The course provides an in depth treatment of biopolymer chemistry and natural products biosynthesis. Topics include: nucleic acid and protein chemistry; biopolymer engineering; the logic and enzymology of natural product biosynthesis with a focus on non-ribosomal peptide synthetases and polyketide synthases.

The course is intended for students interested in biological applications of mathematics and modeling in biology, aiming at demonstrating how relatively simple mathematics can be applied to a variety of models to draw interesting conclusions. Connections will be made between diverse biological examples linked by common mathematical themes. A variety of discrete and continuous ordinary and partial differential equation models will be explored.

This course establishes a foundation in the application of statistics to problems in genetics and genomics through lectures, homework sets, and class discussions of publications. Statistical topics may include probabilistic modeling, likelihood based inference, Bayesian inference, bootstrap, EM algorithm, regularization, statistical modeling, principal components analysis, multiple hypothesis testing, and causality. There is an emphasis on applications in population genetics, gene expression, and human genomics. The statistical programming language R is extensively used to explore methods and analyze data.

Aging is a fascinating biological phenomenon because it seems inevitable, yet recent research suggests that longevity can be manipulated through genetics and environment. Moreover, aging is the major risk factor for a host of chronic and neurological diseases; thus, understanding the molecular regulation of aging will be critical in addressing these health issues in the future. We will explore the current state of the field, including genetic discoveries of longevity mutants, cell biological and metabolic characterization of aging animals, and genomic and computational analyses used to uncover molecular mechanisms that control longevity.

This course establishes a foundation in the application of statistics to problems in genetics and genomics through lectures, homework sets, and class discussions of publications. Statistical topics may include probabilistic modeling, likelihood based inference, Bayesian inference, bootstrap, EM algorithm, regularization, statistical modeling, principal components analysis, multiple hypothesis testing, and causality. There is an emphasis on applications in population genetics, gene expression, and human genomics. The statistical programming language R is extensively used to explore methods and analyze data.