The Case for Building Faculty Capacity for Coding in BMB Education and What We Are Doing About It

Several prominent areas of biochemistry and molecular biology (BMB) heavily utilize coding. Research in areas such as bioinformatics, computational biology, genomics, proteomics, data science and machine learning, and protein dynamics and design all rely on coding to handle large amounts of data and visualization. In December of 2024, we asked the BMB educational community, “Should we be teaching coding to basic science students?”, and solicited responses for a “Coding in Biochemistry and Molecular Biology” survey 1. We also pledged to share the results of the survey with the BMB community. Our survey garnered 80 respondents. When asked, “Do you think it is important for your students to learn some coding?” 96.3% of respondents selected “yes,” indicating that among our survey respondents, BMB faculty believe that exposure to some coding is important for students. In order to gauge the level of coding experience among survey respondents we asked, “Do you have any experience coding in the context of Biochemistry or Molecular Biology? If yes, what languages have you worked with (select all that apply)?” While 53.8% of respondents indicated they had experience with coding in Python and 38.8% had experience coding in R (not mutually exclusive), 33.8% of respondents indicated they had no experience coding in a BMB context. In addition, less than half (41.3%) of respondents have experience running coding activities in a Biochemistry or Molecular Biology course. In response to the question, “Would you be interested in instruction resources (activities, tutorials, workshops, etc.) for coding in Biochemistry or Molecular Biology?” Only 3.8% of respondents selected “No.” Of the remaining responses, 72.2% were “Yes” and 24.1% were “Maybe, if I knew more about the resources.” As faculty may be reluctant to teach what they don't know, these numbers suggest that there is not only a need to train faculty in basic coding, but also to teach faculty how to teach BMB courses with coding. The survey asked respondents, “Which topics in Biochemistry or Molecular Biology would best facilitate an introduction to coding for your students (select up to 3)?” The results of this question are shown in Figure 1. The top five topics identified by respondents in order of popularity were (1) plotting in python, (2) molecular visualization, (3) working with Google Colab, (4) molecular docking, and (5) protein sequence analysis. These results suggest a possible pathway for faculty to learn coding and to expose students to some basic coding by learning to plot data using python in Google's Colab environment. The last survey question invited open-ended responses, “If you have any other suggestions or questions, please let us know!” Three respondents commented on using AI in teaching BMB. There has been a dramatic increase (or maybe a tidal wave!) of AI activity related to teaching, learning, and life in general in the year since the initial survey was conducted. It is particularly noteworthy that coding in Google Colab is directly linked to the Gemini AI environment, which can assist users in writing code if the users know a little about creating prompts to get desired results from AI. Coding with AI and Generative AI prompt engineering are two major issues that need to be considered by faculty integrating coding into BMB courses. In sum, our survey results support teaching basic coding to BMB students; however, to generate curricular change, BMB faculty need technological content knowledge training to adopt computational literacy into their existing coursework 2. To help address these needs, we have three workshops planned for the spring and summer of 2026. More details about our upcoming workshops, Slack channel, LinkedIn group, and GitHub repostitories can be found at: https://sites.wabash.edu/pybmb. Our team will present a virtual IQB Crash Course entitled, “CodeBMB: Computational Literacy for Biochemistry and Molecular Biology Education,” sponsored by the team from the RCSB PDB at Rutgers University. Prior IQB Crash Courses on Python Scripting can be accessed from the PDB-101 Training Resources page (https://pdb101.rcsb.org/train/training-events). Unlike previous IQB sessions, this crash course will not require any prior knowledge of coding and will be designed to help you learn a bit of Python and to see how you can use it in your teaching and research. We will host a two-day virtual workshop for coding novices on Python Scripting on May 19 and May 21. This workshop, “Introduction to Coding and Computational Resources” will consist of two 3-h sessions with some homework required between the two sessions. We will begin by describing the Technological Pedagogical Content Knowledge (TPACK) framework for coding and AI use in BMB education 2. We will then follow up with some introductory coding skills (e.g., navigating Google Drive 3, Colab 4, and GitHub 5) and specific applications of Python in the BMB classroom and lab. Our NSF support will enable us to offer three 2-day workshops over the next 12 months (May 2026–April 2027). In addition to this introductory workshop, we will offer a more advanced workshop on learning to code and a third workshop on teaching with code. This workshop series will be repeated in the following year (May 2027–April 2028). Participants completing the three workshops will gain the knowledge and pedagogical skills needed to teach students how to utilize code in a BMB environment. At the 2026 Biennial Conference on Chemical Education in Madison, Wisconsin, we will host a full day (three 1.5 h sessions) workshop entitled, “A beginner's guide to using coding to enhance (bio)chemical education: From zero to classroom exercise in one day!” Faculty participants will learn to utilize online resources such as GitHub, Google Drive, and Google Colab to write and run Python and R code. They will also learn to use AI tools to quickly develop working code. Finally, participants will develop their own chemistry/biochemistry exercise and will be invited to share their projects with other participants. We expect participants at any of these workshops will gain confidence in using Python- or R-based computational exercises in their BMB courses. Perhaps more importantly, participants will be joining a community of like-minded and supportive faculty members better prepared to educate the next generation of BMB students! This work was supported by the National Science Foundation, 2518732, 2518733. The authors declare no conflicts of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request.