Archived Versions

6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution

As taught in: Fall 2008

Image summarizing challenges in computational biology.

Pictographic representation of the challenges in computational biology. (Image by Prof. Manolis Kellis.)

Level:

Undergraduate / Graduate

Instructors:

Prof. Manolis Kellis

Prof. James Galagan

Course Features

Course Description

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

Technical Requirements

Special software is required to use some of the files in this course: .fa, .py, and .zip.