7.91J Fundamentals of Computational and Systems Biology

7.91J Fundamentals of Computational and Systems Biology (Spring 2014, MIT OCW). Instructors: Prof. Christopher Burge, Prof. David Gifford, and Prof. Ernest Fraenkel. 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.

The MIT Initiative in Computational and Systems Biology 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. (from

Lecture 10 - Markov and Hidden Markov Models of Genomic and Protein Features

Prof. Christopher Burge begins by reviewing Lecture 9, then begins his lecture on hidden Markov models (HMM) of genomic and protein features. He addresses the terminology and applications of HMMs, the Viterbi algorithm, and then gives a few examples.

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Lecture 01 - Introduction to Computational and Systems Biology
Lecture 02 - Local Alignment (BLAST) and Statistics
Lecture 03 - Global Alignment of Protein Sequences (NW, SW, PAM, BLOSUM)
Lecture 04 - Comparative Genomic Analysis of Gene Regulation
Lecture 05 - Library Complexity and Short Read Alignment (Mapping)
Lecture 06 - Genome Assembly
Lecture 07 - ChIP-seq Analysis; DNA-protein Interactions
Lecture 08 - RNA-sequence Analysis: Expression, Isoforms
Lecture 09 - Modeling and Discovery of Sequence Motifs
Lecture 10 - Markov and Hidden Markov Models of Genomic and Protein Features
Lecture 11 - RNA Secondary Structure - Biological Functions and Prediction
Lecture 12 - Introduction to Protein Structure; Structure Comparison and Classification
Lecture 13 - Predicting Protein Structure
Lecture 14 - Predicting Protein Interactions
Lecture 15 - Gene Regulatory Networks
Lecture 16 - Protein Interaction Networks
Lecture 17 - Logic Modeling of Cell Signaling Networks
Lecture 18 - Analysis of Chromatin Structure
Lecture 19 - Discovering Quantitative Trait Loci (QTLs)
Lecture 20 - Human Genetics, SNPs, and Genome Wide Associate Studies
Lecture 21 - Synthetic Biology: From Parts to Modules to Therapeutic Systems
Lecture 22 - Causality, Natural Computing, and Engineering Genomes