Will Heuett Marymount University, email@example.com
John Jungck (University of Delaware; firstname.lastname@example.org)
Learning in Mathematical Biology classrooms and laboratories has gone through four generations since Sputnik and the STEM education reform initiatives that responded to that international challenge. In a very conservative analysis by a skeptic of reform, Richard Askey (1999) characterized three revolutions as “New Math,” “Back-to-Basics,” and “NCTM standards” and foreshadowed an impending fourth movement which we might now recognize as “Common Core” or “Next Generation Science Standards.” In 2000, Lynn Steen responded with a series of questions that presented a very different perspective on the issues that respected educational and cognitive science research. Changes in the technology deployed are obvious benchmarks that we have had to respond to over this time frame of the past sixty years. However, there are at least three other drivers that correspond to, but are sometimes uncoupled from these changes, that address the context and goals of mathematics education. First, the focus of what mathematics is important to biologists has simultaneously changed from a primary focus on theorems, proofs, and problems in calculus to the inclusion of modeling, statistics, liner algebra, combinatorics, and even some computational thinking. Second, the movement away from proprietary interests towards open science, open source, Creative Commons, copyleft, crowdsourcing, and Citizen Science has fundamentally changed the resources that we have available as well as the potential for students engaging in work for the common good, participatory democracy, and active responsible citizenship. These efforts have often called upon the historical contributions to mathematics in the growing field of ethnomathematics and the challenges and opportunities to address issues of social justice, equity, and inclusion. Third, our pedagogy has similarly changed from “sages on stages” to “guides on the sides” to “meddlers in the middle” as we shifted from instructionalist pedagogies designed to “sort and select” students to learner-centered classrooms designed to improve retention and graduation rates, especially of historically underrepresented groups.
Mathematical Epidemiology can be the perfect elective-level course for an undergraduate. Background need not include more than some calculus and linear algebra. Students start on Day 1 by delving into the scope of what mathematics can accomplish, as well as beginning to build basic models of disease spread. Content throughout the semester includes differential equations, linear algebra, use of data, reading the literature, synthesizing ideas, writing about process and results, and incorporating current events. The topics in this course strengthen and expand upon the mathematical background students learned in earlier studies. This talk discusses semester-long structure, ideas for writing assignments, using epidemiological applications to improve student understanding of mathematical theory, and balancing prepared semester materials with impromptu discussion of current events.
Over the last 7 years at UMBC we have run an REU site funded by NSF, NSA, and UMBC's Meyerhoff program that teaches the basics of parallel computing on a state of the art cluster. As one of the largest REU sites we bring in 24-33 broadly diverse students each year and form teams of 4-5 students. Clients present computationally intensive problems to the teams who then propose solutions. Several of these projects address biological phenomena such as clustering of beta cells in a computational islet, probabilistic release of calcium in a cardiac cell, and most recently clustered cell migration in a drosophila egg chamber. We will discuss the training component, the evolution of the client selection process, and highlight several results many of which have led to student publications.
The success of mathematical biology education initiatives relies upon the retention of
students in STEM disciplines. More efforts are needed to improve retention rates in
STEM, especially of historically underrepresented groups. Not only are women and
underrepresented groups important components of the workforce, but STEM fields can
benefit from the wider range of solutions contributed by diversity in its researchers.
Progressing on the path towards STEM careers is not always easy, however, and many
young women disappear along the way. This loss of women is a common phenomenon
in academic systems around the globe and has been described as the “leaky pipeline.”
The leaky pipeline for women in STEM careers starts at early stages, continues
throughout the career trajectory, and gradually results in a small proportion of women
We initiated a pilot study to begin to remedy the situation, focusing on Africa. Following a group discussion in which participants from 15+ countries in Africa identified ‘mentoring’ as a potential solution to the leaky pipeline for women in STEM in their communities, we created of a program for professional development, mentoring, and support for one particular group, female African students in the one-year coursework master’s program in mathematics at the African Institute of Mathematical Sciences (AIMS) – South Africa. Through the pre-existing AIMS Women in STEM (WIS) Mentoring/Outreach Initiative, we held mentoring sessions, mentoring lunches, and began a mentor-mentee matching program.
Mentoring sessions included presentations on the career journeys of women researchers in STEM, including the challenges they faced and obstacles they overcame, and panel discussions on professional development. The one-on-one mentoring program matched 13 mentor-mentee pairs. Pairs met once per week for 4 weeks, with the option for a second pairing in an additional 4-week session. Mentors and mentees could focus on any or all of three aspects: navigating the career path, choosing a research area in mathematics, and discussion of personal challenges encountered. Thirdly, we arranged mentoring lunches for the informal discussion of career development, related issues, and mutual support.
Exit survey feedback from the three initiatives we implemented will be presented, focusing on whether these programs increased or decreased the students’ perception of their available resources to succeed, other benefits reported, and what role these programs may play to increase the likelihood that the women continue in STEM careers. This talk will conclude with an audience discussion regarding what may best address this issue of retention in STEM and related topics.
Modeling and simulation (M&S) is an integral part of both Systems Biology and Pharmacology education.
The dynamical mechanisms of living systems can be difficult to reveal in a laboratory setting; however,
through a combination of laboratory measurements and mechanistic model building M&S approaches
can provide a substrate for hypothesis testing. In addition, increased adoption by the Pharmaceutical
Industry of model-based approaches to the development of new therapies intensifies the demand for
strong M&S skills in graduating students. Here, we present two case studies that address the need to
incorporate computational M&S into the biological sciences curriculum.
First, we consider Computational Systems Biology, offered to Physiology and Bioinformatics graduate students at the University of Michigan. The goal of this course is to enable students to translate descriptions of biological and physiological processes into mathematical models, which can then be solved numerically using MATLAB. This course serves students with a variety of undergraduate backgrounds and programming experience, and therefore utilizes online training resources and a summer workshop to establish basic computational skills. By level-setting students in advance, the course can focus on the domain-specific application of M&S rather than on generic training on the tools. We found that graphical-based tools with a block diagram for modeling are helpful to introduce dynamical systems/ODE modeling to biomedical scientists with no advance calculus or programming experience.
Secondly, we examine Quantitative Methods in Pharmacology for graduate students at Harvard Medical School. The course focuses on computational pharmacology and follows a flipped classroom approach: students review the background material outside of class and spend class time working hands-on with SimBiology. SimBiology is a MATLAB-based tool that offers both an intuitive graphical approach and a programmatic interface to modeling biological systems. It also includes functions and capabilities to perform common tasks, such as simulation to predict system behavior, sensitivity analysis to identify significant biological pathways, and parameter estimation to fit models to data. By using SimBiology, students quickly and easily implement and analyze models of complex biological and pharmacological processes. To help students appreciate the relevance of the content, the course invites guest lecturers from industry to speak about the use of SimBiology, systems approaches, and M&S in pharmaceutical research. One iteration of the course had the students generate a model related to their thesis research, permitting them to recast their work in a quantitative modeling framework for M&S. The course evaluations have been uniformly positive, with many of the students noting that the skills and concepts they acquired were immediately applicable to their research projects.