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Molecular and Cell Biology I
Mark Siegal and Sevinc Ercan
offered each Fall
In-depth study of cell biology, with an emphasis on the molecular aspects of cell function. Topics include protein structure and synthesis, gene expression and its regulation, cell replication, and specialized cell structure and function. The course provides an introduction to genomics and bioinformatics and examines developmental biology, evolution, and systems biology.
Life Science: Genomes and Diversity
offered Spring alternating years
Millions of species of animals, plants and microbes inhabit our planet. Genomics, the study of all the genes in an organism, is providing new insights into this amazing diversity of life on Earth. We begin with the fundamentals of DNA, genes and genomes. We then explore microbial diversity, with an emphasis on how genomics can reveal many aspects of organisms, from their ancient history to their physiological and ecological habits. We follow with examinations of animal and plant diversity, focusing on domesticated species, such as dogs and tomatoes, as examples of how genomic methods can be used to identify genes that underlie new or otherwise interesting traits. Genomics has also transformed the study of human diversity and human disease. We examine the use of DNA to trace human ancestry, as well as the use of genomics as a diagnostic tool in medicine. With the powerful new technologies to study genomes has come an increased power to manipulate them. We conclude by considering the societal implications of this ability to alter the genomes of crop plants, livestock and potentially humans.
As part of the Genomes and Diversity course available through the Open Education Project, Mark has created patternGenes, an interactive computer simulation that teaches how gene-activity data are analyzed by hierarchical clustering. There are several good online animations of how genome-wide gene-activity data are gathered using approaches such as DNA microarrays (see, for example, this one, this one, and this one). patternGenes is different because it focuses on how to extract meaning from such a vast amount of data. It does so using a computer-game-like format. The development of patternGenes was supported by a National Science Foundation CAREER award.