Computational Modeling of Immune Signals Thesis Proposal Guide to This Proposal

Draft 12/14/11 Control of rejection is currently the primary technical challenge in transplantation, and is the principle reason for toxic immunosuppression treatments. This destructive inflammatory process involves a strong two-way cell-mediated cytotoxic response. The recipient immune system responds to the transplanted tissue, while the passenger immune cells of the allograft respond to the host tissues. Unless modulated or suppressed, transplanted tissue is inevitably destroyed by the host's immune response. Immunosuppressive drugs are effective in moderating the immune response, but in large doses also make recipients vulnerable to opportunistic infection and progressively destroy kidney function, causing significant long term morbidity. Excellent surgical methods and immunosuppresive agents exist. The primary obstacle to improving long-term transplant outcomes is establishing a personalized medication regimen optimizing the balance between immunosuppression and immune function-the individual minimum effective level of immunosuppression. Current methods to assess rejection rely on following indicators that grade rejection-associated tissue damage, and don't allow for optimally balanced dosing prior to that pathology. This thesis proposes to investigate computational methods for classifying immune signaling protein (cytokine) profiles to detect rejection well in advance of traditional methods by measuring proteomic immune signaling factors and using computational models to predict whether these factors are indicative of rejection. This investigation further seeks to determine, in several distinct tissue systems, whether cytokine patterns in inflammation associated with rejection can be distinguished from unspecific, wound-induced, or immunosuppressed cytokine patterns of inflammation. The proposed method will be validated in established small animal surgical models. Successful validation may allow for future follow-up clinical translation, potentially providing individual-optimized immunosuppression treatment plans. This thesis proposal is intended to be concise and easy to understand by readers from a wide range of backgrounds. An understanding of basic biology, immunology, and statistics is required. The introduction includes a discussion of the problem of advanced rejection detection, the current state of the art in the field, why the problem has not yet been solved, a brief look at what is needed to develop a solution, and the hypothesis of this thesis The literature review provides a listing of references from computer science and immunology that are relevant to this research, a more in-depth review of the literature that this thesis will rely upon most directly, and finally a brief review of alternative approaches that have been described in the literature. The Current Work section describes work that has been completed so far, and the 2 technical …