PROJECT ABSTRACT: Human immune T-cells are responsible for detecting and destroying infected or compromised cells. This is important for controlling pathogens and cancer. T-cells achieve this by recognising molecular markers on the surface of the target cell through their T-cell receptor (TCR). T-cells also display a variety of Co-signalling receptors that respond to other molecular markers which communicate information on the progression of infection or status of the target cell and its environment. T-cells therefore face the challenge of integrating all of this information to control their response. Failure to effectively regulate T-cell responses are associated with autoimmunity, the destruction of healthy tissue. Cancer cells can also hijack this co- signalling receptor system to evade T-cells and avoid detection/termination. This project aims to use mathematical modelling and machine learning, trained on a large database of T-cell co-signalling receptors, to sample the space of possible signalling networks that can explain how information is relayed and integrated in T-cells. This will allow us to better understand how T-cells appropriately recognize and neutralize compromised targets as well as propose novel synthetic biological circuits to further improve T-cell function in the context of fighting cancer and avoiding autoimmunity. BBSRC PRIORITY AREAS ADDRESSED: Data-driven biology, Synthetic Biology, Systems Approaches to the Biosciences, Technology Development for the Biosciences