MS08 - OTHE-1
Student-Alumni Council Room (#2154) in The Ohio Union

Mathematical Approaches to Support Women’s Health

Friday, July 21 at 10:30am

SMB2023 Follow Friday during the "MS08" time block.
Room assignment: Student-Alumni Council Room (#2154) in The Ohio Union.

Ashlee N. Ford Versypt

Description:

In June 2022, an event titled Collaborative Workshop for Women in Mathematical Biology: Mathematical Approaches to Support Women’s Health was held in conjunction with the University of Minnesota's Institute for Mathematics and its Application. The event hosted project teams involving women at different career stages, from early career mathematicians to leaders in the field, to bolster leadership among senior mathematical biologists and data scientists, and to provide mentoring for early career mathematicians. The project teams each worked on a mathematical biology topic related to women's health during the workshop and then continued their collaboration. This session highlights the contributions of four of the project teams.

Ying Zhang

Brandeis University (Mathematics)

"Studying the Effects of Oral Contraceptives on Coagulation Using a Mathematical Modeling Approach"

The use of oral contraceptives (OCs) is known to increase the risk of thrombosis, but the mechanisms underlying this risk and the determinants of the tests that assess this risk are not fully understood. In this study, we used a mathematical model to study the effects of the OC levonorgestrel (lev) on blood clotting. Lev is reported to change the plasma levels of blood clotting factors. The model simulates coagulation reactions in a small injury under flow, takes clotting factors as inputs and outputs time courses of the coagulation enzyme thrombin. We created a virtual patient population with factor levels before and after lev use that were based on published patient data. After analyzing the simulated thrombin, we found that changes in factor levels due to lev increased the amount and speed of thrombin generation for all virtual patients. This suggested that the factor level changes alone can heighten the prothrombotic state of the model system. We extended the model to include generation of the inhibitor APC so we could test the effects of lev on the systems’ sensitivity to APC. In line with literature reports, the use of lev decreased the APC sensitivity, which correlates with increased thrombosis risk.
Additional authors: Amy Kent, University of Oxford; Karin Leiderman, University of North Carolina at Chapel Hill; Anna C. Nelson, Duke University; Suzanne Sindi, University of California, Merced; Melissa M. Stadt, University of Waterloo; Lingyun (Ivy) Xiong, University of Southern California.

Susan Rogowski, Alejandra D. Herrera-Reyes, and Yena Kim

Florida State University (Mathematics)

"Parameter Estimation for COVID-19 SVIRD Model Using Predictor-Corrector Algorithm"

Stable parameter estimation is an ongoing challenge within biomathematics, especially in epidemiology. Oftentimes epidemiological models are composed of large numbers of equations and parameters. High dimensionality makes classic parameter estimation approaches, such as least square fitting, computationally expensive, and the presence of observational noise and reporting errors that accompany real-time data can make these parameter estimation problems ill-posed and unstable. The recent COVID-19 pandemic highlighted the need for efficient parameter estimation tools. In this paper, we develop a modified version of a regularized predictor-corrector algorithm aimed at stable low-cost reconstruction of infectious disease parameters. This method is applied to a new compartmental model describing COVID-19 dynamics, which accounts for vaccination and immunity loss (from vaccinated and recovered populations). Numerical simulations are carried out with synthetic and real data for COVID-19 pandemic. Based on the reconstructed disease transmission rates (and known mitigation measures), observations on historical trends of COVID-19 in the states of Georgia and California are presented. Such observations can be used to provide insights into future COVID policies.
Additional authors: Yena Kim; Alejandra D. Herrera-Reyes; Alexandra Smirnova; Ruiyan Luo; Diana White

Yeona Kang

Howard University (Mathematics)

"Extended-release Pre-Exposure Prophylaxis and Drug Resistant HIV"

The pharmacologic tail of long acting cabotegravir (CAB-LA, injectable PrEP) allows months-long intervals between injections, but it might encourage the growth of drug-resistant HIV strains during the acute infection stage. We present a within-host, mechanistic Ordinary Differential Equation model of the HIV latency and infection cycle in CD4+ T-cells to investigate. We develop a pharmacokinetic/pharmacodynamic model for long acting cabotegravir (CAB-LA, injectable PrEP) to relate the inhibitory drug response to the drug concentration in plasma as well as rectal, cervical, and vaginal fluids and tissue. After validating our model against experimental results, we build in-silico trials. First, we separately administer CAB-LA to the in-silico macaque and human patients prior to and post-SHIV/HIV exposure, to observe SHIV and HIV infectivity dynamics, respectively. The model does not include a mechanism for CAB-LA to generate drug-resistant HIV mutations, but we observe the result when mutations arise naturally. We find CAB-LA may encourage the drug-resistant strain to grow and to outcompete the wild-type in the acute stage. The in-silico trials show that the level of drug resistance, the effectiveness of CAB-LA against the mutations, and the degree of fitness for the mutant strain of virions to infect T-cells determine the course of the drug-resistant strain.
Additional authors: Yanping Ma (Loyola Marymount University); Angelica Davenport (Florida State University); Jennifer Aduamah(University of Delaware), Kathryn Link (Pfzier) and Katharine Gurski (Howard University)

Rayanne Luke

Johns Hopkins University (Applied Mathematics and Statistics)

"Towards a mathematical understanding of ventilator-induced lung injury in preterm rat pups"

Approximately 1 % of infants are born extremely preterm and are prone to respiratory distress. Typical treatments are less effective for this group and invasive mechanical ventilation applied as a last resort causes trauma, leading to ventilator-induced lung injury (VILI). Further, maternal infection can cause prenatal and neonatal lung infection, inflammation, and often very preterm birth. Inflammation is expected to stiffen the lungs, but exceptions occur, and a complete picture of the mechanisms of stiffening remains unknown. To better understand these mechanisms, we present an application of parameter estimation to a compartment model of pressure-volume lung dynamics along with newly designed image analysis metrics. We also apply optimization to data from a neonatal rat model and identify key parameter differences between healthy and unhealthy groups that may suggest the mechanisms of VILI in infected respiratory systems. Finally, combined analyses of our strategies identify correlations between inflammatory markers and model parameters with no analog in the data, suggesting that mathematical approaches provide an important path towards understanding VILI and infection.
Additional authors: Gess Kelly; Melissa Stoner; Jordana Esplin O'Brien; Sharon R. Lubkin; Laura Ellwein Fix

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Annual Meeting for the Society for Mathematical Biology, 2023.