Modeling immune system control of atherogenesis

Abstract Motivation: Atherosclerosis is a disease that is present in almost all humans, typically beginning in early adolescence. It is a human disease broadly investigated, that is amenable to quantitative analysis. Oxidized low-density lipoproteins (LDLs) and their autoantibodies are involved in the development of atherosclerosis in animal models, but their role in humans is still not clear. Computer models may represent a virtual environment to perform experiments not possible in human volunteers that can provide a useful instrument for monitoring both the evolution of atherosclerotic lesions and to quantify the efficacy of treatments, including vaccines, oriented to reduce the LDLs and their oxidized fraction. Results: We report the application of an agent-based model to model both the immune response to atherogenesis and the atheromatous plaque progression in a generic artery wall. The level of oxidized LDLs, the immune humoral response with production of autoantibodies, the macrophages activity and the formation of foam cells are in good agreement with available clinical data, including the formation of atheromatous plaques in patients affected by hypercholesterolemia. Availability: The model is available at http://www.immunogrid.eu/atherogenesis/ Contact: [email protected]

Optimal vaccination schedules using simulated annealing

Abstract Summary: Since few years the problem of finding optimal solutions for drug or vaccine protocols have been tackled using system biology modeling. These approaches are usually computationally expensive. Our previous experiences in optimizing vaccine or drug protocols using genetic algorithms required the use of a high performance computing infrastructure for a couple of days. In the present article we show that by an appropriate use of a different optimization algorithm, the simulated annealing, we have been able to downsize the computational effort by a factor102. The new algorithm requires computational effort that can be achieved by current generation personal computers. Availability: Software and additional data can be found at http://www.immunomics.eu/SA/ Contact: [email protected]

Optimization of HAART with genetic algorithms and agent-based models of HIV infection

Motivation: Highly Active AntiRetroviral Therapies (HAART) can prolong life significantly to people infected by HIV since, although unable to eradicate the virus, they are quite effective in maintaining control of the infection. However, since HAART have several undesirable side effects, it is considered useful to suspend the therapy according to a suitable schedule of Structured Therapeutic Interruptions (STI).In the present article we describe an application of genetic algorithms (GA) aimed at finding the optimal schedule for a HAART simulated with an agent-based model (ABM) of the immune system that reproduces the most significant features of the response of an organism to the HIV-1 infection.Results: The genetic algorithm helps in finding an optimal therapeutic schedule that maximizes immune restoration, minimizes the viral count and, through appropriate interruptions of the therapy, minimizes the dose of drug administered to the simulated patient.To validate the efficacy of the therapy that the genetic algorithm indicates as optimal, we ran simulations of opportunistic diseases and found that the selected therapy shows the best survival curve among the different simulated control groups.Availability: A version of the C-ImmSim simulator is available at http://www.iac.cnr.it/~filippo/c-ImmSim.htmlContact: [email protected]

Computational modelling approaches to vaccinology

Excepting the Peripheral and Central Nervous Systems, the Immune System is the most complex of somatic systems in higher animals. This complexity manifests itself at many levels from the molecular to that of the whole organism. Much insight into this confounding complexity can be gained through computational simulation. Such simulations range in application from epitope prediction through to the modelling of vaccination strategies. In this review, we evaluate selectively various key applications relevant to computational vaccinology: these include technique that operates at different scale that is, from molecular to organisms and even to population level

A novel paradigm for cell and molecule interaction ontology: from the CMM model to IMGT-ONTOLOGY

International audienc

Agent Based Modeling of Lung Metastasis-Immune System Competition,

Extended Abstract The Triplex vaccine is a cell vaccine developed as an immunopreventive approach to breast cancer. Recent studies showed that the same vaccine has a considerable therapeutic effect against lung metastases derived by mammary carcinoma Using three different signals (the target antigen, interleukin-12 (IL-12) and allogeneic MHC molecules) it stimulates immune system response in many ways. The "in vivo" experiment lasts for 32 days. For the induction of lung micrometastasis, all mice received an intravenous injection of 2.5·10 4 metastatic cells at day 0. In standard "in vivo" experiments it is considered common practice to use multiple sets of mice, each treated with a different protocol. One of these sets (the control set) is usually treated with a placebo solution in order to study/represen

SimB16: Modeling Induced Immune System Response against B16-Melanoma

Immunological therapy of progressive tumors requires not only activation and expansion of tumor specific cytotoxic T lymphocytes (CTLs), but also an efficient effector phase including migration of CTLs in the tumor tissue followed by conjugation and killing of target cells. We report the application of an agent-based model to recapitulate both the effect of a specific immunotherapy strategy against B16-melanoma in mice and the tumor progression in a generic tissue section. A comparison of the in silico results with the in vivo experiments shows excellent agreement. We therefore use the model to predict a critical role for CD137 expression on tumor vessel endothelium for successful therapy and other mechanistic aspects. Experimental results are fully compatible with the model predictions. The biologically oriented in silico model derived in this work will be used to predict treatment failure or success in other pre-clinical conditions eventually leading new promising in vivo experiments

The evolution of the high-energy cut-off in the X-Ray spectrum of the GX 339-4 across a hard-to-soft transition

We report on X-ray observations of the black-hole candidate GX 339-4 during its 2006/2007 outburst. The hardness-intensity diagram of all RXTE/PCA data combined shows a q-shaped track similar to that observed in previous outbursts. The evolution through the HID suggests that in the early phase of the outburst the source underwent a sequence of state transitions, from the hard to the soft state, which is supported by our timing analysis. Broadband (4-200 keV) spectra, fitted with an exponentially cutoff powerlaw, show that the hard spectral component steepens during the transition from the hard to the soft state. The high-energy cutoff decreased monotonically from 120 to 60 keV during the brightening of the hard state, but increased again to 100 keV during the softening in the hard intermediate state. In the short-lived soft intermediate state the cutoff energy was ~ 130 keV, but was no longer detected in the soft state. This is one of the first times that the high-energy cut-off has been followed in such detail across several state transitions. We find that in comparison to several other spectral parameters, the cut-off energy changes more rapidly, just like the timing properties. The observed behaviour of the high energy cutoff of GX 339-4 is also similar to that observed with RXTE-INTEGRAL-Swift during the 2005 outburst of GRO J1655-40. These results constitute a valuable reference to be considered when testing theoretical models for the production of the hard component in these systems.Comment: 16 pages, 8 figures, 5 tables, accepted for publication in MNRAS Main Journa