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

> **NIH NIH R01** · RESEARCH INST NATIONWIDE CHILDREN'S HOSP · 2022 · $364,877

## Abstract

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.

## Key facts

- **NIH application ID:** 10246263
- **Project number:** 5R01AI143740-03
- **Recipient organization:** RESEARCH INST NATIONWIDE CHILDREN'S HOSP
- **Principal Investigator:** Jayajit Das
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $364,877
- **Award type:** 5
- **Project period:** 2019-09-05 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10246263

## Citation

> US National Institutes of Health, RePORTER application 10246263, Developing a predictive in silico toolkit for modeling NK cell responses against RNA virus infections (5R01AI143740-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10246263. Licensed CC0.

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