Natural Sciences > Biology > Foundations of Computational and Systems Biology

Foundations of Computational and Systems Biology posted by duggu on 12/9/2007

Add To Favorites

Courseware/Lectures

Test/Tutorials

Course Highlights

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.

This course site includes an extensive listing of bioinformatics tools with links to online resources in the tools section as well as a full set of lecture notes.

Course Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Technical Requirements

MATLAB® software is required to run the .m files found on this course site. RasMol software or another molecular graphics program (e.g. DeepView, PyMol) is required to view the .pdb files found on this course site. Any number of biological sequence comparison software tools can be used to import the FASTA formatted sequence (.fa) files found on this course site. Use the Python Interpreter to run the .py files found on this course site. Media player software, such as QuickTime® Player, RealOne™ Player, or Windows Media® Player, is required to run the .avi files found on this course site.

*Some translations represent previous versions of courses.

Syllabus

Overview

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and also serves as an introduction to the analysis of complex biological systems. It covers principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling. It also includes exposure to currently emerging research areas. The subject is designed for advanced undergraduates and graduate students with strong backgrounds in either molecular biology or computer science, but not necessarily both. Foundational material covering basic programming skills or biological principles will be provided for students, according to their needs.

Prerequisites

Some background in basic genetics, biochemistry, and molecular biology is required for this course. We assume that you are familiar with chapters 3, 4, and 6 of the textbook Molecular Biology of the Cell, by Alberts et al. If you are having problems with this introductory material, please see your teaching assistant.

Textbooks

Mount, David W. Bioinformatics: Sequence and Genome Analysis. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press, 2001. ISBN: 0879695978.

Python programming text:

Lutz, Mark, and David Ascher. Learning Python. 2nd ed. Beijing; Cambridge, MA: O'Reilly, 2003. ISBN: 0596002815.

Recommended texts on basic molecular biology, cell biology, and biochemistry:

Watson, James, Tania Baker, Stephen Bell, Alexander Gann, Michael Levine, and Richard Losick. Molecular Biology of the Gene. 5th ed. San Francisco: Addison-Wesley, 2000. ISBN: 0805316434.

Alberts, Bruce, Alexander Johnson, Julian Lewis, Martin Raff, Keith Roberts, and Peter Walter. Molecular Biology of the Cell. 4th ed. New York: Garland Science, 2002. ISBN: 0815332181.

Branden, Carl-Ivar, and John Tooze. Introduction to Protein Structure. 2nd ed. New York: Garland Pub., 1991. ISBN: 0815303440.

The following text provides in-depth treatment of molecular mechanics and other modeling methods:

Leach, Andrew R. Molecular Modelling: Principles and Applications. Harlow, England: Longman, 1996. ISBN: 0582239338.

The following text contains more in-depth discussion of many of the algorithms and methods discussed in class:

Durbin, R., S. Eddy, A. Krogh, G. Mitchison. Biological Sequence Analysis. Cambridge, U.K.; New York: Cambridge University Press, 1998. ISBN: 0521620414.

Better than it sounds, and fun too! A very useful statistics introduction or refresher:

Gonick, Larry, and Woollcott Smith. The Cartoon Guide to Statistics. 1st HarperPerennial ed. New York, NY: Harper Perennial, 1993. ISBN: 0062731025.

The following text covers basic statistics, with applications to biology and medicine:

Glantz, S. A. Primer of Biostatistics. 4th ed. New York: McGraw-Hill, Health Professions Division, 1997. ISBN: 0070242682.

Structure of Class

The course is divided into four units. A majority of sessions will consist of lectures by the instructors. On four of the class days we have scheduled literature discussions in order to link material covered in lecture to current research. On each of the literature discussion days, two papers will be assigned. A short take-home quiz covering the main points of the two papers will be available two days before the discussion and will be collected at the beginning of class. This will be followed by an instructor-and/or student-led discussion. The quizzes, as well as participation in discussions, will contribute to your grade (see below).

Recitations

One biology and two computer science/math recitations are offered on a weekly basis. Students may elect to attend, twice a week, any combination of these recitations.

Homework

There will be four written or computer-based homework assignments. These are designed to promote deeper understanding of the algorithms discussed in class and to provide hands-on experience with bioinformatics and computational biology tools. Students may discuss the homework problems amongst themselves, but all students must complete their own assignment. Duplicate or nearly-identical homeworks from different students will not be accepted.

