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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 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: https://en.wikipedia.org/wiki/Roc_(mythology)#mediaviewer/File:Rocweb.jpg)

    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.
  • 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.
  • Multiple sequence alignment of homologous cytochrome C protein sequences using Jalview viewer.

    Sequence Similarity: An inquiry based and "under the hood" approach for incorporating molecular sequence...

    Learning Objectives
    At the end of this lesson, students will be able to:
    • Define similarity in a non-biological and biological sense when provided with two strings of letters.
    • Quantify the similarity between two gene/protein sequences.
    • Explain how a substitution matrix is used to quantify similarity.
    • Calculate amino acid similarity scores using a scoring matrix.
    • Demonstrate how to access genomic data (e.g., from NCBI nucleotide and protein databases).
    • Demonstrate how to use bioinformatics tools to analyze genomic data (e.g., BLASTP), explain a simplified BLAST search algorithm including how similarity is used to perform a BLAST search, and how to evaluate the results of a BLAST search.
    • Create a nearest-neighbor distance matrix.
    • Create a multiple sequence alignment using a nearest-neighbor distance matrix and a phylogram based on similarity of amino acid sequences.
    • Use appropriate bioinformatics sequence alignment tools to investigate a biological question.
  • 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.
  • Simplified Representation of the Global Carbon Cycle, https://earthobservatory.nasa.gov/Features/CarbonCycle/images/carbon_cycle.jpg

    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 QIIME to Interpret Environmental Microbial Communities in an Upper Level Metagenomics Course

    Learning Objectives
    Students will be able to:
    • list and perform the steps of sequence processing and taxonomic inference.
    • interpret microbial community diversity from metagenomic sequence datasets.
    • compare microbial diversity within and between samples or treatments.
  • DNA Detective: Genotype to Phenotype. A Bioinformatics Workshop for Middle School to College. In this image, students are selecting the mutant Arabidopsis plant defective for the “mystery” gene that they identified and annotated through the DNA Subway Red Line.
  • DNA barcoding research in first-year biology curriculum

    CURE-all: Large Scale Implementation of Authentic DNA Barcoding Research into First-Year Biology Curriculum

    Learning Objectives
    Students will be able to: Week 1-4: Fundamentals of Science and Biology
    • List the major processes involved in scientific discovery
    • List the different types of scientific studies and which types can establish causation
    • Design experiments with appropriate controls
    • Create and evaluate phylogenetic trees
    • Define taxonomy and phylogeny and explain their relationship to each other
    • Explain DNA sequence divergence and how it applies to evolutionary relationships and DNA barcoding
    Week 5-6: Ecology
    • Define and measure biodiversity and explain its importance
    • Catalog organisms using the morphospecies concept
    • Geographically map organisms using smartphones and an online mapping program
    • Calculate metrics of species diversity using spreadsheet software
    • Use spreadsheet software to quantify and graph biodiversity at forest edges vs. interiors
    • Write a formal lab report
    Week 7-11: Cellular and Molecular Biology
    • Extract, amplify, visualize and sequence DNA using standard molecular techniques (PCR, gel electrophoresis, Sanger sequencing)
    • Explain how DNA extraction, PCR, gel electrophoresis, and Sanger sequencing work at the molecular level
    Week 12-13: Bioinformatics
    • Trim and assemble raw DNA sequence data
    • Taxonomically identify DNA sequences isolated from unknown organisms using BLAST
    • Visualize sequence data relationships using sequence alignments and gene-based phylogenetic trees
    • Map and report data in a publicly available online database
    • Share data in a formal scientific poster
  • Possible implementations of a short research module

    A Short Laboratory Module to Help Infuse Metacognition during an Introductory Course-based Research Experience

    Learning Objectives
    • Students will be able to evaluate the strengths and weaknesses of data.
    • Students will be able to employ prior knowledge in formulating a biological research question or hypothesis.
    • Students will be able to distinguish a research question from a testable hypothesis.
    • Students will recognize that the following are essential elements in experimental design: identifying gaps in prior knowledge, picking an appropriate approach (ex. experimental tools and controls) for testing a hypothesis, and reproducibility and repeatability.
    • Students will be able to identify appropriate experimental tools, approaches and controls to use in testing a hypothesis.
    • Students will be able to accurately explain why an experimental approach they have selected is a good choice for testing a particular hypothesis.
    • Students will be able to discuss whether experimental outcomes support or fail to support a particular hypothesis, and in the case of the latter, discuss possible reasons why.
  • blind cave fish
  • “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