Advances in computational immunology

Published versio

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]

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

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

Gene expression and pathway bioinformatics analysis detect a potential predictive value of MAP3K8 in thyroid cancer progression

Thyroid cancer is the commonest endocrine malignancy. Mutation in the BRAF serine/threonine kinase is the most frequent genetic alteration in thyroid cancer. Target therapy for advanced and poorly differentiated thyroid carcinomas include BRAF pathway inhibitors. Here, we evaluated the role of MAP3K8 expression as a potential driver of resistance to BRAF inhibition in thyroid cancer. By analyzing Gene Expression Omnibus data repository, across all thyroid cancer histotypes, we found that MAP3K8 is up-regulated in poorly differentiated thyroid carcinomas and its expression is related to a stem cell like phenotype and a poorer prognosis and survival. Taken together these data unravel a novel mechanism for thyroid cancer progression and chemo-resistance and confirm previous results obtained in cultured thyroid cancer stem cellsComment: 5 page

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