Archived Versions

Lecture Notes

This section contains scribe notes and slides from selected lectures. Students were required to write up scribe notes; see the syllabus for more details.

All scribe notes are courtesy of 6.047 students and are used with permission.

SES # TOPICS SCRIBE NOTES SLIDES
L1 Introduction: biology, algorithms, machine learning (PDF) (PDF - 1.7MB)
L2 Global / local alignment, dynamic programming (PDF) (PDF)
L3 String search, BLAST, database search (PDF) (PDF - 1.4MB)
L4 Clustering basics, gene expression, sequence clustering   (PDF)
L5 Classification, feature selection, SVM (PDF) (PDF)
L6 HMMs 1: evaluation, parsing (PDF) (PDF)
L7 HMMs 2: posterior decoding, learning (PDF) (PDF)
L8 Generalized HMMs and gene prediction (PDF) (PDF - 1.5MB)
L9 Regulatory motifs, Gibbs sampling, EM   (PDF)
L10 Gene evolution: phylogenetic algorithms, NJ, ML, parsimony (PDF) (PDF)
L11

Molecular evolution, coalescence, selection, Ka/Ks

Guest lecturer: Daniel Neafsey, Broad Institute
 

(PDF) (PDF) (Courtesy of Daniel Neufsey. Used with permission.)
L12

Population genomics: fundamentals

Guest lecturer: Pardis Sabeti, Harvard Systems Biology
 

(PDF)  
L13

Population genomics: association studies

Guest lecturer: Pardis Sabeti, Harvard Systems Biology
 

(PDF)  
L14 Midterm    
L15 Genome assembly, Euler graphs (PDF)  
L16 Comparative genomics 1: biological signal discovery, evolutionary signatures (PDF)  
L17 Comparative genomics 2: phylogenetics, gene and genome duplication    
L18 Conditional random fields, gene finding, feature finding (PDF) (PDF)
L19 Regulatory networks, Bayesian networks   (PDF)
L20 Inferring biological networks, graph isomorphism, network motifs (PDF)  
L21 Metabolic modeling 1: dynamic systems modeling (PDF) (PDF - 1.1MB)
L22 Metabolic modeling 2: flux balance analysis and metabolic control analysis (PDF)  
L23

Systems biology

Guest lecturer: Uri Alon, Weizmann Institute of Science
 

   
L24

Module networks

Guest lecturer: Aviv Regev, Broad Institute
 

(PDF)  
L25

Synthetic biology

Guest lecturer: Tom Knight, MIT Computer Science and Artificial Intelligence Laboratory
 

(PDF)  
L26 Final presentations