Taking Infertility out of the Equation

Polycystic Ovarian Syndrome (PCOS) is one of the leading causes of infertility in women of childbearing age.  But finding the cause is difficult because of female fertility’s complex interplay of hormones and women’s individual cycles.

So what is one way the medical community can address the problem?

If you answered “with math,” you get a gold star.

Dr. Jim Selgrade is a mathematician who has spent the better part of two decades working out a mathematical model of a “normal” menstrual  cycle.  He created a mathematical behemoth which uses 14 to 16 differential equations and 40 to 50 parameters, but the model keeps changing because, as he puts it, “there always seems to be another hormone involved.”

Once he finished the model of a normal cycle,  Selgrade  added or changed certain parameters in order to perturb it, or make it match the abnormal cycle of someone with PCOS. In the model, excess estrogen seemed to be the culprit, and progesterone helped return things to equilibrium. The advantage to this process is that theories can be tested quickly, without relying on taking invasive daily samples from human patients.  The information can also help endocrinologists develop more effective courses of treatment for their patients.

As for Selgrade, he is interested in the connection between PCOS and insulin sensitivity.  “The sensitivity shows up in women with PCOS, but they don’t show diabetes,” Selgrade says. “Why does this affect the ovaries? We’re trying to figure out if there’s a connection to testosterone, one of the building blocks of estrogen. Testosterone is necessary, but if you have too much you can get estrogen at the wrong time during the cycle, which results in infertility.”

“Of course,” he adds, “this means we’ll probably have to put more variables into the model.”