of evolutionary computing | 2001

Building ARMA Models with Genetic Algorithms

Tommaso Minerva; Irene Poli

The current state of the art in selecting ARMA time series models requires competence and experience on the part of the practitioner, and sometimes the results are not very satisfactory. In this paper, we propose a new automatic approach to the model selection problem, based upon evolutionary computation. We build a genetic algorithm which evolves the representation of a predictive model, choosing both the orders and the predictors of the model. In simulation studies, the procedure succeeded in identifying the data generating process in the great majority of cases studied.


Journal of Systems Chemistry | 2011

A stochastic model of the emergence of autocatalytic cycles

Alessandro Filisetti; Alex Graudenzi; Roberto Serra; Marco Villani; Davide De Lucrezia; Rudolf Marcel Füchslin; Stuart A. Kauffman; Norman H. Packard; Irene Poli

Autocatalytic cycles are rather common in biological systems and they might have played a major role in the transition from non-living to living systems. Several theoretical models have been proposed to address the experimentalists during the investigation of this issue and most of them describe a phase transition depending upon the level of heterogeneity of the chemical soup. Nevertheless, it is well known that reproducing the emergence of autocatalytic sets in wet laboratories is a hard task. Understanding the rationale at the basis of such a mismatch between theoretical predictions and experimental observations is therefore of fundamental importance.We here introduce a novel stochastic model of catalytic reaction networks, in order to investigate the emergence of autocatalytic cycles, sensibly considering the importance of noise, of small-number effects and the possible growth of the number of different elements in the system.Furthermore, the introduction of a temporal threshold that defines how long a specific reaction is kept in the reaction graph allows to univocally define cycles also within an asynchronous framework.The foremost analyses have been focused on the study of the variation of the composition of the incoming flux. It was possible to show that the activity of the system is enhanced, with particular regard to the emergence of autocatalytic sets, if a larger number of different elements is present in the incoming flux, while the specific length of the species seems to entail minor effects on the overall dynamics.


Artificial Life | 2007

Synchronization Phenomena in Surface-Reaction Models of Protocells

Roberto Serra; T. Carletti; Irene Poli

A class of generic models of protocells is introduced, which are inspired by the Los Alamos bug hypothesis but which, due to their abstraction level, can be applied to a wider set of detailed protocell hypotheses. These models describe the coupled growth of the lipid container and of the self-replicating molecules. A technique to analyze the dynamics of populations of such protocells is described, which couples a continuous-time formalism for the growth between two successive cell divisions, and a discrete map that relates the quantity of self-replicating molecules in successive generations. This technique allows one to derive several properties in an analytical way. It is shown that, under fairly general assumptions, the two growth rates synchronize, so that the lipid container doubles its size when the number of self-replicating molecules has also doubledthus giving rise to exponential growth of the population of protocells. Such synchronization had been postulated a priori in previous models of protocells; here it is an emergent property. We also compare the rate of duplication of two populations generated by two different protocells with different kinds of self-replicating molecules, considering the interesting case where the rate of self-replication of one kind is higher than that of the other, but its contribution to the container growth rate is smaller. It is shown that in this case the population of offspring of the protocell with the faster-replicating molecule will eventually grow faster than the other. The case where two different types of self-replicating monomers are present in the same protocell is also analyzed, and it is shown that, if the replication follows a first-order kinetic equation, then the faster replicator eventually displaces the slower one, whereas if the growth is sublinear the two coexist. It is also proven by an appropriate rescaling of time that the results that concern the system asymptotic dynamics hold both for micelles and vesicles.


Statistical Methods and Applications | 1998

A genetic algorithm for graphical model selection

Irene Poli; Alberto Roverato

Graphical log-linear model search is usually performed by using stepwise procedures in which edges are sequentially added or eliminated from the independence graph. In this paper we implement the search procedure as a genetic algorithm and propose a crossover operator which operates on subgraphs. In a simulation study the proposed procedure is shown to perform better than an automatic backward elimination procedure at the cost of a small increase of computational time.


