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.
Foundations of Computational and Systems Biology
Massachusetts Institute of Technology via MIT OpenCourseWare
Overview
Syllabus
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
