Systems Biomedicine: Molecules, Cells and Networks
This is a core course for entering PhD, MD/PhD, and Master's students that introduces students to integrated approaches to understanding physiological functions and the underlying biochemical, cell biological and molecular biological mechanisms. Insight from global measurements such as whole genome sequencing, mRNA profiling and proteomics as well as physiological and clinical measurements are integrated with computational models to provide a multiscale understanding of disease initiation and progression and therapeutic action.
[Fall 2013 Poster] [Lecture Materials]
Principles of Pharmacology
Graduate and medical courses are integrated to introduce the students to important areas of pharmacology including pharmacokinetics, pharmacodynamics and drug metabolism. Receptors, enzymes and channels are considered as drug targets. Structural aspects of drug design, computational methods for drug-target docking, current issues in drug discovery and development and gene therapy strategies are all discussed. Students attend medical pharmacology lectures on drug treatment within the cancer pathophysiology course to get a clinical perspective. The students are introduced to emerging concepts in systems pharmacology and how these may be used for drug discovery and polypharmacology and prediction of complex adverse events based on genomic and epigenomic status.
Cell Signaling Systems
This course uses the primary literature to develop a systems level understanding of the mechanisms underlying both information flow and information processing through cell signaling pathways and networks. Effects of signaling on tissue and organ functions in normal and disease states are considered. Current experimental and theoretical concepts in cellular regulatory and drug action, therapeutic and adverse, are highlighted.
Systems Biology: Biomedical Modeling
This course takes a case-based approach to teach contemporary mathematical modeling techniques to graduate students. Lectures provide biological background and describe the development of both classical mathematical models and more recent representations of biological processes. Students are taught how to analyze the models and use computation to generate predictions that may be experimentally tested. The course has four sections to cover different modeling approaches that are currently being used in biomedical research. These approaches can be classified as: graph theory and network analysis; statistical models, including principal components and regression; ordinary differential equation and partial differential equation-based models; and stochastic models.
[Teaching Resources Published in Science Signaling]