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?
About this Course
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Learner Career Outcomes
Icahn School of Medicine at Mount 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.
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TOP REVIEWS FROM INTRODUCTION TO SYSTEMS BIOLOGY
Very useful course in order to learn the basics in systems biology. You only need some training in biology to understand the concepts but the course is easy to follow and very complete.
Great foundational material and insights from Prof. Iyengar that supports the 'disruptive' trends underway at the intersection of Computational Biology and Medicine.
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.
This course has been a very instructive course for me. I would like to thank ISM and Dr. Iyengar for his sacrifice for sharing his knowledge!!!
About the Systems Biology and Biotechnology Specialization
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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