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  • Structure of protein ABCB6

    Investigating the Function of a Transport Protein: Where is ABCB6 Located in Human Cells?

    Learning Objectives
    At the end of this activity students will be able to:
    • describe the use of two common research techniques for studying proteins: SDS-PAGE and immunoblot analysis.
    • determine a protein’s subcellular location based on results from: 1) immunoblotting after differential centrifugation, and 2) immunofluorescence microscopy.
    • analyze protein localization data based on the limitations of differential centrifugation and immunofluorescence microscopy.
  • Binding pocket diagram The image suggests that by providing appropriate non-covalent interactions at sites A, B and C, students can create a binding pocket selective for the neurotransmitter molecule serotonin.

    Serotonin in the Pocket: Non-covalent interactions and neurotransmitter binding

    Learning Objectives
    • Students will design a binding site for the neurotransmitter serotonin.
    • Students will be able to determine the effect of a change in molecular orientation on the affinity of the molecule for the binding site.
    • Students will be able to determine the effect of a change in molecular charge on the affinity of the molecule for the binding site.
    • Students will be able to better differentiate between hydrogen bond donors and acceptors.
    • Students can use this knowledge to design binding sites for other metabolites.
  • Images of students participating in the SIDE activity

    Using a Sequential Interpretation of Data in Envelopes (SIDE) approach to identify a mystery TRP channel

    Learning Objectives
    • Students will be able to analyze data from multiple experimental methodologies to determine the identity of their "mystery" TRP channel.
    • Students will be able to interpret the results of individual experiments and from multiple experiments simultaneously to identify their "mystery" TRP channel.
    • Students will be able to evaluate the advantages and limitations of experimental methodologies presented in this lesson.
  • Genome view obtained from the integrated genome viewer: screenshot of Illumina 75bp single-end reads from two rockfishes Sebastes chrysomelas (top) and S. carnatus (bottom) aligned to a closely related reference genome (S. rubrivinctus).  Reads shown are within the coding region of a gene that was located in an island of genomic divergence between the two species.  The CT mutation within S. carnatus is predicted to cause an amino acid substitution from Lysine to Phenylalanine in a taste receptor gene.  This

    An Introduction to Eukaryotic Genome Analysis in Non-model Species for Undergraduates: A tutorial from the Genome...

    Learning Objectives
    At the end of the activity, students will be able to:
    • Explain the steps involved in genome assembly, annotation, and variant detection to other students and instructors.
    • Create meaningful visualizations of their data using the integrated genome viewer.
    • Use the Linux command line and web-based tools to answer research questions.
    • Produce annotated genomes and call variants from raw sequencing reads in non-model species.
  • 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)
  • 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.
  • Graphic of structured decision making process

    Using Structured Decision Making to Explore Complex Environmental Issues

    Learning Objectives
    Students will be able to:
    1. Describe the process, challenges, and benefits of structured decision making for natural resource management decisions.
    2. Explain and reflect on the role of science and scientists in structured decision making and how those roles interact and compare to the roles of other stakeholders.
    3. Assess scientific evidence for a given management or policy action to resolve an environmental issue.
  • 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
  • The MAP Kinase signal transduction pathway

    Cell Signaling Pathways - a Case Study Approach

    Learning Objectives
    • Use knowledge of positive and negative regulation of signaling pathways to predict the outcome of genetic modifications or pharmaceutical manipulation.
    • From phenotypic data, predict whether a mutation is in a coding or a regulatory region of a gene involved in signaling.
    • Use data, combined with knowledge of pathways, to make reasonable predictions about the genetic basis of altered signaling pathways.
    • Interpret and use pathway diagrams.
    • Synthesize information by applying prior knowledge on gene expression when considering congenital syndromes.
  • 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.
  • “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
  • 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.
  • Building a Model of Tumorigenesis: A small group activity for a cancer biology/cell biology course

    Learning Objectives
    At the end of the activity, students will be able to:
    • Analyze data from a retrospective clinical study uncovering genetic alterations in colorectal cancer.
    • Draw conclusions about human tumorigenesis using data from a retrospective clinical study.
    • Present scientific data in an appropriate and accurate way.
    • Discuss why modeling is an important practice of science.
    • Create a simple model of the genetic changes associated with a particular human cancer.
  • 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 preforming the leaky neuron activity.

    The Leaky Neuron: Understanding synaptic integration using an analogy involving leaky cups

    Learning Objectives
    Students will able to:
    • compare and contrast spatial and temporal summation in terms of the number of presynaptic events and the timing of these events
    • predict the relative contribution to reaching threshold and firing an action potential as a function of distance from the axon hillock
    • predict how the frequency of incoming presynaptic action potentials effects the success of temporal summation of resultant postsynaptic potentials