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In the early 2000s, our primarily undergraduate, white institution (PUI/PWI), began recruiting and enrolling higher numbers of students of color and first-generation college students. However, like many of our peer institutions, our established pedagogies and mindsets did not provide these students an educational experience to enable them to persist and thrive in STEM. Realizing the need to systematically address our lack of inclusivity in science majors, in 2012 faculty from multiple disciplines developed the Science, Math, and Research Training (SMART) program. Here, we describe an educational innovation, originally funded by a grant from the Howard Hughes Medical Institute, designed to support and retain students of color, first generation college students, and other students with marginalized identities in the sciences through a cohort-based, integrated, and inclusive first-year experience focused on community and sense of belonging. The SMART program engages first-year students with semester-long themed courses around “real world” problems of antibiotic resistance and viral infections while integrating the fields of Biology, Chemistry, Mathematics, and an optional Computer Science component. In the decade since its inception, 97% of SMART students have graduated or are on track to graduate, with 80.9% of these students earning a major in a STEM discipline. Here, we present additional student outcomes since the initiation of this program, results of the student self-evaluative surveys SALG and CURE, and lessons we have learned from a decade of this educational experience. 

Mathematical models of biological systems must strike a balance between being sufficiently complex to capture important biological features, while being simple enough that they remain tractable through analysis or simulation. In this work, we rigorously explore how to balance these competing interests when modeling murine melanoma treatment with oncolytic viruses and dendritic cell injections. Previously, we developed a system of six ordinary differential equations containing fourteen parameters that well describes experimental data on the efficacy of these treatments. Here, we explore whether this previously developed model is the minimal model needed to accurately describe the data. Using a variety of techniques, including sensitivity analyses and a parameter sloppiness analysis, we find that our model can be reduced by one variable and three parameters and still give excellent fits to the data. We also argue that our model is not too simple to capture the dynamics of the data, and that the original and minimal models make similar predictions about the efficacy and robustness of protocols not considered in experiments. Reducing the model to its minimal form allows us to increase the tractability of the system in the face of parametric uncertainty.