Curriculum

Advanced Electives


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.

 

HT 160:  Genetics in Modern Medicine
This course will provide a firm foundation for understanding the relationship between molecular biology, developmental biology, genetics, genomics, bioinformatics, and medicine. The goal is to develop explicit connections between basic research, medical understanding, and the perspective of patients. During the course the principles of human genetics will be reviewed. Students will become familiar with the translation of clinical understanding into analysis at the level of the gene, chromosome and molecule, the concepts and techniques of molecular biology and genomics, and the strategies and methods of genetic analysis, including an introduction to bioinformatics. The course will extend beyond basic principles to current research activity in human genetics.

 

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 than lectures.

 

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.


Statistics/Quantitative Biology Courses


Biophysics 170:  Evolutionary and Quantitative Genomics
Aims to develop deep quantitative understanding of basic forces of evolution, molecular evolution, genetic variations and their dynamics in populations, genetics of complex phenotypes, and genome-wide association studies. Application of these foundational concepts to cutting edge studies in epigenetics, gene regulation and chromatin; cancer genomics, and microbiomes.  Modules consist of lectures, journal club discussions of high impact publications, and guest lectures that provide clinical correlates. Homework assignments and final projects aim to develop hands-on experience and understanding of genomic data from evolutionary principles.

 

BMI713: Computing Skills for Biomedical Sciences
This course will prepare students for advanced graduate level classes that require practical programming and data analysis skills through active learning methods. The main focus of this course is to familiarize students with the R programming language. Additionally, students will learn about the command line on Linux-based systems, high-performance computing environments, and fundamental data analysis approaches.  The skills taught in this course will enable students to design and implement programs for reproducible data analysis, manage file-based datasets, apply basic statistical, algorithmic, and visual approaches for data interpretation, and execute analyses on a compute cluster. 

 

BMI715: Computational Statistics for Biomedical Sciences
This course will provide a practical introduction to statistical analysis of biological and biomedical data. Basic techniques will be covered, including descriptive statistics, elements of probability, hypothesis testing, nonparametric methods, correlation analysis, and linear regression. Emphasis will be on how to choose appropriate statistical tests, how to assess statistical significance, and how to avoid common mistakes in analysis of large datasets. This course is geared toward graduate students in the biological sciences, but others are welcome if space permits. No previous knowledge in statistics is required, but some proficiency in R will be assumed. 

 

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.

 

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.

 

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: Linear Models
An in-depth introduction to statistical methods with linear models and related methods. Topics include group comparisons (t-based methods, non-parametric methods, bootstrapping, analysis of variance), linear regression models and their extensions (ordinary least squares, ridge, LASSO, weighted least squares, multi-level models), model checking and refinement, model selection, cross-validation. The probabilistic basis of all methods will be emphasized.

 

BMIF 201: Concepts in Genome Analysis
This course focuses on quantitative aspects of genetics and genomics, including computational and statistical methods of genomic analysis. We will introduce basic concepts and discuss recent progress in population and evolutionary genetics and cover principles of statistical genetics of Mendelian and complex traits. We will then introduce current genomic technologies and key algorithms in computational biology and bioinformatics. We will discuss applications of these algorithms to genome annotation and analysis of epigenomics, cancer genomics and metagenomics data. Proficiency in programming and basic knowledge of genetics and statistics will be assumed.

 

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



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