Professor, Biomedical Engineering University of Virginia Charlottesville, Virginia, United States
Introduction: The biotechnology and pharmaceutical industries are increasingly reliant on a workforce pipeline of graduates possessing the skills needed to quantitatively describe complex systems to predict functional outcomes relevant to healthy physiological function and to disease states. These skills will be essential for not only identifying novel drug targets and ascertaining the etiology of complex diseases such as cancer and heart disease, but also for achieving truly personalized medical diagnostics, therapies, and surgical approaches toward treating these diseases. Moreover, inherent biological complexity and high-throughput measurement approaches lead to massive “big data” sets, often with thousands of heterogeneous values. This complexity requires data science tools such as data-driven modeling and machine learning to appropriately integrate heterogeneous data. Thus, it is imperative to train a diverse new generation of scientists in the concepts and practice of multi-scale systems bioengineering and biomedical data sciences (BDS) research. At the University of Virginia, we developed an NSF-funded REU Site in Multi-Scale Systems Bioengineering and BDS (NSF #1560282 & #1950374) that has supported 76 students engaging in research projects for the past seven years.
Materials and
Methods: Over the seven-year period from 2017-2023, we recruited 76 students out of a total of 1,212 applicants, with participants coming from 50 colleges and universities around the country. Two summers (2020 & 2021) the program was run as a virtual REU due to institutional constraints on visiting researchers due to the COVID-19 pandemic. Prior to the start of each summer, these REU students were matched to a mentor based on a combination of student interest in specific sub-areas of systems bioengineering research (including interest in specific mentors) and mentor availability in a given summer. Most research projects relied primarily on previously developed methods and tools and typically involved application to biological data and generation of testable hypotheses. The specific research projects included a wide variety of topics in the field, ranging anywhere from molecular scale biophysics models to cell-scale signaling models, biomedical data science analysis of genetic data, tissue-level biomechanics models, and image analysis algorithms for quantifying cell distribution in tissue-engineered constructs. The participants took part in an introductory bootcamp on the fundamentals of systems modeling and applied biostatistics and had multiple opportunities to present their research progress throughout the summer to experts in the field. They also received professional development training through workshops and seminars on research ethics, technical communication, and launching careers in systems bioengineering. We analyzed participant demographics, outcomes in presenting or publishing their work, career outcomes, and survey data from each summer’s cohort.
Results, Conclusions, and Discussions: Of the total of 76 students who participated in the REU, participants came from 50 colleges and universities and represented 21 different majors, with 46% of them biomedical engineering (BME) majors; 68% were from groups traditionally underrepresented in STEM, 32% were first-generation students; 59% were women; and 39% attended non-R1 institutions. Of these 76 students, 69% have presented their work in-person at national meetings, and six have become co-authors on seven papers. Of the 60 who have since graduated, 80% are either in graduate school or in STEM industry positions. Post-REU surveys of participants revealed that 98% of respondents rated their overall experience with the REU as either “very satisfied” or “satisfied” (average 4.72 on a Likert scale). Evaluations of specific program objectives and mentoring were similarly high. In terms of impact on long-term goals, 75% said that the REU increased their interest in STEM and encouraged them to pursue further education towards a research or academic career, while 45% said the program helped solidify interest specifically in systems bioengineering.
During its first seven years, the Multi-Scale Systems Bioengineering and BDS REU program at the University of Virginia was successful in meeting goals in terms of recruitment statistics, student outcomes, and feedback, even during two years of a completely online program. From a programmatic standpoint, we have several recommendations: Our large number of applications suggest that the specific research area is important (as opposed to, for instance, a non-thematic or more diffuse REU), and if well-presented to possible applicants can be a highly motivating selling point. Our experience during the pandemic was that a virtual REU can lead to positive outcomes, although cohort bonding and the experience of working within a lab setting are diminished. One challenge of a bootcamp for all participants is appropriately accommodating the needs of the varied research topics. Some projects require coding, but others do not since they use established software tools; some require model development, others data science. We are continually iterating to find the optimal balance of instruction in topics that are foundational to every student in the program.
Acknowledgements (Optional): We would like to acknowledge the support of the National Science Foundation (#1560282 & #1950374) in funding this REU site from 2016-2024 with supplemental funding site from the National Institutes of Health (NIH) National Cancer Institute (5U54CA274499-02), as well as Dr. Brian Helmke for assisting with assessment of the program and Kitter Bishop, Hannah Moore, & Karen Sleezer for coordination of program logistics.
