Skip to main content

You are here


Search found 6 items

Introductory Biology

  • Students engaged in building the PCR model

    A Close-Up Look at PCR

    Learning Objectives
    At the end of this lesson students will be able to...
    • Describe the role of a primer in PCR
    • Predict sequence and length of PCR product based on primer sequences
    • Recognize that primers are incorporated into the final PCR products and explain why
    • Identify covalent and hydrogen bonds formed and broken during PCR
    • Predict the structure of PCR products after each cycle of the reaction
    • Explain why amplification proceeds exponentially
  • Simplified Representation of the Global Carbon Cycle,

    Promoting Climate Change Literacy for Non-majors: Implementation of an atmospheric carbon dioxide modeling activity as...

    Learning Objectives
    • Students will be able to manipulate and produce data and graphs.
    • Students will be able to design a simple mathematical model of atmospheric CO2 that can be used to make predictions.
    • Students will be able to conduct simulations, analyze, interpret, and draw conclusions about atmospheric CO2 levels from their own computer generated simulated data.
  • The mechanisms regulating the trp operon system.

    Discovering Prokaryotic Gene Regulation with Simulations of the trp Operon

    Learning Objectives
    Students 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.
  • Multiple sequence alignment of homologous cytochrome C protein sequences using Jalview viewer.

    Sequence Similarity: An inquiry based and "under the hood" approach for incorporating molecular sequence...

    Learning Objectives
    At 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.
  • Mechanisms regulating the lac operon system

    Discovering Prokaryotic Gene Regulation by Building and Investigating a Computational Model of the lac Operon

    Learning Objectives
    Students 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.
  • Arabidopsis Seedling

    Linking Genotype to Phenotype: The Effect of a Mutation in Gibberellic Acid Production on Plant Germination

    Learning Objectives
    Students 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.