Collaborative Workshop for Women in Mathematical Biology: Mathematical Approaches to Support Women’s Health
Advisory: The deadline for application is March 18, 2022.
Organizers
- Ashlee Ford Versypt — University at Buffalo (SUNY)
- Rebecca Segal — Virginia Commonwealth University
- Blerta Shtylla — Pfizer
- Suzanne Sindi — University of California, Merced
This five-day workshop focuses on collaborative research, in small groups of women, each group working on an open problem in a particular area of mathematical biology. Each group will include 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. Complementing the research time there will be activities engaging all participants, including career panels, discussions and building community.
Women researchers from underrepresented groups, working at universities with a teaching focus and small colleges, and those isolated geographically from potential collaborators, are especially encouraged to apply.
Schedule
Monday, June 20, 2022
- 8:30–9:30am — Workshop Check-in (UnitedHealth Group-Minnetonka)
- 9:30am–12:00pm — Intro and Project Overviews
- 12–12:30pm — Quick Group Meeting
- 12:30–1:30 pm — Lunch (on site)
- 1:30–5pm — Group Research
- 5–7pm — Group Research
Tuesday, June 21, 2022
- 9am–12pm — Research
- 12–1pm — Lunch
- 1–2:30pm — Career Panel
- 2:30–5:30pm — Research
Wednesday, June 22, 2022
- 9am–12pm — Research
- 12pm–1pm — Lunch
- 1–2pm — Pair and Share
- 2–5:30pm — Research
Thursday, June 23, 2022
- 9am–12pm — Research
- 12–1pm — Lunch
- 1–5:30pm — Research
Friday, June 24, 2022
- 8:30–11:30am — Project Presentations / Wrap-up
Participants
| Name | Department | Affiliation |
|---|---|---|
| Jennifer Aduamah | Department of Mathematical Sciences | Rochester Institute of Technology |
| Mukti Chowkwale | Department of Biomedical Engineering | University of Virginia |
| Morgan Craig | Department of Applied Mathematics and Statistics | University of Montreal |
| Angelica Davenport | Department of Mathematics | Florida State University |
| Lisette de Pillis | Department of Mathematics | Harvey Mudd College |
| Laura Ellwein Fix | Department of Mathematics and Applied Mathematics | Virginia Commonwealth University |
| Ashlee Ford Versypt | Department of Chemical and Biological Engineering | University at Buffalo (SUNY) |
| Katharine Gurski | Department of Mathematics | Howard University |
| Alejandra Donaji Herrera Reyes | School of Mathematical Sciences | University of Nottingham |
| Adrianne Jenner | School of Mathematics and Statistics | Queensland University of Technology |
| Rachel Jennings | UHG Research & Development | UnitedHealth Group |
| Yeona Kang | Department of Mathematics | Howard University |
| Narges Kelly | Department of Physics | Brandeis University |
| Amy Kent | Mathematical Institute | University of Oxford |
| Ruby Kim | Department of Mathematics | Duke University |
| Yena Kim | Department of Mathematics | Hawaii Pacific University |
| Karin Leiderman | Department of Applied Mathematics and Statistics | Colorado School of Mines |
| Kathryn Link | Department of Mathematics | University of California, Davis |
| Samantha Linn | Department of Mathematics | The University of Utah |
| Sharon Lubkin | Department of Mathematics | North Carolina State University |
| Rayanne Luke | Department of Applied Mathematics and Statistics | Johns Hopkins University |
| Ruiyan Luo | Population Health Sciences | Georgia State University |
| Yanping Ma | Department of Mathematics | Loyola Marymount University |
| Anna Nelson | Department of Mathematics | Duke University |
| Jordana O'Brien | Department of Applied Mathematics | Rochester Institute of Technology |
| Janet Oladejo | Pure and Applied Mathematics | Ladoke Akintola University of Technology |
| Lucy Oremland | Department of Mathematics and Statistics | Skidmore College |
| Jenna Ott | Department of Chemical and Biological Engineering | Princeton University |
| Susan Rogowski | Department of Mathematics | Florida State University |
| Rebecca Segal | Department of Mathematics | Virginia Commonwealth University |
| Blerta Shtylla | Department of Early Clinical Development | Pfizer |
| Robyn Shuttleworth | Department of Biology | University of Saskatchewan |
| Suzanne Sindi | School of Natural Sciences | University of California, Merced |
| Alexandra Smirnova | Department of Mathematics and Statistics | Georgia State University |
| Melissa Stadt | Department of Applied Mathematics | University of Waterloo |
| Melissa Stoner | Department of Mathematical Sciences | Salisbury State University |
| Deborah Sundal | UHG Research & Development | UnitedHealth Group |
| Diana White | Department of Mathematics | Clarkson University |
| Lingyun Xiong | Department of Quantitative and Computational Biology | University of Southern California |
| Sarah Youssef | UHG Research & Development | UnitedHealth Group |
| Wenjing Zhang | Department of Mathematics and Statistics | Texas Tech University |
| Ying Zhang | Department of Mathematics | Brandeis University |
| Lihong Zhao | Department of Applied Mathematics | University of California, Merced |
| Heather Zinn Brooks | Department of Mathematics | Harvey Mudd College |
Projects and teams
Project 1: HIV, Pre-exposure prophylaxis, and drug resistance
- Katharine Gurski, Howard University
- Yeona Kang, Howard University
In December 2021, the FDA approved an injectable pre-exposure prophylaxis (PrEP) for use in at-risk adults and adolescents to reduce the risk of sexually acquired HIV. The cabotegravir extended-release injectable suspension is given first as two initiation injections administered one month apart, and then every two months thereafter. In this project, we aim to study how dynamics of drug-sensitive and drug-resistant HIV strains within hosts affect the prevalence of drug-resistant strains in the population when injectable pre-exposure prophylaxis enters the picture. This project will use methods from dynamical systems, statistics as it relates to sensitivity analysis, data, and parameter estimation and numerical simulation.
