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  • Human karyotype

    Homologous chromosomes? Exploring human sex chromosomes, sex determination and sex reversal using bioinformatics...

    Learning Objectives
    Students successfully completing this lesson will:
    • Practice navigating an online bioinformatics resource and identify evidence relevant to solving investigation questions
    • Contrast the array of genes expected on homologous autosomal chromosomes pairs with the array of genes expected on sex chromosome pairs
    • Use bioinformatics evidence to defend the definition of homologous chromosomes
    • Define chromosomal sex and defend the definition using experimental data
    • Investigate the genetic basis of human chromosomal sex determination
    • Identify at least two genetic mutations can lead to sex reversal
  • SNP model by David Eccles (gringer) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY 4.0 (http://creativecommons.org/licenses/by/4.0)], via Wikimedia Commons

    Exploration of the Human Genome by Investigation of Personalized SNPs

    Learning Objectives
    Students successfully completing this lesson will be able to:
    • Effectively use the bioinformatics databases (SNPedia, the UCSC Genome Browser, and NCBI) to explore SNPs of interest within the human genome.
    • Identify three health-related SNPs of personal interest and use the UCSC Genome Browser to define their precise chromosomal locations and determine whether they lie within a gene or are intergenic.
    • Establish a list of all genome-wide association studies correlated with a particular health-related SNP.
    • Predict which model organism would be most appropriate for conducting further research on a human disease.
  • Adult female Daphnia dentifera. Daphnia spp. make a great study system due to their transparent body and their ease of upkeep in a lab.

    Dynamic Daphnia: An inquiry-based research experience in ecology that teaches the scientific process to first-year...

    Learning Objectives
    Students will be able to:
    • Construct written predictions about 1 factor experiments.
    • Interpret simple (2 variables) figures.
    • Construct simple (2 variables) figures from data.
    • Design simple 1 factor experiments with appropriate controls.
    • Demonstrate proper use of standard laboratory items, including a two-stop pipette, stereomicroscope, and laboratory notebook.
    • Calculate means and standard deviations.
    • Given some scaffolding (instructions), select the correct statistical test for a data set, be able to run a t-test, ANOVA, chi-squared test, and linear regression in Microsoft Excel, and be able to correctly interpret their results.
    • Construct and present a scientific poster.
  • A three-dimensional model of methionine is superimposed on a phase contrast micrograph of Saccharomyces cerevisiae from a log phase culture.

    Follow the Sulfur: Using Yeast Mutants to Study a Metabolic Pathway

    Learning Objectives
    At the end of this lesson, students will be able to:
    • use spot plating techniques to compare the growth of yeast strains on solid culture media.
    • predict the ability of specific met deletion strains to grow on media containing various sulfur sources.
    • predict how mutations in specific genes will affect the concentrations of metabolites in the pathways involved in methionine biosynthesis.
  • pClone Red Makes Research Look Easy

    Using Synthetic Biology and pClone Red for Authentic Research on Promoter Function: Genetics (analyzing mutant...

    Learning Objectives
    • Describe how cells can produce proteins at the right time and correct amount. 
    • Diagram a bacterial promoter with −35 and −10 elements and the transcription start site.
    • Describe how mutational analysis can be used to study promoter sequence requirements.
    • Develop a promoter mutation hypothesis and design an experiment to test it.
    • Successfully and safely manipulate DNA and Escherichia coli for ligation and transformation experiments. 
    • Design an experiment to verify a mutated promoter has been cloned into a destination vector. 
    • Design an experiment to measure the strength of a promoter. 
    • Analyze data showing reporter protein produced and use the data to assess promoter strength. 
    • Define type IIs restriction enzymes.
    • Distinguish between type II and type IIs restriction enzymes.
    • Explain how Golden Gate Assembly (GGA) works.
    • Measure the relative strength of a promoter compared to a standard promoter.  
  • Image from http://www.epa.gov/airdata/ad_maps.html

    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.
  • Set Up Fly Traps: The photo is of the fly traps after being set up for the experiment

    Gotcha! Which fly trap is the best? An introduction to experimental data collection and analysis

    Learning Objectives
    Students will:
    • design and execute an experiment
    • collect, organize, and summarize data
    • analyze and interpret data and make inferences
  • 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.
  • pClone Red Makes Research Look Easy

    Using Synthetic Biology and pClone Red for Authentic Research on Promoter Function: Introductory Biology (identifying...

    Learning Objectives
    • Describe how cells can produce proteins at the right time and correct amount.
    • Diagram how a repressor works to reduce transcription.
    • Diagram how an activator works to increase transcription.
    • Identify a new promoter from literature and design a method to clone it and test its function.
    • Successfully and safely manipulate DNA and Escherichia coli for ligation and transformation experiments.
    • Design an experiment to verify a new promoter has been cloned into a destination vector.
    • Design an experiment to measure the strength of a promoter.
    • Analyze data showing reporter protein produced and use the data to assess promoter strength.
    • Define type IIs restriction enzymes.
    • Distinguish between type II and type IIs restriction enzymes.
    • Explain how Golden Gate Assembly (GGA) works.
    • Measure the relative strength of a promoter compared to a standard promoter.
  • 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.
  • 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
  • Students working with fruit flies in the classroom.

    Fruit Fly Genetics in a Day: A Guided Exploration to Help Many Large Sections of Beginning Students Uncover the Secrets...

    Learning Objectives
    • Students will be able to handle and anesthetize Drosophila fruit flies.
    • Students will be able to use a dissecting microscope to sex Drosophila fruit flies.
    • Students will implement some steps of the scientific method.
    • Students will successfully predict the results of sex-linked genetics crosses.
    • Students will interpret genetic data.
  • 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.