Course Information

Course

Code:	MIT 7.91J
Subject:	Biology
Topic:	Bioinformatics
Views:	28,582

Educator

Name:	Massachusetts Institute of Technology (MIT)
Type:	University

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Foundations of Computational and Systems Biology

Video Lectures

Displaying all 22 video lectures.

I. Genomic Analysis
Lecture 1 Play Video	Introduction to Computational and Systems Biology Instructor: Christopher Burge, David Gifford, Ernest Fraenkel In this lecture, Professors Burge, Gifford, and Fraenkel give an historical overview of the field of computational and systems biology, as well as outline the material they plan to cover throughout the semester.
Lecture 2 Play Video	Local Alignment (BLAST) and Statistics Instructor: Christopher Burge In this lecture, Professor Burge reviews classical and next-generation sequencing. He then introduces local alignment (BLAST) and some of the associated statistics.
Lecture 3 Play Video	Global Alignment of Protein Sequences (NW, SW, PAM, BLOSUM) Instructor: Christopher Burge In this lecture, Prof. Burge discusses global sequence alignment and gapped local sequence alignment. He later talks about substitution matrices for protein comparison.
Lecture 4 Play Video	Comparative Genomic Analysis of Gene Regulation Instructor: Christopher Burge Prof. Burge discusses comparative genomics. He begins with a review of global alignment of protein sequences, then talks about Markov models, the Jukes-Cantor model, and Kimura models. He discusses types of selection: natural, negative, and positive.
II. Genomic Analysis—Next Gen Sequencing
Lecture 5 Play Video	Library Complexity and Short Read Alignment (Mapping) Instructor: David Gifford Prof. Gifford talks about library complexity as it relates to genome sequencing. He explains how to create a full-text minute-size (FM) index, which involves a Burrows-Wheeler transform (BWT). He ends with how to deal with the problem of mismatching.
Lecture 6 Play Video	Genome Assembly Instructor: David Gifford Prof. Gifford talks about two different ways to assemble a genome de novo. The first approach is overlap layout consensus assemblers, as exemplified by string graph assemblers. The second approach is de Bruijn graph-based assemblers.
Lecture 7 Play Video	ChIP-seq Analysis; DNA-protein Interactions Instructor: David Gifford In this lecture, Prof. David Gifford discusses transcriptional regulation. He talks about techniques that can elucidate how genes are regulated, and how gene regulators interact with the genome.
Lecture 8 Play Video	RNA-sequence Analysis: Expression, Isoforms Instructor: David Gifford This lecture by Prof. David Gifford is about RNA-seq (RNA sequencing), a method of characterizing RNA molecules through next-generation sequencing. He begins with the principles of RNA-seq, and then moves on to how to analyze the data generated by RNA-seq.
III. Modeling Biological Function
Lecture 9 Play Video	Modeling and Discovery of Sequence Motifs Instructor: Christopher Burge This lecture by Prof. Christopher Burge covers modeling and discovery of sequence motifs. He gives the example of the Gibbs sampling algorithm. He covers information content of a motif, and he ends with parameter estimation for motif models.
Lecture 10 Play Video	Markov and Hidden Markov Models of Genomic and Protein Features Instructor: Christopher Burge 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.
Lecture 11 Play Video	RNA Secondary Structure; Biological Functions and Predictions Instructor: Christopher Burge Lecture 11 is about RNA secondary structure. Prof. Christopher Burge begins with an introduction and biological examples of RNA structure. He then talks about two approaches for predicting structure: covariation and energy minimization.
IV. Proteomics
Lecture 12 Play Video	Introduction to Protein Structure; Structure Comparison and Classification Instructor: Ernest Fraenkel Professor Ernest Fraenkel begins his unit of the course, which moves across scales, from atoms to proteins to networks. This lecture is about the structure of proteins, and how biological phenomena make sense in light of protein structure.
Lecture 13 Play Video	Predicting Protein Structure Instructor: Ernest Fraenkel This lecture on predicting protein structure covers refining a partially correct structure. Methods include energy minimization, molecular dynamics, and simulated annealing. He moves on to methods for predicting structure from a sequence of amino acid.
Lecture 14 Play Video	Predicting Protein Interactions Instructor: Ernest Fraenkel This lecture is on predicting protein interactions. He discusses structural predictions of protein-protein interactions. He then talks about how measurements of protein-protein interactions are made and Bayes Net prediction of protein-protein interactions.
V. Regulatory Networks
Lecture 15 Play Video	Gene Regulatory Networks Instructor: Ernest Fraenkel This lecture by Prof. Ernest Fraenkel is about gene regulatory networks. He begins by finishing Lecture 14's discussion of protein-protein interactions.
Lecture 16 Play Video	Protein Interaction Networks Instructor: Ernest Fraenkel This lecture by Prof. Ernest Fraenkel is on protein interaction networks. He covers network models, including their structure and an analysis. He asks, "can we use networks to predict function?" He ends with a data integration example.
Lecture 17 Play Video	Logic Modeling of Cell Signaling Networks Instructor: Doug Lauffenburger Prof. Doug Lauffenburger delivers a guest lecture on the topic of logic modeling of cell signaling networks. He begins by giving a conceptual background of the subject, and then discusses an example involving hepatocyes (liver cells).
Lecture 18 Play Video	Analysis of Chromatin Structure Instructor: David Gifford This lecture begins with the epigenetic state, which regulates gene function without changing DNA. Then, how to estimate the protein occupancy of the genome via computational methods. Lastly, how to map enhancers to their regulatory targets.
VI. Computational Genetics
Lecture 19 Play Video	Discovering Quantitative Trait Loci (QTLs) Instructor: David Gifford This lecture is guided by the question "Where is missing heritability found?" Prof. David Gifford discusses computational models that can predict phenotype from genotype. He then discusses how to discover and model quantitative trait loci.
Lecture 20 Play Video	Human Genetics, SNPs, and Genome Wide Associate Studies Instructor: David Gifford This lecture by Prof. David Gifford is on human genetics. He covers how scientists discover variation in the human genome. He discusses how to prioritize variants based on their importance. And then covers how to prove causation, not just correlation.
Lecture 21 Play Video	Synthetic Biology: From Parts to Modules to Therapeutic Systems Instructor: Ron Weiss This guest lecture by Prof. Ron Weiss is on synthetic biology. Prof. Weiss describes how he came to be a synthetic biologist, followed by an overview of the field. He covers basic , technologies for scalability, and programmable therapeutics.
Lecture 22 Play Video	Causality, Natural Computing, and Engineering Genomes Instructor: George Church This guest lecture by Prof. George Church is on the topic of causality, in particular, how to use genomic data and the tools of natural computing to differentiate between correlation and causation.