Cellular and Systems Modeling undertakes the ambitious task of studying the dynamics of biological and biomedical processes from a whole system point of view. The observed systems range over orders of magnitude, from tissue to cells to molecular assemblies! Engineering tools are used along with genome-scale information in mathematical and/or computational models that usually adopt a top-down approach. Modeling diseases, entire ‘virtual’ cells, or subcellular networks of interactions are among typical tasks. Major research topics include the modeling of complex signaling and regulatory networks, transport mechanisms, spatio-temporal evolution of microphysiological events, as well as establishing the links between the development of complex phenotypes and the seemingly unrelated molecular events.
Computational Methods for Biological Modeling and Simulation
Machine Learning from Neural Cortical Circuits
Computational Models of Neural Systems
Statistical Methods for Neuroscience
This course discusses how mechanical quantities and processes such as force, motion, and deformation influence cell behavior and function, with a focus on the connection between mechanics and biochemistry. Specific topics include: (1) the role of stresses in the cytoskeleton dynamics as related to cell growth, spreading, motility, and adhesion; (2) the generation of force and motion by moot molecules; (3) stretch-activated ion channels; (4) protein and DNA deformation; (5) mechanochemical coupling in signal transduction; (6) protein trafficking and secretion; and (7) the effects of mechanical forces on gene expression. Emphasis is placed on the biomechanics issues at the cellular and molecular levels; their clinical and engineering implications are elucidated.
Computational Models in Neuroscience
Course covers computational and mathematical neuroscience. Class will do modeling and analysis of complex dynamics of single neurons and large-scale networks.
Computational Neuroscience Methods
This course offers an introduction to modeling methods in neuroscience. Topics range from modeling the firing patterns of single neurons to using computational methods to understand neural coding. Some systems level modeling is also done.
Computational Cell Biology
Systems Approach to Inflammation
Drug discovery is an interdisciplinary science that seeks to identify small molecular and/or biologic entities for therapeutic intervention and to understand integrated biological systems and processes at the functional and molecular levels. This course will discuss various topics that are relevant to current approaches and principles in drug discovery including drug origins, drug target identification, and validation, biochemical and cell-based screening approaches, proteomic approaches to drug discovery, computational chemistry and biology, and quantitative systems pharmacology, as well as other topics in preclinical and clinical drug development, personalized medicine, Chinese herbal medicines and intellectual property. The course will include case studies intended to aid students in a full understanding of the drug discovery process.
Proteomics and metabolomics are the large scale study of proteins and metabolites, respectively. In contrast to genomes, proteomes and metabolomes vary with time and the specific stress or conditions an organism is under. Applications of proteomics and metabolomics include determination of protein and metabolite functions (including in immunology and neurobiology) and discovery of biomarkers for disease. These applications require advanced computational methods to analyze experimental measurements, create models from them, and integrate with information from diverse sources. This course specifically covers computational mass spectrometry, structural proteomics, proteogenomics, metabolomics, genome mining and metagenomics.