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Biochemistry And Molecular Biology

  • Binding pocket diagram The image suggests that by providing appropriate non-covalent interactions at sites A, B and C, students can create a binding pocket selective for the neurotransmitter molecule serotonin.

    Serotonin in the Pocket: Non-covalent interactions and neurotransmitter binding

    Learning Objectives
    • Students will design a binding site for the neurotransmitter serotonin.
    • Students will be able to determine the effect of a change in molecular orientation on the affinity of the molecule for the binding site.
    • Students will be able to determine the effect of a change in molecular charge on the affinity of the molecule for the binding site.
    • Students will be able to better differentiate between hydrogen bond donors and acceptors.
    • Students can use this knowledge to design binding sites for other metabolites.
  • 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.