The goal of our proposed research is to utilize mathematical modeling (in concert with experiments performed in Projects I and II) to gain insight into the integration of factors that shape the immune response to influenza A virus (IAV) infection. Since the immune system response is an emergent response resulting from many different cells, the challenge is to understand the effects of molecular and single cell stochasticity on immune processes unfolding at the larger scale of infected tissue. Results of experiments performed on different length and time scales, with multiple sources of heterogeneity, and engineered viral probes, to elucidate mechanisms of immune response, from single cell response variability to multi-cell type integration, in human lung epithelial cells and primary dendritic cells (DC), provide a unique opportunity to refine and validate models that can be used predictively. We will implement a modular approach using fine-grained models, which are integrated into multi-scale models after coarse-graining. The models range from deterministic for cell populations, to stochastic for single cells, to agent-based. They will constitute an in silico laboratory that can yield new insight, guide additional cycles of experimental work and theoretical refinement, and advance the understanding of the emergent immunological responses to IAV infection. Specifically, we will develop models (1) for human tracheobronchial epithelial (HTBE) cells to identify and elucidate mechanisms that determine the spread of infection, predict the effectiveness of different treatment protocols and quantify the information transmitted via secreted factors under different conditions, (2) for human primary lung CD1c+ DC to elucidate the mechanisms contributing to variability in cell surface marker expression, pathway activation and cytokine production that affect the development of adaptive immunity, and (3) for human lung tissue to understand the influence of the microenvironment on the responses of epithelial and dendritic cells to IAV infections.
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