Mathematical Models of Metabolic Sex Differences and Oxidative Stress in Health and Disease

Abstract

Sex differences are observed across many physiological systems and contribute to differential disease susceptibility and clinical outcomes in males and females. These differences are often mediated by sex hormones such as estrogen, which regulate enzymes and metabolic pathways. Despite increasing recognition of sex as a biological variable, the underlyingmechanisms by which sex hormones generate sex- and menopausal-specific physiological outcomes remain incompletely understood. The central problem addressed in this dissertation is how hormonal regulation of biochemical pathways gives rise to observed sex differences in metabolism, oxidative stress, and disease risk.This dissertation uses mechanistic mathematical modeling, grounded in biochemical kinetics and informed by experimental and clinical data, to investigate hormone-dependent regulation in metabolic systems. Models of liver cell metabolism are developed to examine the effects of estradiol on one-carbon metabolism and the transsulfuration pathway. Usingthese models, this work explains observed sex differences in the antioxidant glutathione (GSH) and demonstrates how the nonmonotonic response of GSH to estradiol leads to differential homocysteine responses to estrogen supplementation in pre- and postmenopausal women. The models further demonstrate the relative stability of GSH across the menstrual cycle in premenopausal women. In addition, the role of estradiol in regulating oxidative stress, a state characterized by increased reactive oxygen species (ROS) and reduced antioxidant capacity, is investigated, reconciling seemingly contradictory clinical observations and explaining the attenuation of sex differences in Cystathionine β-Synthase Deficiency.In parallel, motivated by sex differences in neurological disorders, we develop mathematical models of neurotransmitter volume transmission and comodulation to study interactions among serotonin, dopamine, histamine, and other neuromodulators in the brain. These models elucidate how neurotransmitter interactions contribute to homeostasis andidentify serotonin, and thus depression, as a potential early biomarker for Parkinson’s disease.