This course will cover the process of gene inheritance and descriptions of genome structure, as well as a discussion of gene content and function across lineages. Students will learn about genome-related technologies, including genome sequencing. They will also learn about how genomes vary across species, as well as the forces driving these evolutionary changes. Students will complete quizzes and exams to demonstrate understanding of the information present in genomes and the processes that drove it. Students will also learn about carrying out genome level data analysis and will use these skills on a final project to test a hypothesis using genomic data.
This course will cover the basic concepts of structural bioinformatics and molecular modeling. A broad qualitative overview of macromolecular structure and protein folding will be provided which includes sequence alignment, secondary structure calculation, and tertiary structure prediction. An introduction to programming languages, data mining and algorithms used in Bioinformatics will be covered to provide competence in handling large and complex biological data. The course also offers practical training on the application of computational modelling in aspects of drug discovery.
This course is designed to provide a basic introduction to experimental and computational methods used in protein structure determination and molecular modeling. The course emphasis will be on the use of computational methods to understand protein folding, dynamics and structure based drug design. The course will provide practical training in the application of modeling techniques in drug discovery.
This is a course on the application of genome-related concepts to genome sequence data. Students will gain familiarity with both existing software and with basic programming (scripting) skills for problems in genomics. Further, students will come to understand the connections between standard computational and statistical approaches and their underpinnings in those fields increasingly dominated by genomic approaches, These include the fields of molecular evolution, population genetics, molecular genetics, molecular biology, and biochemistry. The course will be a hands-on computational lab course, with students working on problems and assignments in class using their laptop computers. Shell scripting and the programming language Python will be used for most of the course.
This course will examine the social, legal, and privacy issues of applying computational approaches to large datasets including those from personal genome projects. The class will expose students to variation-based approaches in genomics, policies and strategies to share genomic data, database management and security, open-access and open-source philosophies, the ethics of collecting, storing, and disseminating human data, and HIPAA, FDA, and IRB regulatory policies for health care professionals and bioinformaticians. Students will be given the opportunity to discuss contemporary case studies, in addition to NIH-sanctioned online training modules (Responsible Conduct in Research).
The PSM program prepares graduates for careers in biotechnology-related fields with a strong emphasis on skill areas that include management, policy and regulation in addition to scientific discovery. This course will provide students with career exposure through interviews with professionals in government and industry and will assist students in developing a career plan. Students will develop a white paper on the current state of Biotechnology based on new advances and challenges in the past year. Members of the advisory board will participate in facilitating the course.
Biostatistics is an important part of the research activities related to biological and medical issues. Statistics is used to analyze phenomena with random properties and is often essential to draw the right conclusions based on a data set. The course will be designed to cover different statistical methods for data analysis mainly applied to medical and biological problems. Advanced undergraduate and graduate students with interests in medicine and biomedical research will benefit most from the course. However statistical methods that can be applied to behavioral science and ecology will also be covered.
This course provides an introduction to the field of computational biology by implementing biological models in the Python programming language. In addition to coverage of the basics of the Python language, topics will include : phylogenetic tree models, implementation of Markov models for biological problems, data structures and algorithms for the analysis of biological sequences, and the use of popular Python modules relevant for biological modeling. Prior basic knowledge of evolutionary theory and of genetics/genomics is expected. Some prior scripting experience is helpful, but students are not required to have an extensive coding background. This is a hands-on computational lab course, with students working on problems in class using their laptop computers.
This course provides an overview of how researchers use data to solve global problems such as climate change, mass extinction, pandemics, and poverty. Students explore interdisciplinary data, from economics to public health and learn a marketable skill: communicating data with R, an intuitive statistical computer language. The course is project based.
Bioinformatics and biomedical data science have emerged as analytics frontiers in reaping the benefits of machine learning toward advanced modeling of massive biological and medical data. This course introduces classical and state-of-the-art methodology development in machine learning applied to bioinformatics and biomedical data science. Topics include classical statistical and machine learning algorithms that are particularly useful in bioinformatics and biomedical analytics. By the end of the class, students will have gained the knowledge of diverse machine learning methods and evaluate relevant scientific publications, as well as the capability of developing open source programs to analyze and interpret biological and biomedical data.