MSCBIO 2040 - CELLULAR AND SYSTEMS MODELING
Minimum Credits: 3
Maximum Credits: 3
A graduate-level introduction into mathematical modeling and analysis of biological systems on the cellular and other levels. This condensed and broad course conveys the unity of the modeling methodology in biology. It spans a range of perspectives derived from the different disciplines from which this new area of research originated: biology, mathematics, engineering, and computer science. The systems covered include quantitative physiology, quantitative cell biology, biological networks, dynamic systems, cell mechanics, and systems modeling of critical illness. The quantitative physiology topics to be covered include hemodynamics, musculoskeletal systems, endocrinology, neuroendocrinology, gastrointestinal/renal, transport phenomena, and pathophysiological conditions. Quantitative cell biology topics surveyed are mathematical models of the cytoskeleton dynamics, intracellular transport, cell locomotion, spatially-distributed models of cell signaling, approaches to whole-cell modeling, and role of modeling in cell-biological research. Models of cellular mechanics will also be addressed. Mathematics of dynamic systems is presented in application to enzyme reactions, bistability in cellular signaling, programmed cell death, and the mechanisms behind the circadian and cell-division rhythms. Biological network theory is presented as it applies to metabolism, protein interactions, regulation of gene expression, and reverse engineering of the biological systems. Theoretical aspects of application of systems modeling to clinical research are also presented on an example of quantitative systems approach to inflammation, sepsis, and trauma. In addition, the course will survey computational methods and models that are broadly useful across the various system types examined. These will include random walk models, master equations, and continuous and discrete models of chemistry within the cell. Finally, the course will include a presentation of general discrete and continuous models broadly useful in cell and systems modeling as well as computational methods for optimization and parameter tuning on such models. Across the entire range of topics, the universality of the systems modeling methodology and its role in biomedical research are emphasized.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad LG/SNC Basis
Click here for class schedule information.
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