III. Quantitative Biology Core (students choose one or more)
STAT/BIOS 801: Statistical Methods in Research
This course presents statistical concepts and statistical methodology useful in descriptive, experimental, and analytical study of biological and other natural phenomena. Most graduate students in the life sciences are interested in this course as a springboard to understanding how to analyze data collected by a wide variety of experimental approaches. The course focuses on practical application of statistics rather than on statistical theory.
STAT/BIOC 842: Computational Biology
Databases, high-throughput biology, literature mining, gene expression, next-generation sequencing, proteomics, metabolomics, systems biology, and biological networks. This course is designed first of all for biology, agronomy and statistics students. However, computer science, mathematics, physics and chemistry majors also may find it beneficial. This course is designed to benefit computational and experimental biologists as well as biostatisticians to understand the principles of analyzing biological data, building models and testing hypotheses using computer science paradigms. This Course does not depend on any graduate course.
STAT/BIOC 843: Next generation sequencing and systems biology
Next-generation RNA and genome sequencing, systems biology. Regulatory networks of transcription, protein-protein interaction networks, theory and practice. Databases, data integration and visualization. Students present computational biology publications and projects.
BIOC 836: Physical Basis of Macromolecular Function.
This course introduces the theory and practice of biophysical characterization of macromolecules. It is based on primary research literature and covers the thermodynamics of protein folding and ligand binding, macromolecular energetics, absorbance and fluorescence spectroscopy, X-ray diffraction, nuclear magnetic resonance, and macromolecular hydrodynamics. Students learn how to apply quantitative approaches to their study of systems in vitro and in a cellular context, and how to analyze and interpret data using these specialized methodologies.