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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.
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
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 Trees: Introducing evolutionary concepts by exploring Crassulaceae phylogeny and biogeographyLearning ObjectivesStudents will be able to:
- Estimate phylogenetic trees using diverse data types and phylogenetic models.
- Correctly make inferences about evolutionary history and relatedness from the tree diagrams obtained.
- Use selected computer programs for phylogenetic analysis.
- Use bootstrapping to assess the statistical support for a phylogeny.
- Use phylogenetic data to construct, compare, and evaluate the role of geologic processes in shaping the historical and current geographic distributions of a group of organisms.
Using Place-Based Economically Relevant Organisms to Improve Student Understanding of the Roles of Carbon Dioxide,...Learning ObjectivesAt the end of this lesson, students will be able to:
- Describe the roles of light energy and carbon dioxide in photosynthetic organisms.
- Identify the effect of nutrients on the growth of photosynthetic organisms.
- Describe global cycles in atmospheric carbon dioxide levels and how they relate to photosynthetic organisms.
Evaluating the Quick Fix: Weight Loss Drugs and Cellular RespirationLearning Objectives
- Students will be able to explain how the energy from sugars is transformed into ATP via cellular respiration.
- Students will be able to predict an outcome if there is a perturbation in the cellular respiration pathway.
- Students will be able to state and evaluate a hypothesis.
- Students will be able to interpret data from a graph, and use that data to make inferences about the action of a drug.
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.