The identification of the molecular and cellular basis for adverse events observed in patients immunized against smallpox is of great public health interest. This is especially true today given efforts to defend the U.S. population and military against potential bioterrorism agents. We have shown recently that adverse vents following smallpox vaccination correlate with systemic cytokine patterns, suggesting a role for cytokines in the pathogenesis of adverse events. A challenge to further delineating the immunological mechanisms of adverse events is that cytokines rarely act in isolation to induce an immune response, but rather they work in a complex network to which immune system cells respond. Cytokines are small signaling proteins that integrate the activities of immune system cells. While it is often well understood how they act individually, the behavior of cytokines as part of a signaling network is less well known and likely depends on the nature of the infecting organism. We propose to develop and evaluate a comprehensive strategy to identify detailed kinetic cytokine network models associated with adverse events following smallpox vaccination. This strategy will be developed and evaluated using proteomic time-series data available from 103 volunteers that are part of an ongoing NIAID/NIH-sponsored trial to evaluate the Aventis Pasteur Smallpox Vaccine (APSV). We will develop software tools using machine learning algorithms to automatically discover cytokine signaling network models from observed time-series cytokine expression levels. Once the underlying cytokine network model has been estimated, our goal is to use the dynamic model as a simulation tool to suggest ways to create vaccines that minimize the risk of adverse events associated with vaccination. The software tools developed in this proposal will be generally applicable for biomedical research to understand the biochemical interactions in time-series data, and, thus, a useful and novel software package will be made available to the vaccine research community. The experience and knowledge gained during the collaboration on this important research problem in immunology coupled with the didactic and mentoring portions of the training program will create a firm foundation upon which I can develop and test significant hypotheses for future studies on the immunology of infectious diseases.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Mentored Quantitative Research Career Development Award (K25)
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Acquired Immunodeficiency Syndrome Research Review Committee (AIDS)
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Prograis, Lawrence J
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University of Tulsa
Biostatistics & Other Math Sci
Schools of Engineering
United States
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