Courses

Required Courses

The BIG program requires that each student completes the 5 required courses listed below.

G1 Fall

BBS Course - Genetics 201: Principles of Genetics

Faculty: Fred Winston (Medical School), Maxwell G. Heiman (Medical School), Ann Hochschild (Medical School), Mitzi I. Kuroda (Medical School), and Steven A. McCarroll (Medical School)

Description: An in-depth survey of genetics, beginning with basic principles and extending to modern approaches and special topics. We will draw on examples from various systems, including yeast, Drosophila, C. elegans, mouse, human and bacteria.

BBS Course - BBS 230: Analysis of the Biological Literature

Faculty: Adrian Salic (Medical School), Welcome W. Bender (Medical School), Michael Demian Blower (Medical School), et al.

Description: Students participate in intensive small group discussions focused on the critical analysis of basic research papers from a wide range of fields including biochemistry, cell and developmental biology, genetics, and microbiology. Papers are discussed in terms of their background, significance, hypothesis, experimental methods, data quality, and interpretation of results. Students will be asked to propose future research directions, to generate new hypotheses and to design experiments aimed at testing them.

BIG Course - Biophysics 170: Quantitative Genomics

Faculty: Shamil R. Sunyaev (Medical School), Leonid Mirny (Medical School), and guest lecturers.

Description: In-depth study of genomics:

  • models of evolution and population genetics
  • comparative genomics: analysis and comparison
  • structural genomics: protein structure, evolution and interactions
  • functional genomics: gene expression, structure and dynamics of regulatory networks.

G1 Spring

BIG Course - Biophysics 205: Computational and Functional Genomics

FacultyMartha L. Bulyk (Medical School), Suzanne Gaudet (Medical School), and Shamil R. Sunyaev (Medical School)

Description: Experimental functional genomics, computational prediction of gene function, and properties and models of complex biological systems. The course will primarily involve critical reading and discussion rather than lecture.

G2 Fall

BBS Course - BCMP 200: Molecular Biology

Faculty: Richard Ian Gregory (Medical School), Kami Ahmad (Medical School), Paul J. Anderson (Medical School), Joseph John Loparo (Medical School), Johannes Walter (Medical School), and Timur Yusufzai (Medical School)

Description: An advanced treatment of molecular biology's Central Dogma. Considers the molecular basis of information transfer from DNA to RNA to protein, using examples from eukaryotic and prokaryotic systems. Lectures, discussion groups, and research seminars.

Recommended: BIG Course – BMI 701: Introduction to Biomedical Informatics

Faculty: Adam Wright (Medical School)

Description: This is an introductory course for medical students that surveys methods in biomedical informatics, including methods and approaches used in clinical informatics, bioinformatics, imaging and population health informatics. Each week a guest lecturer delivers a two hour presentation with slides on a new topic.

Old syllabus: syllabus-bmi-701-2012.pdf

G2 Spring

Recommended: BIG Course – BMI 702: Introduction to Biomedical Informatics

Faculty: Adam Wright (Medical School)

Description: This is an introductory course for medical students that surveys methods in biomedical informatics, including methods and approaches used in clinical informatics, bioinformatics, imaging and population health informatics. Each week a guest lecturer delivers a two hour presentation with slides on a new topic.

Electives

The BIG program requires 8 total courses. Elective courses can be chosen from the entire course catalogs from Harvard and MIT. A list of suggested electives is provided below.

Harvard School of Public Health - EPI 511: Advanced Population & Medical Genetics

Faculty: Alkes Price (School of Public Health)

Description: This course will cover quantitative topics in human population genetics and applications to medical genetics, including the HapMap project, linkage disequilibrium, population structure and stratification, population admixture, admixture mapping, and natural selection. The course is aimed at Epidemiology and Biostatistics students with a strong interest in statistical genetics. The course will emphasize hands-on analysis of large empirical data sets, thus requiring prior experience with a general-purpose high-level programming language such as Python or PERL. After taking this course, each student will have the experience and skills to develop and apply statistical methods to population genetic data. Course Prerequisite(s): BIO227 and BIO510

Harvard School of Public Health - BIOSTAT 249: Bayesian Methodology in Biostatistics

Faculty: Corwin Zigler (Public Health)

Description: General principles of the Bayesian approach, prior distributions, hierarchical models and modeling techniques, approximate inference, Markov chain Monte Carlo methods, model assessment and comparison. Bayesian approaches to GLMMs, multiple testing, nonparametrics, clinical trials, survival analysis.

Harvard University - Statistics 210a: Probability Theory

Faculty: Jun S. Liu and Carl N. Morris

Description: Random variables, measure theory, reasoning by representation. Families of distributions: Multivariate Normal, conjugate, marginals, mixtures. Conditional distributions and expectation. Convergence, laws of large numbers, central limit theorems, and martingales. Prerequisites: Statistics 110 or equivalent required; Statistics 111 or equivalent recommended.

Harvard University - Statistics 211: Statistical Inference

Faculty: Tirthankar Dasgupta

Description: Inference: frequency, Bayes, decision analysis, foundations. Likelihood, sufficiency, and information measures. Models: Normal, exponential families, multilevel, and non-parametric. Point, interval and set estimation; hypothesis tests. Computational strategies, large and moderate sample approximations. Prerequisites: Statistics 111 and 210a or equivalent.

Harvard University - Statistics 220: Bayesian Data Analysis

Faculty: Jun S. Liu

Description: Basic Bayesian models, followed by more complicated hierarchical and mixture models with nonstandard solutions. Includes methods for monitoring adequacy of models and examining sensitivity of models.

Harvard University - Statistics 230: Multivariate Statistical Analysis

Faculty: S. C. Samuel Kou

Description: Multivariate inference and data analysis. Advanced matrix theory and distributions, including Multivariate Normal, Wishart, and multilevel models. Supervised learning: multivariate regression, classification, and discriminant analysis. Unsupervised learning: dimension reduction, principal components, clustering, and factor analysis. Prerequisites: Statistics 110 and 111.