Natural Killer (NK) cells are lymphocytes of the innate immune system that defend us by lysing tumor and pathogen-infected cells. Recent experiments showed generation of long-lived "memory" NK cells in mouse and humans challenged by viral infections (such as CMV), similar to that of memory lymphocytes in the adaptive immune system. We seek to develop a quantitative mechanistic framework with predictive powers for signaling and activation in NK cells including "memory" NK cells in mice and humans by using a synergistic combination of in silico modeling and wet lab experiments. The in silico modeling rooted in statistical physics, non-linear dynamics, and multivariate statistics will be combined with standard biochemical and multi-parametric single cell mass cytometry (CyTOF mass cytometer) experiments on engineered human NK cell lines (NKLs), and naive and "memory" NK cells from mice and humans. We will investigate three overlapping aims:
Aim 1 : Develop a quantitative mechanistic framework for analyzing signaling and activation of NK cells stimulated by activating and inhibitory CMV-encoded ligands.
Aim 2 : Determine differences in signaling mechanisms between naive and "memory" NK cells in the mouse.
Aim 3 : Determine differences in signaling mechanisms between mouse and human "memory" NK cells. At the successful completion of the aims we will have a quantitative predictive mechanistic framework for studying NK cell signaling in mice and humans. The systems level knowledge gained from the proposed projects can also be applied in understanding mechanisms of activation regulated by heterogeneous receptor ligand interactions in other immune cells.
The developed in silico models with predictive powers will help generate new hypotheses and design experiments for studying NK cell activation. The basic mechanisms that underlie development and response of naive and memory NK cells in humans are likely to provide guidelines to manipulate those mechanisms for therapeutic applications.