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Investigating Cell Signaling with Gene Expression Datasets.
James Wachira1, Cleo Hughes-Darden1, Asamoah Nkwanta2
1Department of Biology, Morgan State University, 1700 E. Cold Spring Lane, Baltimore, MD 21251.
Coursesource
|August 29, 2020
Summary
This lesson introduces undergraduate students to analyzing large datasets in molecular biology. It enhances scientific communication and quantitative data analysis skills using cell signaling and gene expression examples.
Area of Science:
- Molecular Biology
- Genomics
- Bioinformatics
Background:
- Modern molecular biology is data-intensive, lacking sufficient instructional resources for undergraduate data analysis.
- Undergraduates require training in computational techniques for handling large biological datasets.
Purpose of the Study:
- To introduce undergraduate students to essential skills for analyzing large datasets in molecular biology.
- To enhance scientific communication and quantitative data analysis abilities in students.
- To provide awareness of protocols for analyzing high-content quantitative molecular biology data.
Main Methods:
- Active learning sessions integrating data analysis with scientific paper structure exercises.
- Utilizing functional genomics databases and quantitative methods for biological data analysis.
- Assessment through mini-reports and a final report evaluating understanding and application.
Main Results:
- Students gain understanding of signal transduction's role in gene expression control.
- Students improve scientific writing and data interpretation skills.
- Students develop proficiency in analyzing and inferring from quantitative molecular biology data.
Conclusions:
- The lesson effectively equips undergraduates with crucial skills for modern molecular biology research.
- The curriculum bridges the gap in instructional resources for computational biology at the undergraduate level.
- The modules are adaptable for various courses and can supplement laboratory experiments.
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