Developing a predictive in silico toolkit for modeling NK cell responses against RNA virus infections

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Developing a predictive in silico toolkit for modeling NK cell responses against RNA virus infections Mathematical modeling of spatiotemporal processes involved in signaling and activation of immune cells (e.g., T cells) of adaptive immunity have provided novel mechanistic insights into the complex system. Natural Killer (NK) cells are part of the innate immune system which share key similarities and differences with lymphocytes of the adaptive immune system. NK cells provide important resistance against globally important RNA virus (e.g., HCV, DENV, HIV, EBOV, and ZIKV) infections. However, quantitative modeling aimed at deciphering mechanisms that underlie NK cell signaling and activation is under-developed leading to poor understanding of many key results pertaining to NK cell responses to these important viral pathogens. Unlike cells of the adaptive immune system NK cells do not have a single antigen specific triggering receptor, but sum signals derived from activating and inhibitory receptors to determine whether or not effector functions are initiated. A complex signaling network underpins the transmission of these receptor:ligand interactions. The layering of this network includes signals transmitted directly by cell surface receptors, e.g., inhibitory killer cell immunoglobulin-like receptors (KIRs) and signals transmitted via adapter molecules. Our work has focused on the KIR and NKG2-family of receptors as these are a critical component of NK cell protection against globally important RNA virus infections. It is challenging to glean mechanisms that underlie activation of NK cells by these diverse receptor:ligand system using experimental approaches alone due to the large diversity of ligand- receptor interactions, nonlinear signaling reactions, non-trivial spatiotemporal changes in KIR and NKG2-family receptor clustering, and, interactions between different HLA-peptide ligands. To address this challenge we will develop an in silico toolkit by combining spatially resolved mechanistic and data-driven in silico models with bench experiments probing activation of NK cell lines and primary human NK cells expressing specific KIR and NKG2-family receptors that are stimulated by a novel library of peptides derived from globally important RNA viruses (HCV, DENV, EBOV, ZIKAV), HCV and DENV replicons, and, artificial sources. The in silico models will be rooted in statistical physics, statistics, information theory, and non-linear dynamics, and, the wet-lab experiments will be based on live-cell imaging, standard immune-assays, flow cytometry, and confocal imaging. We will pursue three aims:(1) Develop a quantitative toolkit to analyze peptide modulation of KIR and NKG2 receptors. (2) Quantitative modeling of NK cell response to globally important RNA virus (HCV, DENV) infections in vivo. (3) Quantitative determination of the roles of HLA allelic diversity in NK signaling and activation.