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Cutthroat trout in Colorado: A case study connecting evolution and conservationLearning ObjectivesStudents will be able to:
- interpret figures such as maps, phylogenies, STRUCTURE plots, and networks for species delimitation
- identify sources of uncertainty and disagreement in real data sets
- propose research to address or remedy uncertainty
- construct an evidence-based argument for the management of a rare taxon
The Case of the Missing Strawberries: RFLP analysisLearning ObjectivesStudents will be able to:
- Describe the relationship of cells, chromosomes, and DNA.
- Isolate DNA from strawberries.
- Digest DNA with restriction enzymes.
- Perform gel electrophoresis.
- Design an experiment to compare DNAs by RFLP analysis.
- Predict results of RFLP analysis.
- Interpret results of RFLP analysis.
- Use appropriate safety procedures in the lab.
Predicting and classifying effects of insertion and deletion mutations on protein coding regionsLearning ObjectivesStudents will be able to:
- accurately predict effects of frameshift mutations in protein coding regions
- conduct statistical analysis to compare expected and observed values
- become familiar with accessing and using DNA sequence databases and analysis tools
Building a Model of Tumorigenesis: A small group activity for a cancer biology/cell biology courseLearning ObjectivesAt the end of the activity, students will be able to:
- Analyze data from a retrospective clinical study uncovering genetic alterations in colorectal cancer.
- Draw conclusions about human tumorigenesis using data from a retrospective clinical study.
- Present scientific data in an appropriate and accurate way.
- Discuss why modeling is an important practice of science.
- Create a simple model of the genetic changes associated with a particular human cancer.
Using Synthetic Biology and pClone Red for Authentic Research on Promoter Function: Genetics (analyzing mutant...Learning Objectives
- Describe how cells can produce proteins at the right time and correct amount.
- Diagram a bacterial promoter with −35 and −10 elements and the transcription start site.
- Describe how mutational analysis can be used to study promoter sequence requirements.
- Develop a promoter mutation hypothesis and design an experiment to test it.
- Successfully and safely manipulate DNA and Escherichia coli for ligation and transformation experiments.
- Design an experiment to verify a mutated promoter has been cloned into a destination vector.
- Design an experiment to measure the strength of a promoter.
- Analyze data showing reporter protein produced and use the data to assess promoter strength.
- Define type IIs restriction enzymes.
- Distinguish between type II and type IIs restriction enzymes.
- Explain how Golden Gate Assembly (GGA) works.
- Measure the relative strength of a promoter compared to a standard promoter.
An Introduction to Eukaryotic Genome Analysis in Non-model Species for Undergraduates: A tutorial from the Genome...Learning ObjectivesAt the end of the activity, students will be able to:
- Explain the steps involved in genome assembly, annotation, and variant detection to other students and instructors.
- Create meaningful visualizations of their data using the integrated genome viewer.
- Use the Linux command line and web-based tools to answer research questions.
- Produce annotated genomes and call variants from raw sequencing reads in non-model species.
A clicker-based case study that untangles student thinking about the processes in the central dogmaLearning ObjectivesStudents will be able to:
- explain the differences between silent (no change in the resulting amino acid sequence), missense (a change in the amino acid sequence), and nonsense (a change resulting in a premature stop codon) mutations.
- differentiate between how information is encoded during DNA replication, transcription, and translation.
- evaluate how different types of mutations (silent, missense, and nonsense) and the location of those mutations (intron, exon, and promoter) differentially affect the processes in the central dogma.
- predict the molecular (DNA size, mRNA length, mRNA abundance, and protein length) and/or phenotypic consequences of mutations.