• This course is specifically designed for first-year Master’s students in the Biomedical Sciences. The course covers basic concepts in the fields of Biochemistry and Cell Biology. Topics include the structure, function and metabolism of nucleic acids, proteins, carbohydrates and lipids, as well as related regulatory mechanisms including transcription factors and upstream signaling pathways initiated at the cell membrane. Relevance to human disease is emphasized throughout the course. The course is designed with ample iterations and review sessions to ensure the best learning outcomes for all students, including international students, for whom this may be the first educational experience in the U.S.

  • This course seeks to address the interface between basic science and medicine. The course will explore the experimental logic used to unravel the mechanism of disease development, identify and validate therapeutic targets and design therapeutic approaches. The modeling approaches that will be studied utilize cell culture and model organisms including mice, C. elegans and zebra fish. Therapeutic interventions will include small molecules, RNA, peptides, stem cells and personalized medicine. Upon successful completion of this course, students should be able to build hypotheses, design experimental strategies that will facilitate greater understanding of the mechanisms underlying the disease process and develop rational therapeutic approaches. The course will enable improved understanding of current literature pertaining to disease-based research.

  • This course will instruct students in a wide range of both traditional and state-of-the-art methods used in molecular biology, biochemistry and cell biology knowledge of which is essential for experimental design and comprehending research literature and seminar presentations. Prior to each lecture, students will have a short, low-stakes assignment to introduce the topic and prepare them for the live session. Lecture time will include instruction as well as completion of exercises using hypothetical or real experimental data generated using the methods that will involve data analysis and interpretation or experimental design. The course will include instruction on detailed analysis of data in research articles that will provide critical thinking skills. Students will have multiple ways to demonstrate the knowledge and skills gained in the course. In addition to traditional exams, the grade will be based on group exercises, quizzes and a weeks-long instructor-supported group project to enhance critical thinking skills

  • Do you know the intricacies of the process by which a set of data are refined, organized, and published? Is the process of data collection, organization, or analysis the same for all types of data? Are some journals more finicky about publishing than others? How long does the process of data collection to publication take? This course will explore some of the ‘mysteries’ in this scientific writing and publication process. You will gain an appreciation for the entire process involved in publishing a research article.

  • Bioinformatics skills have become an inherent component of life-science research and yet the majority of life science researchers lack basic skills in data analysis and interpretation, and especially in data management, even though such skills are essential to many research projects today. This course will provide students from non-quantitative backgrounds with the skill sets for applying data science and bioinformatics tools in the study of human health and disease using R and Bioconductor. This course is intended for students who are not experts in either data science or bioinformatics. Teaching approaches will alternate between lecture and in-class analysis workshops that will focus on to the selection and statistical analysis of large publicly available data sets. Topics will include basic statistics, hypothesis testing, both parametric and non-parametric analyses (e.g., such as hierarchal clustering and principal component analysis), linear regression analysis, data normalization, reproducibility/sensitivity analysis, multiple test correction, and power assessment Finally, the course will provide an introductory exposure to command-line and Unix-based large-scale data processing, complementing the use of R and Bioconductor as tools for conducting and reproducing analysis frequently required in scientific journals.