This course will introduce the student to contemporary Systems Biology focused on mammalian cells, their constituents and their functions. Biology is moving from molecular to modular. As our knowledge of our genome and gene expression deepens and we develop lists of molecules (proteins, lipids, ions) involved in cellular processes, we need to understand how these molecules interact with each other to form modules that act as discrete functional systems. These systems underlie core subcellular processes such as signal transduction, transcription, motility and electrical excitability. In turn these processes come together to exhibit cellular behaviors such as secretion, proliferation and action potentials. What are the properties of such subcellular and cellular systems? What are the mechanisms by which emergent behaviors of systems arise? What types of experiments inform systems-level thinking? Why do we need computation and simulations to understand these systems?
Informações sobre o curso
Escola de Medicina Icahn do Hospital Monte Sinai
The Icahn School of Medicine at Mount Sinai, in New York City is a leader in medical and scientific training and education, biomedical research and patient care.
- 5 stars56,84%
- 4 stars25,28%
- 3 stars8,74%
- 2 stars5,13%
- 1 star3,99%
Principais avaliações do INTRODUÇÃO À BIOLOGIA SISTÊMICA
Excellent course, could be improved by explaining the more difficult concepts in greater depth, e.g. ODE and PDE math.
Ravi is a great researcher. This class is taught with great clarity and expertise. Subtitle is much less impressive though.
It's a challenging subject. Don't expect to walk away from the course with a complete grasp of systems biology. However, this course is a fantastic place to start.
Very nice course, would have liked more interactivity / problem solving type questions
Sobre Programa de cursos integrados Biologia de sistemas e biotecnologia
Design systems-level experiments using appropriate cutting edge techniques, collect big data, and analyze and interpret small and big data sets quantitatively.
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