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  • 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.
  • 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.
  • “Phenology of a Dawn Redwood” – Images collected by students for this lesson pieced together illustrating a Metasequoia glyptostroboides changing color and dropping its leaves in the fall of 2017 on Michigan State University campus.

    Quantifying and Visualizing Campus Tree Phenology

    Learning Objectives
    The Learning Objectives of this lesson span across the entire semester.
    • Observe and collect information on phenological changes in local trees.
    • Become familiar with a database and how to work with large datasets.
    • Analyze and visualize data from the database to test their hypotheses and questions.
    • Develop a research proposal including empirically-driven questions and hypotheses.
    • Synthesize the results of their analysis in the context of plant biodiversity and local environmental conditions.
  • Students engaged in building the PCR model

    A Close-Up Look at PCR

    Learning Objectives
    At the end of this lesson students will be able to...
    • Describe the role of a primer in PCR
    • Predict sequence and length of PCR product based on primer sequences
    • Recognize that primers are incorporated into the final PCR products and explain why
    • Identify covalent and hydrogen bonds formed and broken during PCR
    • Predict the structure of PCR products after each cycle of the reaction
    • Explain why amplification proceeds exponentially
  • Simplified Representation of the Global Carbon Cycle,

    Promoting Climate Change Literacy for Non-majors: Implementation of an atmospheric carbon dioxide modeling activity as...

    Learning Objectives
    • Students will be able to manipulate and produce data and graphs.
    • Students will be able to design a simple mathematical model of atmospheric CO2 that can be used to make predictions.
    • Students will be able to conduct simulations, analyze, interpret, and draw conclusions about atmospheric CO2 levels from their own computer generated simulated data.
  • Using Place-Based Economically Relevant Organisms to Improve Student Understanding of the Roles of Carbon Dioxide,...

    Learning Objectives
    At the end of this lesson, students will be able to:
    • Describe the roles of light energy and carbon dioxide in photosynthetic organisms.
    • Identify the effect of nutrients on the growth of photosynthetic organisms.
    • Describe global cycles in atmospheric carbon dioxide levels and how they relate to photosynthetic organisms.
  • 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
  • Using phylogenetics to make inferences about historical biogeographic patterns of evolution.

    Building Trees: Introducing evolutionary concepts by exploring Crassulaceae phylogeny and biogeography

    Learning Objectives
    Students will be able to:
    • Estimate phylogenetic trees using diverse data types and phylogenetic models.
    • Correctly make inferences about evolutionary history and relatedness from the tree diagrams obtained.
    • Use selected computer programs for phylogenetic analysis.
    • Use bootstrapping to assess the statistical support for a phylogeny.
    • Use phylogenetic data to construct, compare, and evaluate the role of geologic processes in shaping the historical and current geographic distributions of a group of organisms.
  • A tuco-tuco in South America (photo credit: Jeremy Hsu)

    Furry with a chance of evolution: Exploring genetic drift with tuco-tucos

    Learning Objectives
    • Students will be able to explain how genetic drift leads to allelic changes over generations.
    • Students will be able to demonstrate that sampling error can affect every generation, which can result in random changes in allelic frequency.
    • Students will be able to explore and evaluate the effect of population size on the strength of genetic drift.
    • Students will be able to analyze quantitative data associated with genetic drift.