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  • 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
  • Fully annotated mitochondrial genome of a lichenized fungal species (Cladonia subtenuis).  This represents a visual representation of the final project result of the lesson plan. Students will submit their annotation to NCBI (GenBank) and upon acceptance of their annotation, they typically add this publicly available resource into their resume.

    A CURE-based approach to teaching genomics using mitochondrial genomes

    Learning Objectives
    • Install the appropriate programs such as Putty and WinSCP.
    • Navigate NCBI's website including their different BLAST programs (e.g., blastn, tblastx, blastp and blastx)
    • Use command-line BLAST to identify mitochondrial contigs within a whole genome assembly
    • Filter the desired sequence (using grep) and move the assembled mitochondrial genome onto your own computer (using FTP or SCP)
    • Error-correct contigs (bwa mem, samtools tview), connect and circularize organellar contigs (extending from filtered reads)
    • Transform assembled sequences into annotated genomes
    • Orient to canonical start locations in the mitochondrial genome (cox1)
    • Identify the boundaries of all coding components of the mitochondrial genome using BLAST, including: Protein coding genes (BLASTx and tBLASTX), tRNAs (proprietary programs such as tRNAscan), rRNAs (BLASTn, Chlorobox), ORFs (NCBI's ORFFinder)
    • Deposit annotation onto genome repository (NCBI)
    • Update CV/resume to reflect bioinformatics skills learned in this lesson
  • 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.
  • Using the Cell Engineer/Detective Approach to Explore Cell Structure and Function

    Learning Objectives
    Students will be able to:
    • Identify the major cell organelles
    • List the major functions of the organelles
    • Predict how changes in organelle/cell structure could alter cellular function
    • Explain how overall cellular function is dependent upon organelles/cell structure
    • Relate cell structure to everyday contexts
  • Modeling the Research Process: Authentic human physiology research in a large non-majors course

    Learning Objectives
    Students will be able to:
    • Read current scientific literature
    • Formulate testable hypotheses
    • Design an experimental procedure to test their hypothesis
    • Make scientific observations
    • Analyze and interpret data
    • Communicate results visually and orally