You might be surprised to hear it, but you’ve got rhythm. (Yes, including you over there, who doesn’t even dare to drum your fingers on the steering wheel.) Heartbeats are the obvious example, but almost every other organ in our bodies also has a clock keeping time. These clocks control everything from cellular organization to neural activity, as well as the physical, mental, and behavioral cycles that stop and start at intervals through the day – cycles that constitute our circadian rhythms. Circadian rhythms are integral to both health and happiness—they’re like the body’s central pacemaker impacting sleep, metabolism, and even our risk of disease.
They’re also so intricately complicated that scientists have long wrestled with how to model them. Until now.
To better understand how to control these biological cycles, Daniel Forger, the Robert W. and Lynne H. Browne Professor in Science and professor of mathematics, has discovered how to model the region of the brain that governs them. Behind your eyes, the suprachiasmatic nucleus (SCN) is a group of about 20,000 neurons about the size of a computer chip. The SCN sets the body’s central clock according to how much light comes through the retina, sending signals to other parts of the brain and the rest of the body.
For decades, scientists thought the SCN functioned like a metronome, firing regular electrical pulses quickly during the day and slowly at night. But by building a mathematical model of the neurons, Forger discovered the SCN was actually firing in a complex pattern. His results were so surprising that it took a second study conducted by British colleagues that produced the same results for his findings to be accepted.
This wasn’t the first time Forger’s work has turned a prevailing theory on its head. In 2006, in collaboration with researchers at the University of Utah’s Huntsman Cancer Institute, Forger helped conduct a study on hamsters who had a genetically shortened daily rhythm. Forger’s computer simulations accurately predicted the impact the abbreviated rhythm had on the hamsters’ enzyme production, which reversed our understandings of how genetic mutations affect circadian cycles.
Daniel Forger has always been interested in rhythms. Before falling for the brain, he first learned to love the organ. (His favorite pieces continues to be Bach’s Trio Sonatas.) “I can’t tell you why so many people who end up being mathematicians are interested in music,” he says. But it was music and math that led Forger to his fascination with what he calls “the biological clocks problem.”
Forger first explored biological clocks as a graduate math student, but afterwards he was interested in running his own experiments, so he transferred to NYU’s biology department. That’s when he began studying fruit flies, trying to understand how their circadian clocks functioned at different temperatures. “I brought my appreciation for data with me,” he says.
Now, Forger is applying his expert calculations to apps and wearables. “It used to be that to study a human’s circadian clock, you had to spend thousands of dollars to bring them into the lab for days or weeks,” he explains. “Today, we can just download their data from their Apple Watch or Fitbit.”
In 2014, Forger and then-graduate student Olivia Walch (Ph.D. 2016) used these datasets to develop a free iPhone app, called Entrain. By entering your typical hours of sleep, your exposure to light and darkness, when you’re traveling and where, the app creates an individualized plan of when to expose yourself to light and darkness, in order to help you recover from jet lag more quickly. The app may do more than ease jet lag symptoms; regular sleep disruptions, like those frequent travelers or shift workers encounter, have been linked to depression, cancer, heart disease, and diabetes.
“This app was really about research,” Forger says. But he was surprised by its virality—it’s been downloaded over 200,000 times and in over 100 countries without any marketing. In 2016, he published a paper on the findings from the app data—like, for example, that women consistently schedule between ten and 30 minutes more sleep per night than men do.
Forger is not finished with wearable data. With the help of LSA Technology Services, he’s started creating one of the largest-ever databases on human sleep, which is already helping him assess how accurately wearables track our sleep.
His next study will be leading an international team, supported by a one-million-dollar grant from the prestigious Human Frontier Science Program—making Forger one of the only people to receive such a grant from the program more than once. The team’s goal, Forger says, is no less than “to understand the architecture of sleep.”
Along with collaborators at the University of Tokyo and the University of Zurich, the researchers will be trying to discover how the SCN regulates sleep cycles. Forger is also simultaneously involved in another collaborative sleep study with Srijan Sen, the Frances and Kenneth Eisenberg Professor of Depression and Neurosciences and associate professor of psychiatry, tracking thousands of doctors during their first year on the job. “They’re going through wild shift-work schedules,” Forger explains. “How does that play into the very large rates of depression in the medical field?” He hopes that learning how the SCN works will help scientists learn how to adjust it, helping ward off the negative impacts of disrupted sleep.
The big unanswered questions, Forger thinks, revolve around genetics and circadian rhythms. “Your clock is very different than mine,” he says. “How does that translate to the genome?” It’s why he predicts that circadian rhythms will turn out to be key to personalized medicine. But in the less distant future, he plans to release a number of other apps within the next year: one to increase the performance of athletes and another to mitigate the side effects of chemotherapy for cancer patients. And, somehow in the midst of all of his other work, Forger has made time for his original passion. He’s currently working with U-M’s School of Music, Theater, and Dance to use mathematical analysis on Bach sonatas to help predict performance—figuring out how, he says, “data can make me a better performer.” That’s all any rhythm-chaser can hope for.