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6.895 / 6.095J Computational Biology: Genomes, Networks, Evolution, Fall 2005

Challenges in Computational Biology.

Pictographic representation of the challenges in computational biology. (Figure by MIT OCW. Courtesy of Prof. Manolis Kellis.)

Highlights of this Course

This course features a complete set of homework and recitation assignments in the assignments and recitations sections. In addition, a partial set of lecture notes is available in the lecture notes section.

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: .py.



Prof. Piotr Indyk
Prof. Manolis Kellis

Course Meeting Times

Two sessions / week
1.5 hours / session

One session / week
1 hour / session


Undergraduate / Graduate


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