How cells react to medicines and other protein signals
Understanding cascading cellular protein responses following multi-protein stimuli using network modeling and real-world evidence
This project builds computer models using lab and clinical data to predict how medicines and multiple protein signals change proteins inside cells and cause effects or side effects.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Los Angeles NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project ID | NIH-11171562 on NIH RePORTER |
What this research studies
If you share medical data or samples, researchers will combine protein network modeling with real-world clinical records to map how multiple proteins and drug treatments trigger cascading changes inside cells. They plan to use a context-specific interaction approach to quantify how much each downstream protein contributes to an outcome. The team will link lab-derived interaction maps with adverse drug event reports and other clinical data to improve predictions about which drug effects are likely. The work is mainly computational and data-driven and would use de-identified patient records or biospecimens rather than testing new treatments on participants.
Who could benefit from this research
Good fit: People who have experienced adverse drug reactions, have complex medication histories, or who are willing to share clinical records or biospecimens for research would be most relevant.
Not a fit: Patients whose conditions or medications are unrelated to the proteins studied, or who cannot or do not share their clinical data, are unlikely to see direct benefit.
Why it matters
Potential benefit: If successful, this work could help predict which medicines are more likely to cause side effects and guide safer treatment choices.
How similar studies have performed: Previous protein-network studies have linked drugs to downstream proteins and side effects but have generally been associative, so adding quantitative, context-specific models is a novel advance.
Where this research is happening
Los Angeles, United States
- University of California Los Angeles — Los Angeles, United States (Active)
Researchers
- Principal investigator: Wilson, Jennifer Lynn — University of California Los Angeles
- Study coordinator: Wilson, Jennifer Lynn
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.