Quantitative T cell Immunology
Date du début: 1 mai 2013,
Date de fin: 30 avr. 2017
The primary objective of this ITN is to train a new generation of European quantitative immunologists, who will be able to address challenges arising in Systems Immunology. This will be achieved with the proposed Network, Quantitative T cell Immunology (QuanTI), that will train 11 ESRs and 4 ERs, bringing together renowned experimental and theoretical scientists from private and academic institutions.Health and disease are regulated, to a large extent, by our immune system. A quantitative approach to Immunology must not only integrate a wide range of biological data but also increase our understanding of immunological processes at all levels of organisation. This will be achieved by exploiting state-of-the-art knowledge and technology of the academic and industrial partners. The scientific programme of the proposed Network consists of 6 research work packages and focuses on one of the current challenges in Systems Immunology: the dynamics of T cells in the periphery. Specifically the Network aims to provide a coherent multi-scale framework to: (i) the establishment and diversity of the T cell pool in the periphery, and (ii) the differentiation programme of T cell populations during adaptive immune responses. Synthesising models and immunological data poses a challenge that cannot be successfully managed by immunologists, mathematicians, computer scientists or physicists on their own. QuanTI will provide an excellent multi-disciplinary and inter-sectorial research environment to train a new generation of quantitative immunologists.The propose ITN will strongly advance the development of new quantitative concepts to characterise, and therapeutically modify, immune responses. In a wider context, QuanTI provides a model for a new, hands-on inter-disciplinary and inter-sectorial training programme in biomedical sciences that will catalyse the progress from a qualitative, molecule-oriented approach to a quantitative, systems-level research strategy.
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