# Curriculum ## **CURRICULUM** --- PGG requirements include: - One course in statistical or quantitative biology taken as a BBS elective - Two courses in genetics or genomics taken as BBS electives Many different courses can satisfy these requirements, and the Harvard course catalog changes every year as offerings are added or removed. For this reason, students are not limited to the examples below. Any course that reasonably fits these categories may be used to fulfill the requirements, after discussion and approval with the Program Director, Scott Kennedy. This is always a topic of conversation during the enrollment interview when joining PGG. Below is a list of courses that our students have taken in the past. It is intended only as a sample, not a complete or restrictive list. ### **Some Electives in Genetics and Genomics** **🧬 GENETIC 216****: Advanced Topics in Gene Expression:** This course covers different topics in gene regulation, covering genetic, genomic, biochemical, and molecular approaches. A small number of topics are discussed in depth, using the primary literature. Topics range from prokaryotic transcription to eukaryotic development. **🧬 GENETIC 390QC: Bootcamp: Experimental Approaches in Genetics:** The goal of this course is to provide you with a hands-on survey of major topics and themes in genetic and genomic analysis, as well as exposure to numerous experimental techniques and model organisms. Over the course of nine days, you will spend each day in a new lab across Longwood, learning directly from faculty, postdocs, and senior graduate students. You will gain experience using genetic approaches to address biologically relevant questions in a variety of experimental systems, including bacteria, yeast, nematodes, zebrafish, mice, human cells, and cancer models. You will also learn about clinical cytogenetics, CRISPR screens, and protein structure prediction using AlphaFold. The course will combine short lectures, hands-on laboratory activities, and facility visits that emphasize experimental methods, hypothesis generation and testing, and data analysis. **🧬 GENETIC 305QC: CRISPR genome editing techniques and applications:** CRISPR genome editing has revolutionized the study of genetics and has shown promise to treat genetic disease at its roots. This course will provide an overview on how CRISPR-based genome editing tools work, how they are used to unravel the genetics of complex disease, and how they are being deployed to ameliorate genetic diseases. The course will combine lectures from experts on the development and use of CRISPR-based tools with seminars on the practical application of and ethical issues surrounding genome editing. **🧬 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. **🧬 GENETIC 228****: Genetics in Medicine - From Bench to Bedside:** Focus on translational medicine: the application of basic genetic discoveries to human disease. Each three-hour class will focus on a specific genetic disorder and the approaches currently used to speed the transfer of knowledge from the laboratory to the clinic. Each class will include a clinical discussion, a patient presentation if appropriate, followed by lectures, a detailed discussion of recent laboratory findings and a student-led journal club. Lecturers will highlight current molecular, technological, bioinformatics and statistical approaches that are being used to advance the study of human disease. There is no exam. Students will present one paper per session in a journal club style. **🧬 BIOPHYS 205****: Computational and Functional Genomics:** This is an upper-level critical paper reading and discussion course in the areas of experimental and computational functional genomics. Topics include genome sequencing, sequence analysis, transcriptomics, epigenomics, gene regulation, proteomics, chemical genomics, metabolomics, phenomics, and genetic variation analysis. Journal articles will comprise both classic, landmark papers in genomics and also more recent papers. Topics will be covered through paper presentations and in-class discussions. Students will be responsible for β€˜chalk talk’ style presentations of assigned articles and leading class discussions of those articles, as well as active participation in discussion of all assigned papers. There will be written and oral presentations of final student proposals at the end of the term. **🧬 MICROBI 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** **πŸ“Š 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. **πŸ“Š 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. **πŸ“Š MCB 112****: Biological Sequence Analysis:** Biology has become a computational science, requiring analysis of large data sets from genome sequencing and other technologies. This course teaches computational methods in biological sequence 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 and statistical scientists learning about biological sequence data. **πŸ“Š 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. **πŸ“Š STAT 139: Introduction to 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. Courses through the Biostatistics Department at the Harvard School of Public Health may also be applicable. ##### Additional Resources [Nanocourses chevron\_right](https://curriculumfellows.hms.harvard.edu/nanocourses) [FAS Course Catalog chevron\_right](https://registrar.fas.harvard.edu/courses-exams/fas-courses)