  Quantitative methods and mathematical modeling are playing an increasingly
important role across disciplines. As a result, interdisciplinary mathematics
courses are increasing in popularity. However, teaching such courses at an
advanced level can be challenging. Students often arrive with different
mathematical backgrounds, different interests, and divergent reasons for
wanting to learn the material. Here we describe a course on stochastic
processes in biology, delivered between September and December 2020 to a mixed
audience of mathematicians and biologists. In addition to traditional lectures
and homeworks, we incorporated a series of weekly computational challenges into
the course. These challenges served to familiarize students with the main
modeling concepts, and provide them with an introduction on how to implement
them in a research-like setting. In order to account for the different academic
backgrounds of the students, they worked on the challenges in small groups, and
presented their results and code in a dedicated discussion class each week. We
discuss our experience designing and implementing an element of problem-based
learning in an applied mathematics course through computational challenges. We
also discuss feedback from students, and describe the content of the challenges
presented in the course. We provide all materials, along with example code for
a number of challenges.
