Premium
Modeling Interdisciplinary Collaborations Through a Course‐Based Undergraduate Research Experience (CURE)
Author(s) -
Roberts Rebecca,
Koeppe Julia,
Price Simara,
Allwein Benjamin,
Anderson Trevor,
Daubner Susan Colette,
Irby Stefan,
Mills Jeffrey,
Pikaart Michael,
Craig Paul
Publication year - 2017
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.31.1_supplement.588.12
Subject(s) - function (biology) , undergraduate research , computer science , discipline , plug in , in silico , data science , medical education , chemistry , medicine , biology , biochemistry , sociology , social science , evolutionary biology , gene , programming language
Interdisciplinary collaborations are often essential to answer multi‐faceted questions, yet they present with many challenges such as understanding the information, methodologies, and norms of another field and effective communication between team members. We must begin to provide young scientists with the skills necessary for success as the call for more interdisciplinary science expands. We have modelled an interdisciplinary collaboration at the undergraduate level through a Course‐based Undergraduate Research Experience (CURE). Students in upper‐level Biochemistry and Structural Biology courses collaborated to assign functions to proteins “of unknown function” in the Protein Data Bank (PDB). The Structural Biology students used bioinformatics tools including the ProMOL plugin to the PyMOL molecular graphics environment, along with BLAST, Pfam, and Dali to predict protein functions. In parallel, the Biochemistry students expressed and purified the proteins and carried out in vitro testing, informed by the in silico ‐predicted enzyme function. Students came together in cross‐disciplinary teams throughout the project to share progress, explain their discipline‐specific methodologies, and discuss next steps. They each presented an oral progress report on the entire project, which required that they not only understand their own work but that of their collaborators. Interdisciplinary teams presented final posters to the campus community and students prepared future directions documents. We had students fill out a pre‐ and post‐assessment. The assessment had two foci: the effectiveness of the cross‐course collaboration model and assessment of content learning. While a second iteration of the CURE will be necessary for a full statistical analysis, we see a trend that students learned the information and methodologies of both their own discipline and that of their collaborators. Moreover students appear to have increased their ability to communicate to and learn from their collaborators. Support or Funding Information This project is supported by NSF IUSE 1503811.