Pictographic representation of the challenges in computational biology. (Figure by MIT OCW. Courtesy of Prof. Manolis Kellis.)
This course features a complete set of homework and recitation assignments in the assignments
sections. In addition, a partial set of lecture notes is available in the lecture notes
This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:
Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.
Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.
Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution.
Special software is required to use some of the files in this course: .py.