Manolis Kellis - Courses Taught

I teach the following courses on Computational Biology and Algorithms at MIT.


6.047/6.878 - Computational Biology: Genomes, Networks, Evolution

previously taught with Piotr Indyk (F05, F06), James Galagan (F07, F08, F09, F10)

Covers 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.

  • 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.

    Previous offerings:

  • Fall 2012 (as 6.047/6.878/HST.507): Lecture Notes
  • Fall 2011 (as 6.047/6.878/HST.507): Lecture Notes
  • Fall 2010 (as 6.047/6.878/HST.507): Lecture Notes
  • Fall 2009 (as 6.047/6.878/HST.507): Lecture Notes - Student Evaluations: Ugrad/Grad
  • Fall 2008 (as 6.047/6.878): Lecture Notes - Student Evaluations: Ugrad/Grad
  • Fall 2007 (as 6.047/6.878): Lecture Notes - Student Evaluations: Ugrad/Grad
  • Fall 2006 (as 6.085/6.895): Lecture Notes - Student Evaluations: Ugrad - Grad
  • Fall 2005 (as 6.095/6.895): Lecture Notes - Student Evaluations: Ugrad/Grad


    • 6.046: Introduction to Algorithms

    with Ron Rivest (S06), Srini Devadas (S07), Piotr Indyk (S08), Marten vanDijk (S09)

    Introduction to design and analysis of algorithms. Topics include sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; greedy algorithms; amortized analysis; graph algorithms; shortest paths; network flow. Advanced topics include: number theory, computational biology, string matching, database search, hidden Markov models; number-theoretic algorithms; polynomial and matrix calculations.

    Previous Offerings:

  • Spring 2009 (with Marten van Dijk) (Student Evaluations)
  • Spring 2008 (with Piotr Indyk) (Student Evaluations)
  • Spring 2007 (with Srini Devadas) (Student Evaluations)
  • Spring 2006 (with Ron Rivest) (Student Evaluations)

    • 6.092: Bioinformatics and Proteomics: An Engineering-Based Problem Solving Approach

    Gil Alterovitz, Manolis Kellis, Marco Ramoni

    This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon. New research areas will be explored using current literature as well as text from book chapter materials being written by the instructors. Guest lectures include speakers from both industry and academia.

    (IAP 2005)

    6.096 - Algorithms for Computational Biology (meets with 6.046)

    Manolis Kellis

    This course covers the algorithmic foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, analyze influential algorithms, and apply these to real datasets. Topics include: biological sequence analysis, gene finding, motif discovery, RNA folding, global and local sequence alignment, genome assembly, comparative genomics, genome duplication, genome rearrangements, evolutionary theory, gene expression, clustering algorithms, scale-free networks, machine learning applications to genomics.

    All lectures available online

    (Spring 2005)

    18.417 - Introduction to Computational Molecular Biology

    In 2001, as a TA for Bonnie Berger's 18.417, i gave several guest lectures, availablle online below

    Other Computational Biology Courses offered at MIT

    Course Title Professor
    6.047/6.878 Computational Biology: Genomes, Networks, Evolution M. Kellis (with P. Indyk and J. Galagan)
    6.891 Computational Evolutionary Biology R. Berwick
    6.892/7.90 Computational Functional Genomics D. Gifford, T. Jaakkola, R. Young
    7.81J/8.591 Systems Biology A. vanOudenaarden
    7.91 Foundations of Computational and Systems Biology M. Yaffe, C. Burge, A. Keating
    10.555 BioInformatics I. Rigoutsos, G. Stephanopoulos
    18.417 Introduction to Computational Molecular Biology J. Waldispuhl, B. Berger
    HST.508 Quantitative Genomics and Evolution L. Mirny
    6.096 Algorithms for Computational Biology M. Kellis
    Manolis Kellis