6.085 Computational Biology: Genomes, Networks, Evolution (U)

L TR11-12:30, Room 3-370
Pofessor Manolis Kellis, manoli@mit.edu
Prereq.: 6.001; 7.012; 18.440 or 6.041
3-0-9

This subject qualifies as a Theoretical Computer Science Engineering Concentration subject.

Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study principles of algorithm design, influential problems and techniques, and analyze 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.