Systems Biology of T-cell Activation in Health and Disease

T-cell activation, whether induced by pathogens or auto-antigens, is a complex process relying on multiple layers of tightly controlled intracellular signalling modules that form an intricate network. Defects in this network can cause severe and chronic disorders such as autoimmune diseases. Although 5% of the population suffer from these diseases, only a few therapeutic treatments are available. To a large extent this is attributed to the lack of systems-level insights, which would provide concepts of how to modulate T-cell activation. The SYBILLA project groups 14 partners from 9 different EU countries, including 3 SMEs. Through a multidisciplinary effort it aims to understand at the systems level, how T-cells discriminate foreign from auto-antigens. Towards this goal, a transgenic mouse system will be used as a tractable physiological model. Data will be validated in human T-cells and a humanised mouse model for multiple sclerosis. SYBILLA will develop technological and mathematical tools to generate and integrate high-density quantitative data describing T-cell activation. Proteomics, transcriptomics, metabolomics, imaging and multiplexed biochemical techniques will be applied to obtain holistic maps of T-cell signalling networks and to achieve a quantitative understanding of the network and its regulation in response to different inputs. Building upon our existing network model, constant iterations will be used to develop more robust dynamic models to describe the network s response to perturbations. This will culminate in the generation of a Virtual T-Cell, allowing computer simulation to refine the predictability of physiological and pathophysiological reactions. SYBILLA s impact on EU biopharmaceutical competitiveness will be enormous through identification of new pharmacologic targets, optimised prediction of immunomodulatory drug efficacy, discovery of new concerted biomarkers and improvement of personalised medication for treating autoimmune diseases.