7.36 Foundations of Computational and Systems Biology
Provides an introduction to computational and systems biology. Includes units on the analysis of protein and nucleic acid sequences, protein structures, and biological networks. Presents principles and methods used for sequence alignment, motif finding, expression array analysis, structural modeling, structure design and prediction, and network analysis and modeling. Techniques include dynamic programming, Markov and hidden Markov models, Bayesian networks, clustering methods, and energy minimization approaches. Exposes students to emerging research areas. Designed for students with strong backgrounds in either molecular biology or computer science. Some foundational material covering basic programming skills, probability and statistics is provided for students with less quantitative backgrounds. Students taking graduate version complete additional assignments.
This class counts for a total of 12 credits.
You can find more information at the MIT Department of Biology: 7.91 - Foundations of Computational andSystems Biology site or on the 7.36 Stellar site.
© Copyright 2015 Yasyf Mohamedali