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?
À propos de ce cours
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.
- 5 stars57,22 %
- 4 stars25,24 %
- 3 stars8,67 %
- 2 stars5,20 %
- 1 star3,66 %
Meilleurs avis pour INTRODUCTION À LA BIOLOGIE DES SYSTÈMES
Great foundational material and insights from Prof. Iyengar that supports the 'disruptive' trends underway at the intersection of Computational Biology and Medicine.
Ravi is a great researcher. This class is taught with great clarity and expertise. Subtitle is much less impressive though.
fascinating, difficult, and interesting course. Thanks so much!
I enjoyed the topics that were covered in the course. I removed one star because the quizes required deeper knowledge than what was covered during the lectures.
À propos du Spécialisation Biologie des systèmes et biotechnologie
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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