Skip to main content

You are here

Filters

Search

  • 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.
  • Structure of protein ADA2

    Understanding Protein Domains: A Modular Approach

    Learning Objectives
    • Students will be able to compare protein sequences and identify conserved regions and putative domains.
    • Students will be able to obtain, examine, and compare structural models of protein domains.
    • Students will be able to interpret data on protein interactions (in vitro pull-down and in vitro and in vivo functional assays)
    • Students will be able to propose experiments to test protein interactions.
  • Abelson kinase signaling network. The image shows many connections between genes and illustrates that signaling molecules and pathways function within networks. It emphasizes the indispensability of computational tools in understanding the molecular functioning of cells. The image was generated with Cytoscape from publicly accessible protein-protein interactions databases.

    Investigating Cell Signaling with Gene Expression Datasets

    Learning Objectives
    Students will be able to:
    • Explain the hierarchical organization of signal transduction pathways.
    • Explain the role of enzymes in signal propagation and amplification.
    • Recognize the centrality of signaling pathways in cellular processes, such as metabolism, cell division, or cell motility.
    • Rationalize the etiologic basis of disease in terms of deranged signaling pathways.
    • Use software to analyze and interpret gene expression data.
    • Use an appropriate statistical method for hypotheses testing.
    • Produce reports that are written in scientific style.
  • SNP model by David Eccles (gringer) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY 4.0 (http://creativecommons.org/licenses/by/4.0)], via Wikimedia Commons

    Exploration of the Human Genome by Investigation of Personalized SNPs

    Learning Objectives
    Students successfully completing this lesson will be able to:
    • Effectively use the bioinformatics databases (SNPedia, the UCSC Genome Browser, and NCBI) to explore SNPs of interest within the human genome.
    • Identify three health-related SNPs of personal interest and use the UCSC Genome Browser to define their precise chromosomal locations and determine whether they lie within a gene or are intergenic.
    • Establish a list of all genome-wide association studies correlated with a particular health-related SNP.
    • Predict which model organism would be most appropriate for conducting further research on a human disease.
  • Using QIIME to Interpret Environmental Microbial Communities in an Upper Level Metagenomics Course

    Learning Objectives
    Students will be able to:
    • list and perform the steps of sequence processing and taxonomic inference.
    • interpret microbial community diversity from metagenomic sequence datasets.
    • compare microbial diversity within and between samples or treatments.
  • Using Undergraduate Molecular Biology Labs to Discover Targets of miRNAs in Humans

    Learning Objectives
    • Use biological databases to generate and compare lists of predicted miR targets, and obtain the mRNA sequence of their selected candidate gene
    • Use bioinformatics tools to design and optimize primer sets for qPCR
  • MA plot of RNA-seq data. An MA plot is a visual summary of gene expression data which identifies genes showing differential expression between two treatments.

    Tackling "Big Data" with Biology Undergrads: A Simple RNA-seq Data Analysis Tutorial Using Galaxy

    Learning Objectives
    • Students will locate and download high-throughput sequence data and genome annotation files from publically available data repositories.
    • Students will use Galaxy to create an automated computational workflow that performs sequence quality assessment, trimming, and mapping of RNA-seq data.
    • Students will analyze and interpret the outputs of RNA-seq analysis programs.
    • Students will identify a group of genes that is differentially expressed between treatment and control samples, and interpret the biological significance of this list of differentially expressed genes.