Systems Immunology Approaches for Precision Medicine

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Virginia Tech


The mucosal immune system encompasses a wide array of interactions that work in concert to protect an individual from harmful agents while retaining tolerance to molecules, microbes, and self-antigens that present no danger. The upheaval in the regulation-response balance is a critical aspect in both infectious and immune-mediated disease. To understand this balance and methods of its restoration, iterative and integrative modeling cycles on the pathogenesis of disease are necessary. In this thesis, I present three studies highlighting phases of a systems immunology cycle. Firstly, the thesis provides a description of the construction of a computational ordinary differential equation based model on the host-pathogen-microbiota interactions during Clostridium difficile infection and the use of this model for the development of the hypothesis that host-antimicrobial peptide production may correlate with increased disease severity and promote increased recurrence. Secondly, it provides insight into the necessity of trans-disciplinary analysis for the understanding of novel molecular targets in disease through the immunometabolic regulation of CD4+ T cell by NLRX1 in inflammatory bowel disease. Third, it provides the assessment of novel therapeutics in disease through the evaluation of LANCL2 activation in influenza virus infection. In total, the computational and experimental strategies used in this dissertation are critical foundational pieces in the framework of precision medicine initiatives that can assist in the diagnosis, understanding, and treatment of disease.



Clostridium difficile, inflammatory bowel disease, influenza, computational modeling, immunology