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• ### 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.

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)
• ### 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.

• ### An undergraduate bioinformatics curriculum that teaches eukaryotic gene structure

Learning Objectives
Module 1
• Demonstrate basic skills in using the UCSC Genome Browser to navigate to a genomic region and to control the display settings for different evidence tracks.
• Explain the relationships among DNA, pre-mRNA, mRNA, and protein.
Module 2
• Describe how a primary transcript (pre-mRNA) can be synthesized using a DNA molecule as the template.
• Explain the importance of the 5' and 3' regions of the gene for initiation and termination of transcription by RNA polymerase II.
• Identify the beginning and the end of a transcript using the capabilities of the genome browser.
Module 3
• Explain how the primary transcript generated by RNA polymerase II is processed to become a mature mRNA, using the sequence signals identified in Module 2.
• Use the genome browser to analyze the relationships among:
• pre-mRNA
• 5' capping
• splicing
• mRNA
Module 4
• Identify splice donor and acceptor sites that are best supported by RNA-Seq data and TopHat splice junction predictions.
• Utilize the canonical splice donor and splice acceptor sequences to identify intron-exon boundaries.
Module 5
• Determine the codons for specific amino acids and identify reading frames by examining the Base Position track in the genome browser.
• Assemble exons to maintain the open reading frame (ORF) for a given gene.
• Define the phases of the splice donor and acceptor sites and describe how they impact the maintenance of the ORF.
• Identify the start and stop codons of an assembled ORF.
Module 6
• Demonstrate how alternative splicing of a gene can lead to different mRNAs.
• Show how alternative splicing can lead to the production of different polypeptides and result in drastic changes in phenotype.
• ### A Hands-on Introduction to Hidden Markov Models

Learning Objectives
• Students will be able to process unannotated genomic data using ab initio gene finders as well as other inputs.
• Students will be able to defend the proposed gene annotation.
• Students will reflect on the other uses for HMMs.