Inside the wavering mind of an NK cell: Mathematical Modeling of NK cell Activation 2256230

Abstract Introduction The paradigm of NK cell activation is the “Missing-Self Hypothesis”; NK cells kill cells that lack MHC. When considering a single interaction between an NK cell and a target cell, the “Missing-Self Hypothesis” offers a plausible explanation for NK cell activation. However, an NK cell’s interaction with a target cell is not an isolated event. Experimental studies have shown that NK cells in an MHC-deficient environment, fail to lyse cells lacking MHC. Curiously, a study by the Yokoyama lab found that when 10% of cells in a well-mixed environment lacked MHC, the NK cells failed to lyse most of the cells. Our hypothesis is that target cell recognition is less about the balance of the presence or absence of markers, and more so about detecting outliers in an environment. Furthermore, we propose that NK cells will tolerate a small population of cells without MHC only if the cells lose MHC slowly. Methods We developed a mathematical model of how NK cells assess and learn from their environment to effectively lyse harmful outliers. To capture NK cell learning, an NK cell estimates the environment’s MHC distribution by interacting with target cells. An NK cell lyses a target cell if its MHC expression is an outlier based on its estimated MHC distribution. Results To model an infection, where the infected cells have lower MHC expression, preliminary results show that slightly varying the mean of the infected cell population results in either complete elimination or tolerance of the infected cells. To experimentally test our model predictions, we have initiated a collaboration with the Weis lab at the Huntsman Cancer Institute. Conclusion Our preliminary results suggest NK cells may be vulnerable to “slow growing danger” which makes detecting outliers challenging. We are currently creating a dynamic model to explore the rate of MHC suppression and viral spreading on NK cell reactivity. Funding Source n/a Topic Categories Computational and Systems Immunology (COMP)