Modeling and analysis of the firefly luciferase reaction and the G-protein coupled receptor signaling pathway with ordinary differential equations increases self confidence in mathematical cell biology for novice graduate students

Authors

  • Renee Dale Donald Danforth Plant Science Center, St. Louis, MO Author
  • Naohiro Kato Department of Biology, Louisiana State University, Baton Rouge, LA Author
  • Bill Wischusen Department of Biology, Louisiana State University, Baton Rouge, LA Author

DOI:

https://doi.org/10.30707/LiB7.1.1647875326.072801

Keywords:

Mathematical modeling, Systems biology, Cell biology, Quantitative skills, Computational biology, Education

Abstract

The push for mathematics and computation in biology is greater than ever before. However, perceptions among biology students on the difficulty of mathematics and programming are a barrier to their implementation. A teaching module introducing quantitative skills to biology students is needed. We implemented modeling of firefly luciferase and G-protein coupled receptor signaling with ordinary differential equations in a course for novice graduate students. We assessed whether the course helped the students increase self confidence in application of mathematics in biology. Two concept inventories tracked learning gains in both general and cell biology knowledge. Pre- and post-semester surveys quantified changes in student confidence and their opinions of the usefulness of these techniques in cell biology. We found that the modeling and analysis activities appeared to improve self confidence in and appreciation of quantitative mathematical biology techniques. We describe our assessment methods to determine the suitability of our module.

Additional Files

Published

2020-02-28

Issue

Section

Education

How to Cite

Modeling and analysis of the firefly luciferase reaction and the G-protein coupled receptor signaling pathway with ordinary differential equations increases self confidence in mathematical cell biology for novice graduate students. (2020). Letters in Biomathematics, 7(1), 3-13. https://doi.org/10.30707/LiB7.1.1647875326.072801

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