Marco S. Nobile

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Despite the intense research focused on the investigation of the functioning settings of Particle Swarm Optimization, the particles initialization functions - determining the initial positions in the search space - are generally ignored, especially in the case of real-world applications. As a matter of fact, almost all works exploit uniform distributions to(More)
Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolution of biological systems, modeled according to the stochastic formulation of chemical kinetics. The analysis of dynamical properties of these systems in physiological and perturbed conditions usually requires the execution of a large number of simulations,(More)
In the last years, graphics processing units (GPUs) witnessed ever growing applications for a wide range of computational analyses in the field of life sciences. Despite its large potentiality, GPU computing risks remaining a niche for specialists, due to the programming and optimization skills it requires. In this work we present cupSODA, a simulator of(More)
Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software(More)
We present a parameter estimation method, based on particle swarm optimization (PSO) and embedding the tau-leaping algorithm, for the efficient estimation of reaction constants in stochastic models of biological systems, using as target a set of discrete-time measurements of molecular amounts sampled in different experimental conditions. To account for the(More)
The modeling of biochemical reaction networks is a fundamental but complex task in Systems Biology, which is traditionally performed exploiting human expertise and the available experimental data. Because of the general lack of knowledge on the molecular mechanisms occurring in living cells, an intense research activity focused on the development of reverse(More)
Among the existing global optimization algorithms, Particle Swarm Optimization (PSO) is one of the most effective when dealing with non-linear and complex high-dimensional problems. However, the performance of PSO is strongly dependent on the choice of its settings. In this work we propose a novel and self-tuning PSO algorithm - called Proactive Particles(More)
S-systems are mathematical models based on the power-law formalism, which are widely employed for the investigation of Gene Regulatory Networks (GRNs). Because of their complex dynamics - characterized by multi-modality and nonlinearity-the parameterization of S-systems is far from straightforward, demanding global optimization techniques. The problem of(More)