MIT7.91JS14 (44 files)
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Type: Course
Bibtex:
Tags:
Bibtex:
@article{,
title= {MIT Foundations of Computational and Systems Biology 7.91J},
keywords= {},
journal= {},
author= {Christopher Burge and David Gifford and Ernest Fraenkel},
year= {2014},
url= {https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/index.htm},
license= {},
abstract= {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. This course is designed for advanced undergraduates and graduate students with strong backgrounds in either molecular biology or computer science, but not necessarily both. The scripting language Python—which is widely used for bioinformatics and computational biology—will be used; foundational material covering basic programming skills will be provided by the teaching assistants. Graduate versions of the course involve an additional project component.
### Prerequisites
There are different prerequisites for the various versions of the course. See the table for clarification.
7.01 Fundamentals of Biology
7.05 General Biochemistry
5.07 Biological Chemistry
6.00 Introduction to Computer Science and Programming
6.01 Introduction to Electrical Engineering and Computer Science
18.440 Probability and Random Variables
6.041 Probabilistic Systems Analysis and Applied Probability
| SES # | TOPICS | LECTURERS | KEY DATES |
|--------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------|------------|--------------------------------|
| L1 | Course Introduction: History of Computational Biology; Overview of the Course; Course Policies and Mechanics; DNA Sequencing Technologies | CB, DG, EF | |
| Genomic Analysis | | | |
| L2 | Local Alignment (BLAST) and Statistics | CB | |
| R1 | Statistics; Significance Testing; Bonferroni Correction | TA | |
| L3 | Global Alignment of Protein Sequences (NW, SW, PAM, BLOSUM) | CB | Project: Interests Due |
| L4 | Comparative Genomic Analysis of Gene Regulation | CB | |
| R2 | Clustering, Model Selection, and BIC Scores | TA | |
| Genomic Analysis—Next Gen Sequencing | | | |
| L5 | Library Complexity and Short Read Alignment (Mapping) | DG | Problem Set 1 Due |
| R3 | Burrows–Wheeler Transform (BWT) and Alignments. Guest Lecture: Heng Li (Broad Institute) | GL | |
| L6 | Genome Assembly | DG | Project: Teams Due |
| L7 | ChIP-seq Analysis; DNA-protein Interactions | DG | |
| R4 | Simultaneous ChIP-seq Peak Discovery and Motif Sampling | TA | |
| L8 | RNA-sequence Analysis: Expression, Isoforms | DG | |
| Modeling Biological Function | | | |
| L9 | Modeling and Discovery of Sequence Motifs (Gibbs Sampler, Alternatives) | CB | |
| R5 | Gene Expression Program Discovery Using Topic Models | TA | |
| L10 | Markov and Hidden Markov Models of Genomic and Protein Features | CB | |
| L11 | RNA Secondary Structure—Biological Functions and Prediction | CB | Problem Set 2 Due |
| R6 | Probabilistic Grammatical Models of RNA Structure | TA | |
| E1 | Exam 1 | | |
| Proteomics | | | |
| L12 | Introduction to Protein Structure; Structure Comparison and Classification | EF | |
| R7 | Protein Amino Acid Sidechain Packing Using Markov Random Fields | TA | Project: Research Strategy Due |
| L13 | Predicting Protein Structure | EF | |
| L14 | Predicting Protein Interactions | EF | Problem Set 3 Due |
| R8 | Protein / Protein Interaction Prediction Using Threading | TA | |
| Regulatory Networks | | | |
| L15 | Gene Regulatory Networks | EF | |
| L16 | Protein Interaction Networks | EF | |
| R9 | Regression Trees | TA | |
| L17 | Logic Modeling of Cell Signaling Networks. Guest Lecture: Doug Lauffenburger | GL | |
| L18 | Analysis of Chromatin Structure | DG | Problem Set 4 Due |
| R10 | BayesNets | TA | |
| Computational Genetics | | | |
| L19 | Discovering Quantitative Trait Loci (QTLs) | DG | Project: Written Report Due |
| R11 | Narrow Sense Heritability | TA | |
| L20 | Human Genetics, SNPs, and Genome Wide Associate Studies | DG | |
| L21 | Synthetic Biology: From Parts to Modules to Therapeutic Systems. Guest Lecture: Ron Weiss | GL | Problem Set 5 Due |
| R12 | Exam Review | TA | |
| E2 | Exam 2 | | |
| L22 | Causality, Natural Computing, and Engineering Genomes. Guest Lecture: George Church | GL | |
| P1 | Presentations | | |
| P2 | Presentations (cont.) | | |
### Instructor(s)
Prof. Christopher Burge
Prof. David Gifford
Prof. Ernest Fraenkel
MIT Course Number
7.91J / 20.490J / 20.390J / 7.36J / 6.802J / 6.874J / HST.506J
As Taught In
Spring 2014
Level
Undergraduate / Graduate
},
superseded= {},
terms= {}
}
