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  • Plant ecology students surveying vegetation at Red Hills, CA, spring 2012.  From left to right are G.L, F.D, A.M., and R.P.  Photo used with permission from all students.

    Out of Your Seat and on Your Feet! An adaptable course-based research project in plant ecology for advanced students

    Learning Objectives
    Students will:
    • Articulate testable hypotheses. (Lab 8, final presentation/paper, in-class exercises)
    • Analyze data to determine the level of support for articulated hypotheses. (Labs 4-7, final presentation/paper)
    • Identify multiple species of plants in the field quickly and accurately. (Labs 2-3, field trip)
    • Measure environmental variables and sample vegetation in the field. (Labs 2-3, field trip)
    • Analyze soil samples using a variety of low-tech lab techniques. (Open labs after field trip)
    • Use multiple statistical techniques to analyze data for patterns. (Labs 4-8, final presentation/paper)
    • Interpret statistical analyses to distinguish between strong and weak interactions in a biological system. (Labs 4-7, final presentation/paper)
    • Develop and present a conference-style presentation in a public forum. (Lab 8, final presentation/paper)
    • Write a publication-ready research paper communicating findings and displaying data. (Lab 8, final presentation/paper)
  • “The outcome of the Central Dogma is not always intuitive” Variation in gene size does not necessarily correlate with variation in protein size. Here, two related genes differ in length due to a deletion mutation that removes four nucleotides. Many students do not predict that the smaller gene, after transcription and translation, would produce a larger protein.

    Predicting and classifying effects of insertion and deletion mutations on protein coding regions

    Learning Objectives
    Students 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
  • Hydrozoan polyps on a hermit-crab shell (photo by Tiffany Galush)

    A new approach to course-based research using a hermit crab-hydrozoan symbiosis

    Learning Objectives
    Students will be able to:
    • define different types of symbiotic interactions, with specific examples.
    • summarize and critically evaluate contemporary primary literature relevant to ecological symbioses, in particular that between hermit crabs and Hydractinia spp.
    • articulate a question, based on observations of a natural phenomenon (in this example, the hermit crab-Hydractinia interaction).
    • articulate a testable hypothesis, based on their own observations and read of the literature.
    • design appropriate experimental or observational studies to address their hypotheses.
    • collect and interpret data in light of their hypotheses.
    • problem-solve and troubleshoot issues that arise during their experiment.
    • communicate scientific results, both orally and in written form.
  • ACTN3 from

    The ACTN3 Polymorphism: Applications in Genetics and Physiology Teaching Laboratories

    Learning Objectives
    1. Test hypotheses related to the role of ACTN3 in skeletal muscle function.
    2. Explain how polymorphic variants of the ACTN3 gene affect protein structure and function.
    3. List and explain the differences between fast twitch and slow twitch muscle fibers.
    4. List and explain possible roles of the ACTN3 protein in skeletal muscle function.
    5. Find and analyze relevant scientific publications about the relationship between ACTN3 genotype and muscle function.
    6. Formulate hypotheses related to the relationship between ACTN3 genotype and skeletal muscle function.
    7. Design experiments to test hypotheses about the role of ACTN3 in skeletal muscle function.
    8. Statistically analyze experimental results using relevant software.
    9. Present experimental results in writing.
  • 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.
  • A A student assists Colorado Parks & Wildlife employees spawning greenback cutthroat trout at the Leadville National Fish Hatchery; B greenback cutthroat trout adults in a hatchery raceway; C tissue samples collected by students to be used for genetic analysis (images taken by S. Love Stowell)

    Cutthroat trout in Colorado: A case study connecting evolution and conservation

    Learning Objectives
    Students 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
  • 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.