Global Arc

An overview of research topics and methods in quantitative biology through reading and discussion of primary literature. Students read two papers weekly, each showcasing how modern experimental and analytical techniques are applied to address basic questions in biology with a strong focus on big data. Students examine the achievements and impact of each study, present context and background, dissect experimental and analytical approaches, and highlight remaining challenges. Topics range from gene regulation and organellar dynamics to virology and cancer genomics. Prereqs: MOL 214 or equivalent or permission of the instructors.

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 applied statistics and data science for those interested in pursuing data-driven research. The course may involve examples from any area of science, but it places a special emphasis on modern biological problems and data sets. Topics may include data wrangling, exploration and visualization, statistical programming, likelihood based inference, Bayesian inference, bootstrap, EM algorithm, regularization, statistical modeling, principal components analysis, multiple hypothesis testing, and causality. The statistical programming language R will be extensively used to explore methods and analyze data.

This interdisciplinary course provides a broad overview of computational and experimental approaches to decipher genomes and characterize molecular systems. We focus on methods for analyzing "omics" data, such as genome and protein sequences, gene expression, proteomics and molecular interaction networks. We cover algorithms used in computational biology, key statistical concepts (e.g., basic probability distributions, significance testing, multiple testing correction, performance evaluation), and machine learning methods which have been applied to biological problems (e.g., classification techniques, hidden Markov models, clustering).

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.