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Bioinformatics and Genomics: Computational and experimental methods in bioinformatics and genomics, and how these methods provide insights into protein structure and function, molecular evolution, biological diversity, cell biology and human disease. Topics include database searching, multiple sequence alignment, molecular phylogeny, microarrays, proteomics and pharmacogenomics.

Quantitative Optical Imaging: Explores the fundamentals of optical imaging in biology. Covered topics include an introduction to optics and microscopes, fluorescence microscopy and image analysis. MATLAB will be taught throughout the course and used for image processing.

Explores the principles and emergent properties of collective dynamics through computational modeling and theory. Focuses on multi-agent systems using insights from biology, like the self-assemblage of cells and insect colony behavior. Topics include designing swarm intelligence, networked agents, cellular computing and self-assembly, optimization, synchronization, and evolutionary computation. Uses cross-discipline research developments to practice applied techniques. Biology background is not required.

Statistical Collaboration: Educates and trains students to become effective interdisciplinary collaborators by developing the communication and collaboration skills necessary to apply technical statistics and data science skills to help domain experts answer research questions. Topics include structuring effective meetings and projects; communicating statistics to non-statisticians; using peer feedback, self-reflection and video analysis to improve collaboration skills; creating reproducible statistical workflows; working ethically.

Statistical and Computational Analysis of the Human Genome: This lab course covers fundamental statistical and computational approaches to large scale data. Students will learn the unix command line to: access public human genome data, learn what statistics apply to which types of data and apply them to study specific regions of the human genome involved in development and disease. This lab course will cover fundamental aspects of Virtual computing, Container analysis pipelines (e.g. NextFlow, GitHub) in an intuitive and practical learning framework.

Training in handling issues such as conflicts of interest, mentor/mentee relationships, peer review, research misconduct and authorship and publication. Reviewing policies regarding human and animal subjects, safe laboratory practices. Instruction on conducting quality research including collaborative research, the scientist as a responsible member of society, ethical issues, rigor and reproducibility which will include effective design, execution, analysis, interpretation and communication.

These “gap‐filling” courses give you an opportunity to broaden your capabilities and explore areas of quantitative biology that are new to you. Courses from any of our participating departments can be chosen, depending on your background and future research interests. Undergraduate coursework may be taken for one of the two courses; however, the graduate school will not allow undergraduate credits to apply toward the credits required for a Ph.D. by the Graduate School. See a more detailed description of gap-filling courses.