In the middle of your brain, there’s a personal assistant the size of a grain of rice. It’s a group of about 20,000 brain cells that keeps your body’s daily schedule.

Partly in response to light signals from the retina, this group of neurons sends signals to other parts of the brain and the rest of the body to help control things like sleep, metabolism, immune system activity, body temperature and hormone production on a schedule slightly longer than 24 hours.

Daniel Forger, a mathematics professor at the University of Michigan who uses math to study biological processes, wants to understand this brain region, called the suprachiasmatic nucleus (SCN) in excruciating detail. He is building a mathematical model of the entire structure that he thinks will shed important light on our circadian rhythm, and perhaps lead to treatments for disorders like depression and insomnia, and even diseases influenced by the internal clock like heart disease, Alzheimer’s and cancer.

“I think we’re going to be able to have a very accurate model of the circadian rhythm, all the key proteins, all the electric activity of all 20,000 neurons,” he says. “We’ll be able to track all of them for days on a timescale of milliseconds.”

Forger has already taken a few steps down this path and found some surprises. In a paper published in a recent issue of the journal Science, Forger, along with colleagues Mino Belle and Hugh Piggins of the University of Manchester in England and others, showed that the firing pattern of the time-keeping neurons in the SCN was not at all what researchers had long thought.

Researchers who studied the electrical activity of the SCN had believed that the neurons there helped the body keep time by sending lots of electrical signals during the day, and then falling silent at night. Makes sense. Lots of non-teenage creatures are active during the day and quiet at night.

But when Forger used experimental data to build a mathematical model of the electrical activity, he calculated that there should be lots of activity at dawn and dusk, and a state of “quiet alertness” during the day. That didn’t make much intuititve sense. Worse, the cellular chemistry during this quiet period that Forger’s model predicted would, in normal cells, lead quickly to cell death.

“Skepticism doesn’t begin to describe what I was met with,” says Forger. “Experimentalists told me, ‘That’s crazy.’”

Researchers in the field simply assumed Forger’s model was wrong. Forger refined it and reworked it, and got similar results. Meanwhile, his British colleagues began to probe the fact that there are two types of cells in the SCN, ones that have very strong molecular clocks and do the timekeeping, and others that behave more like normal brain cells.

While previous researchers had recorded the activity of all of the cells in the SCN, Belle and Piggins were able to set up an experiment using mice that would record only the activity of the clock cells. (Mammalian central clocks all seem to work the same way.) Their experimental results matched Forger’s predictions.

“When we got the results, they were shocking,” Forger says. “They were dead on.”

The cells in the SCN that don’t keep time followed the pattern researchers were familiar with, active during the day, quiet at night. The time-keeping cells went bananas in the morning and at night, but then during the day they stayed in a bizarre state of excitement during which they emitted very few impulses. Why these cells can stay alive in this state remains a mystery.

Forger has been down this path before. Another study of his, published in 2007, reversed the thinking on how gene mutations affect circadian rhythms within cells.

Scientists studying a hamster that had a malfunctioning internal clock (its daily rhythm lasted 20 hours instead of 24) found that it had a mutation in a gene called tau. The fuzzy rodent was given the extremely appropriate name “Tau Mutant Hamster.”

They thought Tau Mutant Hamster’s mutation caused an enzyme that helped cells keep time to be less active. Forger predicted that it would instead make the enzyme more active. Experiments later proved he was right.

Now Forger is turning his attention to the entire SCN. He thinks that math is the only way we can understand the sheer complexity of what is happening–neurotransmitters coming and going, protein clocks being built up and broken down, electricity bouncing around.

“To piece it all together, you need more than intuition,” he says. “You need math to see what’s going on.”

Originally Posted on Forbes by Jonathan Fahey on 10.15.09