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  • Students present their posters to classmates and instructors during a poster fair.

    Discovery Poster Project

    Learning Objectives
    Students will be able to:
    • identify and learn about a scientific research discovery of interest to them using popular press articles and the primary literature
    • find a group on campus doing research that aligns with their interests and communicate with the faculty leader of that group
    • create and present a poster that synthesizes their knowledge of the research beyond the discovery
  • Students use plastic Easter eggs and chocolate pieces to simulate the distribution of HIV in T lymphocytes.

    Infectious Chocolate Joy with a Side of Poissonian Statistics: An activity connecting life science students with subtle...

    Learning Objectives
    • Students will define a Poisson distribution.
    • Students will generate a data set on the probability of a T cell being infected with a virus(es).
    • Students will predict the likelihood of one observing the mean value of viruses occurring.
    • Students will evaluate the outcomes of a random process.
    • Students will hypothesize whether a process is Poissonian and design a test for that hypothesis.
    • Students will collect data and create a histogram from their data.
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    Air Quality Data Mining: Mining the US EPA AirData website for student-led evaluation of air quality issues

    Learning Objectives
    Students will be able to:
    • Describe various parameters of air quality that can negatively impact human health, list priority air pollutants, and interpret the EPA Air Quality Index as it relates to human health.
    • Identify an air quality problem that varies on spatial and/or temporal scales that can be addressed using publicly available U.S. EPA air data.
    • Collect appropriate U.S. EPA Airdata information needed to answer that/those questions, using the U.S. EPA Airdata website data mining tools.
    • Analyze the data as needed to address or answer their question(s).
    • Interpret data and draw conclusions regarding air quality levels and/or impacts on human and public health.
    • Communicate results in the form of a scientific paper.
  • 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
  • The Roc is a mythical giant bird of prey, first conceived during the Islamic Golden Age (~8th to 13th centuries CE), popularized in folk tales gathered in One Thousand One Nights. Rocs figured prominently in tales of Sinbad the Sailor. In this 1898 illustration by René Bull, the Roc is harassing two of Sinbad’s small fleet of ships. Illustration by René Bull is licensed under CC BY 2.0. (Source:

    A first lesson in mathematical modeling for biologists: Rocs

    Learning Objectives
    • Systematically develop a functioning, discrete, single-species model of an exponentially-growing or -declining population.
    • Use the model to recommend appropriate action for population management.
    • Communicate model output and recommendations to non-expert audiences.
    • Generate a collaborative work product that most individuals could not generate on their own, given time and resource constraints.
  • 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.