Curriculum

Required Courses


  1. BBS 230: Analysis of the Biological Literature Critical analysis of original research articles in intensive small group discussions. Analyze range of papers in biochemistry, genetics, microbiology, and cell and developmental biology in terms of context, hypotheses, methods, results and future experiments.

 

  1. BBS 330: Critical Thinking and Research Proposal Writing A small group tutorial systematically guiding students in the writing of original, hypothesis-driven research proposals from initial topic selection through completion of a final draft. (Current G2’s and below).

 

  1. Genetics 201: Principles of Genetics 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.
  2.  

  3. One course in statistics/quantitative biology (see options below)
  4.  

  5. Two advanced electives in genetics or genomics (see options below)

Statistics/Quantitative Biology Courses


Biophysics 170:  Quantitative Genomics
Introduction to quantitative modeling and analysis of genome evolution, functional and structural genomic data. Strong emphasis on evolutionary understanding and analysis.
The course provide foundation in the following four areas: Molecular evolutionary and Population Genetics, Comparative Genomics,  Functional Genomics, and  Structural Genomics.

 

Genetics 229 Computational Statistics for Biomedical Sciences 
Analysis of large datasets has become an integral part of biological and biomedical sciences. This course will provide a practical introduction to data analysis, with high-throughput sequencing data as the main source of examples. In the first half, it will cover basic statistical concepts and techniques, including hypothesis testing, nonparametric methods, principal component analysis, correlation analysis, and linear regression. In the second half, it will cover several advanced topics, focusing on issues that one encounters in the literature but are seldom covered in introductory statistics courses. To carry out statistical tests and visualize data, students will learn R, a powerful programming language for statistical computing and graphics. The class will be a combination of lectures and computer labs. We will use recent literature to motivate the statistical methods, and assignments will frequently include attempts to reproduce published findings.

 

MCB 112 - Biological Data Analysis
Biology has become a computational science, requiring analysis of large data sets from genomics, imaging, and other technologies. This course teaches computational methods in biological data analysis, using an empirical and experimental framework suited to the complexities of biological data, emphasizing computational control experiments. The course is primarily aimed at biologists learning computational methods, but is also suited for computational statistical scientists learning about biological data.

 

BST 282 - Introduction to Computational Biology and Bioinformatics
Basic biological problems, genomics technology platforms, algorithms and data analysis approaches in computational biology. There will be three major components of the course: microarray and RNA-seq analysis, transcription and epigenetic gene regulation, cancer genomics.This course is targeted at both biostatistics and biological science graduate students with some statistics and computer programming background who have an interest in exploring genomic data analysis and algorithm development as a potential future direction.

 

STAT 139 - Statistical Sleuthing Through Linear Models
A serious introduction to statistical inference with linear models and related methods. Topics include t-tools and nonparametric alternatives (including bootstrapping and permutation-based methods), multiple-group comparisons, analysis of variance, linear regression, model checking and refinement, and causation versus correlation. Emphasis on thinking statistically, evaluating assumptions, and developing tools for real-life applications.

 

BIOSTAT 281 - Genomic Data Manipulation
Introduction to genomic data, computational methods for interpreting these data, and survey of current functional genomics research. Covers biological data processing, programming for large datasets, high-throughput data (sequencing, proteomics, expression, etc.), and related publications.

 

Courses through the Biostatistics Department at the Harvard School of Public Health may also be applicable.


Advanced Electives


Gen 202:  Principles of Genetic Analysis in Humans

The goal of this course is to familiarize the students with the principles of human genetics and how they apply to modern research in disease gene identification. This course will provide a comprehensive examination of the principles of human inheritance, in the context of both normal human variation and human disease.  Examples of topics to be covered include structure of the human genome, analysis of sequence variation, population genetics, Mendelian inheritance patterns, complex traits, association studies, pharmacogenetics and how genetics impacts medicine.

 

Gen 216:  Advanced Topics in Gene Expression
Covers both biochemical and genetic studies in regulatory mechanisms. Small number of topics discussed in depth, using the primary literature. Topics range from prokaryotic transcription to eukaryotic development.

 

Gen 220:  Molecular Biology and Genetics in Modern Medicine
Scientific, clinical, and ethical aspects of modern human genetics and molecular biology as applied to medicine. Covers genetic approaches and molecular underpinnings of inherited diseases and somatic/genetic diseases are integrated with patient presentations, discussions.

 

Gen 228:  Genetics in Medicine:  From Bench to Bedside
Focus on translational medicine: the application of basic genetic discoveries to human disease. Will discuss specific genetic disorders and the approaches currently used to speed the transfer of knowledge from the laboratory to the clinic.

 

Micro 213:  Social Issues in Biology

Readings, discussion of social/ethical aspects of biology: history, philosophy of science; evolution vs. creationism; genetics and race; women and science; genetic testing; stem cell research; science journalism; genetics and the law; scientists and social responsibility.

 

Biophysics 205:  Computational and Functional Genomics
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 then lectures.


Additional BBS Core Courses


BCMP 200:  Molecular Biology

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.

 

CB 201:  Molecular Biology of the Cell

Molecular basis of cellular compartmentalization, protein trafficking, cytoskeleton dynamics, mitosis, cell locomotion, cell cycle regulation, signal transduction, cell-cell interaction, and cellular/biochemical basis of diseases. Methods covered: mass spectrometry, microscopy, and quantitative approaches to Cell Biology.

 

Micro 201: Molecular Biology of the Bacterial Cell

This course is devoted to bacterial structure, physiology, genetics, and regulatory mechanisms. The class consists of lectures and group discussions emphasizing methods, results, and interpretations of classic and contemporary literature.

 

 


 

 

 

 

 

 

 


© President and Fellows
of Harvard College