Skip to main content

You are here

Filters

Search found 11 items

Introductory Biology

  • Figure 2. ICB-Students come to class prepared to discuss the text
  • A three-dimensional model of methionine is superimposed on a phase contrast micrograph of Saccharomyces cerevisiae from a log phase culture.

    Follow the Sulfur: Using Yeast Mutants to Study a Metabolic Pathway

    Learning Objectives
    At 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.
  • Format of a typical course meeting
  • 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.
  • 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.
  • The Roc is a mythical giant bird of prey, first conceived during the Islamic Golden Age (~8th to 13th centuries CE), popularized in folk tales gathered in One Thousand One Nights. Rocs figured prominently in tales of Sinbad the Sailor. In this 1898 illustration by René Bull, the Roc is harassing two of Sinbad’s small fleet of ships. Illustration by René Bull is licensed under CC BY 2.0. (Source: https://en.wikipedia.org/wiki/Roc_(mythology)#mediaviewer/File:Rocweb.jpg)

    A first lesson in mathematical modeling for biologists: Rocs

    Learning 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.
  • 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.
  • pClone Red Makes Research Look Easy

    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.
  • Image from a clicker-based case study on muscular dystrophy and the effect of mutations on the processes in the central dogma.

    A clicker-based case study that untangles student thinking about the processes in the central dogma

    Learning Objectives
    Students 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.
  • Students at Century College use gel electrophoresis to analyze PCR samples in order to detect a group of ampicillin-resistance genes.

    Antibiotic Resistance Genes Detection in Environmental Samples

    Learning Objectives
    After completing this laboratory series, students will be able to:
    • apply the scientific method in formulating a hypothesis, designing a controlled experiment using appropriate molecular biology techniques, and analyzing experimental results;
    • conduct a molecular biology experiment and explain the principles behind methodologies, such as accurate use of micropipettes, PCR (polymerase chain reaction), and gel electrophoresis;
    • determine the presence of antibiotic-resistance genes in environmental samples by analyzing PCR products using gel electrophoresis;
    • explain mechanisms of microbial antibiotic resistance;
    • contribute data to the Antibiotic Resistance Genes Network;
    • define and apply key concepts of antibiotic resistance and gene identification via PCR fragment size.