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Using Synthetic Biology and pClone Red for Authentic Research on Promoter Function: Introductory Biology (identifying...Learning Objectives
- Describe how cells can produce proteins at the right time and correct amount.
- Diagram how a repressor works to reduce transcription.
- Diagram how an activator works to increase transcription.
- Identify a new promoter from literature and design a method to clone it and test its function.
- Successfully and safely manipulate DNA and Escherichia coli for ligation and transformation experiments.
- Design an experiment to verify a new 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.
Follow the Sulfur: Using Yeast Mutants to Study a Metabolic PathwayLearning ObjectivesAt the end of this lesson, students will be able to:
- use spot plating techniques to compare the growth of yeast strains on solid culture media.
- predict the ability of specific met deletion strains to grow on media containing various sulfur sources.
- predict how mutations in specific genes will affect the concentrations of metabolites in the pathways involved in methionine biosynthesis.
Sequence Similarity: An inquiry based and "under the hood" approach for incorporating molecular sequence...Learning ObjectivesAt the end of this lesson, students will be able to:
- Define similarity in a non-biological and biological sense when provided with two strings of letters.
- Quantify the similarity between two gene/protein sequences.
- Explain how a substitution matrix is used to quantify similarity.
- Calculate amino acid similarity scores using a scoring matrix.
- Demonstrate how to access genomic data (e.g., from NCBI nucleotide and protein databases).
- Demonstrate how to use bioinformatics tools to analyze genomic data (e.g., BLASTP), explain a simplified BLAST search algorithm including how similarity is used to perform a BLAST search, and how to evaluate the results of a BLAST search.
- Create a nearest-neighbor distance matrix.
- Create a multiple sequence alignment using a nearest-neighbor distance matrix and a phylogram based on similarity of amino acid sequences.
- Use appropriate bioinformatics sequence alignment tools to investigate a biological question.
Linking Genotype to Phenotype: The Effect of a Mutation in Gibberellic Acid Production on Plant GerminationLearning ObjectivesStudents will be able to:
- identify when germination occurs.
- score germination in the presence and absence of GA to construct graphs of collated class data of wild-type and mutant specimens.
- identify the genotype of an unknown sample based on the analysis of their graphical data.
- organize data and perform quantitative data analysis.
- explain the importance of GA for plant germination.
- connect the inheritance of a mutation with the observed phenotype.
Discovering Prokaryotic Gene Regulation with Simulations of the trp OperonLearning ObjectivesStudents will be able to:
- Perturb and interpret simulations of the trp operon.
- Define how simulation results relate to cellular events.
- Describe the biological role of the trp operon.
- Describe cellular mechanisms regulating the trp operon.
- Explain mechanistically how changes in the extracellular environment affect the trp operon.
- Define the impact of mutations on trp operon expression and regulation.
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.
Discovering Prokaryotic Gene Regulation by Building and Investigating a Computational Model of the lac OperonLearning ObjectivesStudents will be able to:
- model how the components of the lac operon contribute to gene regulation and expression.
- generate and test predictions using computational modeling and simulations.
- interpret and record graphs displaying simulation results.
- relate simulation results to cellular events.
- describe how changes in environmental glucose and lactose levels impact regulation of the lac operon.
- predict, test, and explain how mutations in specific elements in the lac operon affect their protein product and other elements within the operon.
A first lesson in mathematical modeling for biologists: RocsLearning Objectives
- Systematically develop a functioning, discrete, single-species model of an exponentially-growing or -declining population.
- Use the model to recommend appropriate action for population management.
- Communicate model output and recommendations to non-expert audiences.
- Generate a collaborative work product that most individuals could not generate on their own, given time and resource constraints.