Artificial Life | 2015

The search for candidate relevant subsets of variables in complex systems

Marco Villani; Andrea Roli; Alessandro Filisetti; Marco Fiorucci; Irene Poli; Roberto Serra

We describe a method to identify relevant subsets of variables, useful to understand the organization of a dynamical system. The variables belonging to a relevant subset should have a strong integration with the other variables of the same relevant subset, and a much weaker interaction with the other system variables. On this basis, extending previous work on neural networks, an information-theoretic measure, the dynamical cluster index, is introduced in order to identify good candidate relevant subsets. The method does not require any previous knowledge of the relationships among the system variables, but relies on observations of their values over time. We show its usefulness in several application domains, including: (i) random Boolean networks, where the whole network is made of different subnetworks with different topological relationships (independent or interacting subnetworks); (ii) leader-follower dynamics, subject to noise and fluctuations; (iii) catalytic reaction networks in a flow reactor; (iv) the MAPK signaling pathway in eukaryotes. The validity of the method has been tested in cases where the data are generated by a known dynamical model and the dynamical cluster index is applied in order to uncover significant aspects of its organization; however, it is important that it can also be applied to time series coming from field data without any reference to a model. Given that it is based on relative frequencies of sets of values, the method could be applied also to cases where the data are not ordered in time. Several indications to improve the scope and effectiveness of the dynamical cluster index to analyze the organization of complex systems are finally given.


PLOS ONE | 2012

Do Natural Proteins Differ from Random Sequences Polypeptides? Natural vs. Random Proteins Classification Using an Evolutionary Neural Network

Davide De Lucrezia; Debora Slanzi; Irene Poli; Fabio Polticelli; Giovanni Minervini

Are extant proteins the exquisite result of natural selection or are they random sequences slightly edited by evolution? This question has puzzled biochemists for long time and several groups have addressed this issue comparing natural protein sequences to completely random ones coming to contradicting conclusions. Previous works in literature focused on the analysis of primary structure in an attempt to identify possible signature of evolutionary editing. Conversely, in this work we compare a set of 762 natural proteins with an average length of 70 amino acids and an equal number of completely random ones of comparable length on the basis of their structural features. We use an ad hoc Evolutionary Neural Network Algorithm (ENNA) in order to assess whether and to what extent natural proteins are edited from random polypeptides employing 11 different structure-related variables (i.e. net charge, volume, surface area, coil, alpha helix, beta sheet, percentage of coil, percentage of alpha helix, percentage of beta sheet, percentage of secondary structure and surface hydrophobicity). The ENNA algorithm is capable to correctly distinguish natural proteins from random ones with an accuracy of 94.36%. Furthermore, we study the structural features of 32 random polypeptides misclassified as natural ones to unveil any structural similarity to natural proteins. Results show that random proteins misclassified by the ENNA algorithm exhibit a significant fold similarity to portions or subdomains of extant proteins at atomic resolution. Altogether, our results suggest that natural proteins are significantly edited from random polypeptides and evolutionary editing can be readily detected analyzing structural features. Furthermore, we also show that the ENNA, employing simple structural descriptors, can predict whether a protein chain is natural or random.


Biophysical Reviews and Letters | 2008

SYNCHRONIZATION PHENOMENA IN PROTOCELL MODELS

Alessandro Filisetti; Roberto Serra; Timoteo Carletti; Irene Poli; Marco Villani

This work aims to consider simplified models of protocells in order to describe their general behaviors. The advantage of the modelling approach is that the early protocells of life-forms on Earth are not reproduced in the present time. However, the problem is considered as a right track to understand the origin of life as well as to work with more objective synthesis of new drugs.


workshop artificial life and evolutionary computation | 2014

On Some Properties of Information Theoretical Measures for the Study of Complex Systems

Alessandro Filisetti; Marco Villani; Andrea Roli; Marco Fiorucci; Irene Poli; Roberto Serra

The identification of emergent structures in dynamical systems is a major challenge in complex systems science. In particular, the formation of intermediate-level dynamical structures is of particular interest for what concerns biological as well as artificial systems. In this work, we present a set of measures aimed at identifying groups of elements that behave in a coherent and coordinated way and that loosely interact with the rest of the system (the so-called “relevant sets”). These measures are based on Shannon entropy, and they are an extension of a measure introduced for detecting clusters in biological neural networks. Even if our results are still preliminary, we have evidence for showing that our approach is able to identify and partially characterise the relevant sets in some artificial systems, and that this way is more powerful than usual measures based on statistical correlation. In this work, the two measures that contribute to the cluster index, previously adopted in the analysis of neural networks, i.e. integration and mutual information, are analysed separately in order to enhance the overall performance of the so-called dynamical cluster index. Although this latter variable already provides useful information about highly integrated subsystems, the analysis of the different parts of the index are extremely useful to better characterise the nature of the sub-systems.

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Roberto Serra

University of Modena and Reggio Emilia

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Debora Slanzi

Ca' Foscari University of Venice

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Marco Villani

University of Modena and Reggio Emilia

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Davide De Lucrezia

Ca' Foscari University of Venice

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Davide De March

Ca' Foscari University of Venice

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Matteo Borrotti

Ca' Foscari University of Venice

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Emanuele Argese

Ca' Foscari University of Venice

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T. Carletti

Ca' Foscari University of Venice

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