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HST 507
Advanced Computational Biology: Genomes, Networks, Evolution

Term: Fall

Course Director(s): Manolis Kellis
Time:
Lecture: T/R 1-2:30pm; Recitation: F 3-4pm
Location:
Lecture: MIT: 32-141; Recitation: MIT: 4-237
Course Website:
None
Prerequisite:
6.006, 6.041, Biology (GIR); or permission of instructor
Restrictions:
None
MIT Units:
4-0-8 (G-Level Credit)
Harvard Units:
N/A
Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. Principles of algorithm design, influential problems and techniques, and analysis of large-scale biological datasets. Topics include (a) genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search; (b) networks: gene expression analysis, regulatory motifs, biological network analysis; (c) evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks. Graduate Section (HST507): Additionally examines recent publications in the areas covered, with research-style assignments. A more substantial final project is expected, which can lead to a thesis and publication.