Project 2: Modeling the stability and effectiveness of dosing regimens of oral hormonal contraceptives
- Mentor Lisette de Pillis, Harvey Mudd College
- Heather Zinn Brooks, Harvey Mudd College
Oral contraceptives are a leading form of birth control in the United States, but consistent daily use and unwanted side effects can pose challenges for some users. Existing mathematical models of the effects of hormonal contraception on the menstrual cycle do not incorporate the dynamics of the on/off dosing regimens or the metabolism of the exogenous hormones, although methods from differential equations and dynamical systems are well-positioned to investigate these questions. We aim to explore the stability of the contraceptive state achieved by oral hormonal contraceptives using a mechanistic mathematical model of the menstrual cycle. Such a model could provide insight into when a contraceptive state is lost due to inconsistency or changes in hormonal birth control use, which may further inform the advisement of care providers and the choices of birth control users.
Project 3: Effects of exogenous-hormone induced perturbations on blood clotting
- Mentor Karin Leiderman, Colorado School of Mines
- Anna Nelson, Duke University
Exogenous hormones are used by hundreds of millions of people worldwide for contraceptives and hormonal replacement therapy (HRT). However, estrogen in combined oral contraceptives (OC) and HRT have been shown to significantly increase the risk of both arterial and venous thrombosis.The objectives for this project are to use a mechanistic mathematical model of flow-mediated coagulation to investigate the effects of exogenous-hormone induced perturbations that have been observed on blood clotting. We will use the model to simulate specified hormone induced perturbation profiles, i.e., percent changes in plasma levels of proteins and blood platelets caused by estrogen and progesterone, in varying doses, separately and together. The first objective will be to verify the observations from the literature showing increased clotting for specified profiles and doses. It is also well known that plasma levels of clotting factors vary among individuals. Variation that is considered normal and still healthy is a range between 50 and 150% of the mean value of the healthy population. Our second objective will be to identify individuals that may be more susceptible to thrombosis due to certain hormones and doses. We will accomplish this by performing global sensitivity analysis on model output metrics where variance is due to uncertainty in the input levels of clotting factors, platelets, and hormones.
Project 4: Development of effective therapeutic schedules in breast and gynecological cancers
- Morgan Craig, University of Montreal
- Adrianne Jenner, Queensland University of Technology
After lung cancer, breast cancer continues to be projected as the second most commonly diagnosed cancer in Canada. Leveraging data cancer growth, pharmacokinetic and pharmacodynamic models of various cancer therapies, and models of therapeutic resistance, this project aims to identify responders/non-responders to treatments and establish effective therapeutic schedules in breast and gynecological cancers. For this, we will develop mathematical and pharmacokinetic/pharmacodynamic models, integrated with patient data, to construct and implement in silico clinical trials. Familiarity with MATLAB, Python, and/or R is recommended. Other data fitting software including Monolix may be introduced, but prior knowledge is not necessary.
Project 5: Modeling neonatal respiratory distress
- Laura Ellwein Fix, Virginia Commonwealth University
- Sharon Lubkin, North Carolina State University
Respiratory distress in the newborn, a condition characterized by difficulty breathing, occurs in about 7% of newborns. This team’s project will address a question related to modeling of respiratory mechanics in the neonatal population. We previously developed an ordinary differential equations (ODE) model describing dynamic breathing volumes and pressures in aggregate compartments depicting the airways, lungs, chest wall, and intrapleural space, in an ideal spontaneously breathing preterm infant. Current areas of inquiry include application to ventilated infants and parameter identification using clinical data from a neonatal intensive care unit or an animal model. Alternatively, a specific unsolved problem could arise that requires the incorporation of a different dynamic model type such as spatially dependent or stochastic, or connects the organ level respiratory system with different physiology. The team’s co-leaders have interests in physiology, biotransport, tissues, cardiovascular and respiratory systems, and the use of noninvasive data in modeling. Our expertise centers on physiological mechanistic modeling, spatiotemporal systems and dynamics, parameter identification, numerics, and model development starting from simple to complex. Ideal team members would have interest and knowledge in some of these areas and embrace the opportunity to learn in other areas.
Project 6: On stable estimation of disease parameters and forecasting in epidemiology
- Ruiyan Luo, Georgia State University
- Alexandra Smirnova, Georgia State University
Real-time reconstruction of disease parameters for an emerging outbreak helps to provide crucial information for the design of public health policies and control measures. The goal of our team project is to investigate and compare parameter estimation algorithms that do not require an explicit deterministic or stochastic trajectory of system evolution, and where the state variable(s) and the unknown disease parameters are reconstructed in a predictor-corrector manner in order to mitigate the excessive computational cost of a quasi-Newton step. We plan to look at uncertainty quantification and implications of parameter estimation on forecasting of future incidence cases. Theoretical study will be combined with numerical experiments using synthetic and real data for COVID-19 pandemic.