Finding how viruses act in the very early stages of infection using data and computation
Official title: An integrative, data-driven, and computational approach to uncovering dynamic mechanisms of early viral infection
Funded at Yale University · NIH-11159450 · Data updated 2026-11-12
This project creates computer tools that use single-cell data and gene‑editing experiments to find which genes control early viral replication and host responses, aiming to help people at risk from viruses like coronaviruses.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | August 15, 2021 |
| Project end | July 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $418,750 |
| NIH project number | 5R35GM143072-05 |
| Project ID | NIH-11159450 on NIH RePORTER |
Who could benefit from this research
✓Could help
People recently exposed to or in the very early stages of a viral infection, or those willing to donate relevant samples, would be the most relevant candidates for participation in follow-up or related studies.
✕Less likely to help
Patients with conditions unrelated to viral infection or those needing immediate clinical treatment are unlikely to receive direct benefit from this basic, lab‑based research.
What this research studies
Researchers will combine single-cell measurements, combinatorial CRISPR gene perturbations, and advanced machine learning to map how genes change during the asymptomatic, early phase of viral infection. The team will integrate different kinds of data and model systems to identify common gene regulatory programs that drive infection and host response. Computational models will be trained on these cross-modal datasets to predict which genes control early viral behavior. Results are intended to point to targets for diagnostics or therapies that could stop viruses before severe disease develops.
Why it matters
Potential benefit: If successful, this work could reveal early-response genes or pathways that become targets for new diagnostics or treatments to prevent infections from progressing to severe illness.
How similar studies have gone: Previous single-cell and CRISPR studies have identified host factors for infection, but combining cross-modal machine learning with combinatorial perturbations to map early dynamic mechanisms is a relatively novel approach.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Van Dijk, David — Yale University
- Study coordinator: Van Dijk, David
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.