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Massachusetts Institute of Technology

Foundations of Computational and Systems Biology (Spring 2014)

Massachusetts Institute of Technology via MIT OpenCourseWare


Course Features

  • Video lectures
  • Captions/transcript
  • Lecture notes
  • Projects (no examples)
  • Assignments: presentations (no examples)
  • Assignments: programming with examples
  • Assignments: written (no examples)

Course Highlights

The MIT Initiative in Computational and Systems Biology (CSBi) is a campus-wide research and education program that links biology, engineering, and computer science in a multidisciplinary approach to the systematic analysis and modeling of complex biological phenomena. This course is one of a series of core subjects offered through the CSB Ph.D program, for students with an interest in interdisciplinary training and research in the area of computational and systems biology.

Course Description

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.


1. Introduction to Computational and Systems Biology.
2. Local Alignment (BLAST) and Statistics.
3. Global Alignment of Protein Sequences (NW, SW, PAM, BLOSUM).
4. Comparative Genomic Analysis of Gene Regulation.
5. Library Complexity and Short Read Alignment (Mapping).
6. Genome Assembly.
7. ChIP-seq Analysis; DNA-protein Interactions.
8. RNA-sequence Analysis: Expression, Isoforms.
9. Modeling and Discovery of Sequence Motifs.
10. Markov and Hidden Markov Models of Genomic and Protein Features.
11. RNA Secondary Structure; Biological Functions and Predictions.
12. Introduction to Protein Structure; Structure Comparison and Classification.
13. Predicting Protein Structure.
14. Predicting Protein Interactions.
15. Gene Regulatory Networks.
16. Protein Interaction Networks.
17. Logic Modeling of Cell Signaling Networks.
18. Analysis of Chromatin Structure.
19. Discovering Quantitative Trait Loci (QTLs).
20. Human Genetics, SNPs, and Genome Wide Associate Studies.
21. Synthetic Biology: From Parts to Modules to Therapeutic Systems.
22. Causality, Natural Computing, and Engineering Genomes.

Taught by

Prof. Christopher Burge , Prof. David Gifford and Prof. Ernest Fraenkel

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