Model Hybridisation and Visualisation Techniques for the Investigation of Complex Disease Processes

Tertiary lymphoid tissues (TLT) develop ectopically in most autoimmune disorders, their presence is strongly correlated with disease prognosis. The autoantibody response driven by germinal centres within TLT is an important driver of autoim- munity in Sjörgen’s syndrome, for which there currently lacks any adequate therapy beyond palliative care. The cellular and molecular processes driving lymphoid neogenesis have remained elusive despite intense scrutiny utilising gene knock-out mice, lineage specific reporter mice, gene expression analysis, immunohistochemistry and flow cytometry. These approaches permit a thorough understanding of the formation of secondary lymphoid tissues. However, the mechanisms driving the formation and function of tertiary lymphoid tissues have proven to be more controversial and enigmatic, principally due to differences between experimental models and human disease pathology.

This thesis describes the development of a novel model hybridisation framework and visualisation tools, permitting the development of predictive multi-scale models. A model of Sjögren's syndrome pathogenesis was developed from human and murine in vivo and in vitro data. By applying machine learning (ML) methods, including manifold learning and perceptron networks, we are able to identify potential avenues for therapeutic interventions. This approach identified a novel therapeutic approach for Sjögren's syndrome in silico, a prediction that was subsequently validated in vivo utilising a murine disease model.