This graduate course will cover the processes of gene inheritance and gene expression as they are manifested across the entire genome. Students will learn about genome-related technologies, including genome sequencing and mapping. They will also learn about genome structure and how genomes vary across species, as well as the forces driving these evolutionary changes. A significant part of the course will cover genome-level data analyses, and students will complete a major project in genome analysis, in addition to several smaller problem-based assignments
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.
Computing has become an indispensable tool for scientific research and for businesses. The ability to integrate numerical computation with experimental results becomes a necessary condition for a productive researcher and business operators. The proposed course aims to bridge the gaps between current CS (computer science) graduate programs and the emerging scientific computing needs. In particular, this course focuses on three scripting languages and multiple operating environments for scientific computing and for businesses. Driven by increasingly sophisticated data needs, scripting languages have emerged from obscurity to prominence for their versatility and ease of use. For practical scientific computing projects, scripting has become critical for integrating computational results with multiple scientific research formulations and for delivering market insights for businesses.
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.