Finding how viruses act in the very early stages of infection using data and computation
An integrative, data-driven, and computational approach to uncovering dynamic mechanisms of early viral infection
['FUNDING_OTHER'] · YALE UNIVERSITY · NIH-11159450
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
| Phase | ['FUNDING_OTHER'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | YALE UNIVERSITY (nih funded) |
| Locations | 1 site (NEW HAVEN, UNITED STATES) |
| Trial ID | NIH-11159450 on ClinicalTrials.gov |
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.
Who could benefit from this research
Good fit: 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.
Not a fit: 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.
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 performed: 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
NEW HAVEN, UNITED STATES
- YALE UNIVERSITY — NEW HAVEN, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: VAN DIJK, DAVID — YALE UNIVERSITY
- Study coordinator: VAN DIJK, DAVID
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 below.