# An integrative, data-driven, and computational approach to uncovering dynamic mechanisms of early viral infection

> **NIH NIH R35** · YALE UNIVERSITY · 2022 · $418,750

## Abstract

Project Summary
Cutting-edge technologies are generating large datasets across biological processes, including those following
viral infection and host responses. However, lack of computational tools that can extract meaningful insights,
and lack of ability to integrate information across different model systems and data modalities, are roadblocks to
deriving biological and mechanistic understanding of these processes. The recent rise of devastating viruses
including SARS family viruses reveals that a deeper, basic mechanistic understanding of viral infection is still
lacking. Specifically, new insights into early viral infection (asymptomatic replication phase) and early-responding
genes that govern infection and disease outcome are critical for understanding progression of infection and host
responses. During my postdoctoral research, I developed several widely-used algorithms for biomedical machine
learning and single-cell data analysis, and applied these to a broad range of biological systems, including
infectious disease. Here, I propose to develop a completely new approach that is founded in cross-modal
computational analysis and can be applied to dynamic processes across living systems. In this proposal, the
method will be trained upon and applied to uncovering virus infection dynamics. By leveraging single-cell
technologies, combinatorial CRISPR perturbation, and advanced machine learning, this new approach will learn
the gene regulatory logic that governs infection. By spanning model systems, I will extract information that can
be derived more cleanly from in-vitro systems, such as early infection timepoints. Through cross-integration of
these data with in-vivo data from mouse models we will bring the precision questions that can be asked in human
organoids together with the physiological environment of animal models, powering our ability to derive relevant
insights into gene networks underlying a complex, dynamic process. I will build a single-cell atlas of virus infection
and train a machine learning algorithm to obtain a predictive model of infection dynamics. By also integrating
data from single-cell combinatorial CRISPR perturbation, I will infer causal gene networks as well as synergistic
gene interactions that govern infection dynamics. This combination of advanced machine learning methods,
large-scale single-cell analysis, and gene perturbation data will allow discovery of the drivers of infection,
signatures of both susceptibility and protection, and gene networks that can ultimately be targeted for therapeutic
intervention. Synergistic gene interactions will open up future paths to potentially more effective, specific, and
even combinatorial therapies. The innovative coupling of computational methods and deep data collection to
extract information, particularly during early infection phases, has the potential to fundamentally change our
understanding of viral infections, as well as provide a framework that can be applied to a b...

## Key facts

- **NIH application ID:** 10468210
- **Project number:** 5R35GM143072-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** David van Dijk
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $418,750
- **Award type:** 5
- **Project period:** 2021-08-15 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10468210

## Citation

> US National Institutes of Health, RePORTER application 10468210, An integrative, data-driven, and computational approach to uncovering dynamic mechanisms of early viral infection (5R35GM143072-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10468210. Licensed CC0.

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