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.

This course focuses on computational methods in cryo-EM, including three-dimensional ab-initio modelling, structure refinement, resolving structural variability of heterogeneous populations, particle picking, model validation, and resolution determination. Special emphasis is given to methods that play a significant role in many other data science applications. These comprise of key elements of statistical inference, image processing, optimization, and dimensionality reduction. The software packages RELION and ASPIRE are routinely used for class demonstration on both simulated and publicly available experimental datasets.

Advances in molecular biology and computation have propelled the study of genomics forward, including how genes are organized and how their regulation manifests complex phenotypes. A hallmark of genomics is the production and analysis of large data sets. This course will pair an overview of genomics with practical instruction in the analytical techniques required to use it in research and medicine. We will start with a primer on genetics and an introduction to programming using Python. The goal of this course is to provide a foundation for understanding the data heavy experiments that are increasingly common in biomedical research.

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.

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.