Using new math to understand the body's daily clock and rhythms
DMS/NIGMS 2: Moment Kernel Machines for Decoding Complexity in Dynamic Biological Networks
This project develops a new mathematical method to understand and ultimately help people with sleep and daily rhythm problems.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Washington University NIH-funded |
| Lab location | 1 site (Saint Louis, United States) |
| Project ID | NIH-11192851 on NIH RePORTER |
What this research studies
From my point of view, researchers are building a new modeling tool called a Moment Kernel Machine to simplify and decode complex biological networks that control daily rhythms. They will work with data on circadian neurons and other rhythmic signals to make models that predict how the brain's clock responds to environmental cues. The team plans to use a mix of mathematical development, computer simulations, and biological data (likely from lab experiments) to find clear ways to change timing, like shifting chronotype or strengthening sleep consolidation. Over time these findings could guide new, more targeted ways to time light or other cues to improve sleep.
Who could benefit from this research
Good fit: People with chronic problems in sleep timing or circadian rhythm disorders (for example delayed or advanced sleep phase) would be the most relevant candidates for future trials informed by this work.
Not a fit: Patients whose sleep issues stem from unrelated medical causes (such as obstructive sleep apnea driven by airway anatomy) or who need immediate clinical interventions are unlikely to benefit directly from this basic-methods research.
Why it matters
Potential benefit: If successful, this could point to clearer, more precise ways to shift sleep timing and improve sleep consolidation for people with circadian rhythm problems.
How similar studies have performed: Mathematical and computational models have previously improved understanding of biological clocks, but the specific Moment Kernel Machine approach is novel and largely untested in people.
Where this research is happening
Saint Louis, United States
- Washington University — Saint Louis, United States (Active)
Researchers
- Principal investigator: Li, Jr-Shin — Washington University
- Study coordinator: Li, Jr-Shin
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.