Quantifying the Generation of T Cell Immunity using a Systems Biology Approach.

The immune system is our defense against pathogens. Quantitatively predicting its response to foreign stimulation is key toward developing tools to interfere with or prevent infection (e.g. vaccines and immunotherapies). I use a systems biology approach and develop computational models describing dynamics occurring within lymph nodes, sites where activated immune cells are generated. These effector cells circulate out into blood and to sites of infection participating in immunity. I both quantitatively and qualitatively study dynamics of immune cells during a generalized infection as well as during infection with Mycobacterium tuberculosis (Mtb). The models predict that their 3-dimensional configuration enables the lymph nodes to support rare antigen-specific T cells to efficiently search for antigen-bearing dendritic cells, and this efficiency is not reduced when the precursor frequency increases in a wide range. The models also predict strategies to manipulate the differentiation of immune cells to maximize specific subtypes of T cells populations, depending on different immunomodulation goals. When coupled with Mtb infection models, our models are able to assist vaccine design by finding correlations between immune cell subsets and protection against Mtb, and also help identify mechanisms controlling different disease outcomes at host level.PhDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113563/1/changgon_1.pd