Objective: The aim of this study was to develop a computational model of HIV infection able to simulate the natural history of the disease and to test predictive parameters of disease progression. Design: We describe the results of a numerical simulation of the cellular and humoral immune response to the HIV-1 infection as an adaptive pathway in a “bit-string” space. Methods: A total of 650 simulations of the HIV-1 dynamics were performed with a modified version of the Celada-Seiden immune system model . Results: Statistics are in agreement with epidemiological studies showing a lognormal distribution for the time span between the infection and AIDS development. As predictive parameters of disease progression we found that HIV-1 accumulates “bit”-mutations mainly in the peptide sequences recognized by cytotoxic CD8 T cells, indicating that cell-mediated immunity plays a major role in viral control. The viral load set-point was closely correlated with the time from the infection to AIDS development. Viral divergence from the viral quasispecies that was present at the beginning of the infection in long-term non-progressors (LTNP) was found to be similar to that found in rapid progressors at the time CD4 T cells drop below the critical value of 200 cells per ml. In contrast, the diversity indicated by the number of HIV strains present at the same time was higher for rapid and normal progressors compared to LTNP, suggesting that the early immune response can make the difference. Conclusion: This computational model may help to define the predictive parameters of HIV dynamics and disease progression, with potential applications in therapeutic and vaccines simulations.

We describe a model for the optimization of the issuances of Public Debt securities developed together with the Italian Ministry of Economy and Finance. The goal is to find the composition of the portfolio issued every month which minimizes a specific “cost function”. Mathematically speaking, this is a stochastic optimal control problem with strong constraints imposed by national regulations and the Maastricht treaty. The stochastic component of the problem is represented by the evolution of interest rates. At this time the optimizer employs classic Linear Programming techniques. However more sophisticated techniques based on Model Predictive Control strategies are under development.

This paper is devoted to the formulation of a model for the optimal asset-liability man- agement for insurance companies. We focus on a typical guaranteed investment con- tract, by which the holder has the right to receive after T years a return that cannot be lower than a minimum predened rate rg. We take account of the rules that usually are imposed to insurance companies in the management of this funds as reserves and solvency margin. We formulate the problem as a stochastic optimization problem in a discrete time setting comparing this approach with the so-called hedging approach. The utility function to maximize depends on various parameters including specific goals of the company management. Some preliminary numerical results are reported to ease the comparison between the two approaches.

We present computer simulations of the HIV infection based on a sophisticated cellular automata model of the immune response. The infection progresses following the well known three-phases dynamics observed in patients, that is, acute, silent and acquired immunodeficiency. Antigenic shift and selection of escape viral mutants with low transcription rate explain the long-term course of the asymptomatic phase, while the immunodeficiency status appears to be the consequence of a drastic reduction in T helper cell repertoire