Quantitative Validation of a Yellow Fever Vaccine Model An effective yellow fever vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for booster dose, the optimal immunization schedule for immunocompetent, immunosuppressed, and children, among other issues. The objective of this work is to demonstrate that computational tools can be used to simulate different scenarios regarding yellow fever vaccination and the immune response of the individuals to this vaccine, thus assisting the response of some of these open questions. In this context, this work presents the results of a computational model of the human immune response to vaccination against yellow fever. The model takes into account essential cells and molecules of the human immune system, such as antigenpresenting cells, B and T lymphocytes, memory cells, and antibodies. The model was able to replicate the levels of antibodies obtained experimentally in different vaccination scenarios, allowing a quantitative validation with experimental data.

Generation of digital patients for the simulation of tuberculosis with UISSTB EC funded STriTuVaD project aims to test, through a phase IIb clinical trial, two of the most advanced therapeutic vaccines against tuberculosis. In parallel, we have extended the Universal Immune System Simulator to include all relevant determinants of such clinical trial, to establish its predictive accuracy against the individual patients recruited in the trial, to use it to generate digital patients and predict their response to the HRT being tested, and to combine them to the observations made on physical patients using a new in silicoaugmented clinical trial approach that uses a Bayesian adaptive design. This approach, where found effective could drastically reduce the cost of innovation in this critical sector of public healthcare. One of the most challenging task is to develop a methodology to reproduce biological diversity of the subjects that have to be simulated, i.e., provide an appropriate strategy for the generation of libraries of digital patients. This has been achieved through the the creation of the initial immune system repertoire in a stochastic way, and though the identification of a xe2x80x9cvector of featuresxe2x80x9d that combines both biological and pathophysiological parameters that personalize the digital patient to reproduce the physiology and the pathophysiology of the subject.