Menopause, driven by ovarian aging and the depletion of ovarian reserve, marks the end of a woman’s fertility, and while many aspects of these processes are well understood, the overall dynamics remain unclear. A new study from Rice University researchers, published in Biophysical Journal on Feb. 10, introduces a novel approach to unraveling the complex patterns of ovarian aging using stochastic analysis, a mathematical approach that examines systems by evaluating all potential outcomes using random probability.

Led by Anatoly Kolomeisky, professor of chemistry and chemical and biomolecular engineering, the research team has developed a theoretical framework that quantitatively predicts menopause timing. By analyzing how ovarian follicles transition through different stages, the researchers’ model explains why menopause occurs and sheds light on individual variability and cross-population differences. These insights could improve fertility planning, inform health care decisions related to hormonal therapies and enhance our understanding of age-related health risks associated with ovarian aging.

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