Data-Driven Computational Approaches for Hormone-Regulated Vaginal Tissue Remodeling Across the Lifespan - Hormonal changes associated with puberty, reproductive maturity, pregnancy, postpartum recovery, and menopause affect virtually every woman. The vagina is one of the most hormone-responsive organs in the body, undergoing continuous adaptation that influences pelvic support, continence, sexual health, reproductive function, and response to therapy. Yet the mechanisms linking hormone dynamics to tissue adaptation remain largely unknown, leaving clinicians without the predictive tools needed to identify women at risk, anticipate disease progression, or tailor therapies to individual hormonal states. As a result, millions of women continue to experience pelvic floor disorders, childbirth related injury, recurrent vaginal infections, sexual dysfunction, and genitourinary syndrome of menopause, conditions that often persist for years or decades and substantially impair quality of life, intimate relationships, and healthy aging. The objective of this interdisciplinary project is to establish the first predictive, data-driven, physics-informed computational framework capturing how hormone homeostasis governs the structural and functional adaptation of vaginal tissue across the lifespan. Unlike existing computational models that are calibrated for fixed physiological states and treat vaginal tissue as a static biological system, the proposed framework represents the vagina as a dynamically evolving hormone-responsive organ. The central hypothesis is that hormone-driven tissue remodeling can be learned from experimental and clinical data and embedded within a physics-based computational framework to predict tissue behavior across physiological states and therapeutic interventions. This project has three synergistic aims that integrate hormone-dynamics modeling, tissue remodeling, and computational simulation. Together, these aims address distinct scientific challenges while providing complementary information that strengthens and informs the overall framework. Aim 1 will develop mathematical and data-driven models that characterize reproductive hormone dynamics and learn quantitative relationships between hormone trajectories and tissue adaptation. Mechanistic and stochastic representations of hormone regulation will be integrated with uncertainty-aware modeling approaches to identify hormone-driven evolution laws governing tissue microstructure across reproductive maturity, pregnancy, postpartum recovery, and menopause. Aim 2 will integrate the learned evolution laws into a nonlinear growth and remodeling framework that describes how vaginal tissue structure and mechanical behavior evolve under changing hormonal conditions. Tissue structural organization and constitutive parameters will be treated probabilistically and calibrated using experimental data. Aim 3 will develop a computationally efficient platform that integrates finite element modeling, reduced order modeling, machine learning surrogates, and uncertainty propagation to enable rapid, high-fidelity simulation of hormone-regulated tissue adaptation and therapeutic response. The ultimate goal of this project is to establish predictive computational tools that enable rapid and accurate evaluation of therapies for vaginal health. The resulting framework will create a new paradigm for in silico evaluation and optimization of hormone replacement therapies, biomaterials, regenerative approaches, and device-based interventions, accelerating therapeutic development, reducing reliance on costly experimental studies, and advancing women’s health through more effective and targeted treatments.
