The course provides an in-depth treatment of nucleic acid and peptide/protein chemical biology. Topics include: nucleic acid and protein synthesis; biopolymer engineering using modern chemical biology approaches; biopolymer function and regulation in biological systems.

Introduces students to the promise and challenges of AI and machine learning methods, including large language models, in precision health. Topics covered will include analysis of genomes, omics, and clinical data, as well as learning from multiple data sources. No prior knowledge of biology or medicine is required; an introduction to these topics and the nature of biological data will be provided. In depth knowledge of AI is not required, but students should have basic familiarity with coding and computer science.

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 explores the biochemical foundations of human physiology and how it is disturbed in disease. We discuss the roles of metabolic, the cardiovascular, and immune systems in various diseases, particularly cancer. Specific topics include: the functions of the major organ systems, and how we measure and model their activity; nutrition and the maintenance of metabolic homeostasis; the anti-tumor immune response; the origins, consequences, and major treatment paradigms of cancer; and the process of translating basic science into novel therapies. The class will consist of lectures and student-led discussions of scientific papers.

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.

Mandatory first-year graduate course centered around the weekly QCB seminar series, intended to help develop competency in critical reading and assessment of academic literature across subfields early in graduate training. Class meetings comprise student-driven presentations and discussions surveying research topics relevant to upcoming talks, with an emphasis on latest methodologies and debates. Assessment includes seminar and class attendance, in-class and in-seminar participation, and peer evaluation.

This course explores the biochemical foundations of human physiology and how it is disturbed in disease. We discuss the roles of metabolic, the cardiovascular, and immune systems in various diseases, particularly cancer. Specific topics include: the functions of the major organ systems, and how we measure and model their activity; nutrition and the maintenance of metabolic homeostasis; the anti-tumor immune response; the origins, consequences, and major treatment paradigms of cancer; and the process of translating basic science into novel therapies. The class consists of lectures and student-led discussions of scientific papers.