Computational Biology: Genomes, Networks, Evolution

Image summarizing challenges in computational biology.

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


MIT Course Number

6.047 / 6.878

As Taught In

Fall 2008



Cite This Course

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

Manolis Kellis, and James Galagan. 6.047 Computational Biology: Genomes, Networks, Evolution, Fall 2008. (Massachusetts Institute of Technology: MIT OpenCourseWare), (Accessed). License: Creative Commons BY-NC-SA

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