Elective Course Offerings

 

In addition to the advanced electives in the home department and the BIG core courses, students may take two of the following courses:

 

Genomic Medicine (HMS) (Coming in Spring 2013)

 

Courses offered at the Faculty of Arts and Sciences (Please See Harvard University Course Catalogue for more information):

 

Statistics: 171 Introduction to Stochastic Processes

 

Description: An introductory course in stochastic processes. Topics include Markov chains, branching processes, Poisson processes, birth and death processes, Brownian motion, martingales, introduction to stochastic integrals, and their applications.

 

Statistics: 220 Bayesian Data Analysis

 

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.

 

Statistics: 285r Statistical Machine Learning

(Not currently offered)

 

Applied Mathematics 147: Nonlinear Dynamical Systems

 

Description: An introduction to nonlinear dynamical phenomena, covering the behavior of systems described by ordinary differential equations. Topics include: stability; bifurcations; chaos; routes to chaos and universality; approximations by maps; strange attractors; fractals. Techniques for analyzing nonlinear systems are introduced with applications to physical, chemical, and biological systems such as forced oscillators, chaotic reactions, and population dynamics.

 

Applied Mathematics 215: Fundamentals of Biological Signal Processing

 

Description: The course will introduce Bayesian analysis, maximum entropy principles, hidden markov models and pattern theory. These concepts will be used to understand information processing in biology. The relevant biological background will be covered in depth.

 

MCB 198: Advanced Mathematical Techniques for Modern Biology 

(Not Currently Offered)

 

SCRB 150: Human Genetics: Mining Our Genomes for an Understanding of Human Variation and Disease

 

Description: The sequencing of the human genome has revealed the full extent of genetic variation that exists within us as a species. This genetic diversity underlies much of our physical variation as well as our differences in responsiveness to disease stimuli and their treatments. We will explore these and other ramifications of human genetic diversity by applying classical and contemporary genetic tools to the identification of specific genes and pathways that functionally underlie our variable biology.

 

Systems Biology 200: Dynamic and Stochastic Processes in Cells

 

Description: Rigorous introduction to (i) dynamical systems theory as a tool to understand molecular and cellular biology (ii) stochastic processes in single cells, using tools from statistical physics and information theory.

 

Systems Biology 201: Principles of Animal Development from a Systems Perspective

 

Description: Intensive and critical analysis of systems approaches to circuits and principles controlling pattern formation and morphogenesis in animals. Students develop their own ideas and present them through mentored "chalk talks" and other interactive activities.

 

Courses offered at MIT:

6.047/6.878: Computational Biology: Genomes, Networks, Evolution

 

Description: Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. Principles of algorithm design, influential problems and techniques, and analysis of large-scale biological datasets. Topics include (a) genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search; (b) networks: gene expression analysis, regulatory motifs, biological network analysis; (c) evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks.

 

6.874J/7.90J: Computational Functional Genomics

 

Description: Presents computational approaches and algorithms for contemporary problems in systems biology, with a focus on models of biological systems, including regulatory network discovery and validation. Topics include genotypes, regulatory factor binding and motif discovery, and whole genome RNA expression; regulatory networks (discovery, validation, data integration, protein-protein interactions, signaling, whole genome chromatin immunoprecipitation analysis); and experimental design (model validation, interpretation of interventions). Discusses computational methods, including directed and undirected graphical models, such as Bayesian networks, factor graphs, Dirichlet processes, and topic models. Multidisciplinary team-oriented final research project. Students taking graduate version complete additional assignments.

 

7.81J/8.591J/9.531J: Systems Biology

 

Description: Introduction to cellular and population-level systems biology with an emphasis on synthetic biology, modeling of genetic networks, cell-cell interactions, and evolutionary dynamics. Cellular systems include genetic switches and oscillators, network motifs, genetic network evolution, and cellular decision-making. Population-level systems include models of pattern formation, cell-cell communication, and evolutionary systems biology. Students taking graduate version explore the subject in more depth.

 

7.91J/20.490J: Foundations of Computational and Systems Biology

 

Description: Provides an introduction to computational and systems biology. Includes units on the analysis of protein and nucleic acid sequences, protein structures, and biological networks. Presents principles and methods used for sequence alignment, motif finding, expression array analysis, structural modeling, structure design and prediction, and network analysis and modeling. Techniques include dynamic programming, Markov and hidden Markov models, Bayesian networks, clustering methods, and energy minimization approaches. Exposes students to emerging research areas. Designed for students with strong backgrounds in either molecular biology or computer science. Some foundational material covering basic programming skills, probability and statistics is provided for students with less quantitative backgrounds. Students taking graduate version complete additional assignments.

 

Courses offered at Harvard School of Public Health

(Please See HSPH Course Catalogue for more information):

 

Biostatistics 257: Computational and Statistical Methods in Human Genetics

 

Description: This course concentrates on the statistical aspects of genetic studies for complex-disease, covering both modern linkage and association analysis. The goal is to enable students to read fundamental papers and to engage in original research.

 

Biostatistics 227: Fundamental Concepts in Gene Mapping

 

Description: This course introduces students to the diverse statistical methods used throughout the process of statistical genetics, from familial aggregation and segregation studies to linkage scans candidate-gene association studies. Topics covered include basic principles from population genetics, multipoint and model-free linkage analysis, family-based and population-based association testing, and Genome Wide Association analysis. Instructors use ongoing research into the genetics of respiratory disease, psychiatric disorders and cancer to illustrate basic principles. Weekly homeworks supplement reading, course lectures, discussion and section. Relevant concepts in genetics and molecular genetics will be reviewed in lectures and labs. The emphasis of the course is fundamental principles and concepts.

 

Biostatistics 512: Introductory Computational Biology & Bioinformatics

 

Description: Basic problems, technology platforms, algorithms and data analysis approaches in computational biology. Algorithms covered include dynamic programming, hidden Markov model, Gibbs sampler, clustering and classification methods.