ASSIGNMENTS	TOPICS
Homework 1	Protein Sequence Analysis
Homework 2	DNA Sequence Analysis
Homework 3	Protein Structure Analysis
Homework 4	Systems Analysis

Homeworks must be turned in at the beginning of class on the due date to be eligible for full credit. Homeworks turned in by Monday at noon following the due date will be eligible for 50% credit. No homeworks will be accepted after this time. No exceptions.

Grading

Grades will be assigned based on the following scheme:

ACTIVITIES	PERCENTAGES
Four Homework Assignments (10+10+10+5%)	35%
Three Literature-based Quizzes (5% each)	15%
Two In-class Exams (20% Midterm; 25% Final)	45%
Class Participation	5%

Python Programming and Tutorial

Python and Perl are widely used in bioinformatics and computational biology. The majority of the homework assignments will include problems that involve writing simple programs in the scripting language Python. Because many students may have little or no programming experience, hands-on tutorials in Python will be offered by Dr. Peter Woolf during the second week of classes.

The aim of the Python tutorial is to give students a basic working knowledge of this scripting language. This tutorial is intended for students with little or no programming experience, and will focus on the tools and utilities needed to do research in bioinformatics and computational biology.

The TAs will also provide instruction on writing Python scripts in recitation sections during the first few weeks of class. See your TA if you are still having difficulty after attending all of these sessions and studying in the books recommended above.

Calendar

LEC #	TOPICS	KEY DATES
1	Introduction/Sequence Comparison and Dynamic Programming
2	Multiple Sequence Alignments I
3	Multiple Sequence Alignments II	Homework 1 handed out
4	Phylogenetic Analysis
5	Literature Discussion
6	Genome Sequencing and DNA Sequence Analysis	Homework 1 due
7	DNA Sequence Comparison and Alignment
8	DNA Motif Modeling and Discovery	Homework 2 handed out
9	Markov and Hidden Markov Models for DNA Sequences
10	DNA Sequence Evolution
11	RNA Secondary Structure Prediction	Homework 2 due
12	Literature Discussion on Predicting the Functions of DNA/RNA Sequences
13	Midterm Exam – in class – Protein and DNA Sequence Analysis
14	Protein Secondary Structure Prediction
15	Introduction to Protein Structure and Classification
16	Comparing Protein Structures Molecular Modeling: Methods and Applications
17	Using Computational Methods to Analyze, Predict, and Design Protein Sequences and Structures Solving Structures using X-ray Crystallographpy and NMR
18	Solving Structures using X-ray Crystallographpy and NMR (cont.) Homology Modeling	Homework 3 handed out
19	Methods for Protein Structure Prediction: Homology Modeling and Fold Recognition
20	Threading and ab initio Structure Prediction Computational Protein Design	Homework 3 due
21	Introduction to Systems Biology	Homework 4 handed out
22	Feedback Systems and Coupled Differential Equations
23	DNA Microarrays and Clustering
24	Literature Discussion on DNA Microarrays and Clustering	Homework 4 due (a day after Lec #24)
25	Computational Annotation of the Proteome
26	Literature Discussion on Computational Annotation of the Proteome

Readings

Mount, David W. Bioinformatics: Sequence and Genome Analysis. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press, 2001. ISBN: 0879695978.

LEC #	TOPICS	READINGS
1	Introduction/Sequence Comparison and Dynamic Programming	Mount. pp. 1-7, 29-35, 45-48, 51-64.
2	Multiple Sequence Alignments I	Mount. pp. 8-9, 65-89, 96-115, 140-155, 161-170.
3	Multiple Sequence Alignments II	Mount. pp. 139-150, 152-157, 161-171, 185-198.
4	Phylogenetic Analysis	Mount. pp. 237-280, 283-286, 291-308.
5	Literature Discussion	Pellegrini, M., E. M. Marcotte, M. J. Thompson, D. Eisenberg, and T. O. Yeates. "Assigning Protein Functions by Comparative Genome Analysis: Protein Phylogenetic Profiles." Proc. Natl. Acad. Sci. USA. 96, no. 8 (13 Apr 1999): 4285-8. Suel, G. M., S. W. Lockless, M. A. Wall, and R. Ranganathan. "Evolutionarily Conserved Networks of Residues Mediate Allosteric Communication in Proteins." Nat. Struct. Biol. 10, no. 1 (Jan 2003): 59-69.
6	Genome Sequencing and DNA Sequence Analysis	Woolf, Peter, Christopher Burge, Amy Keating, and Michael Yaffe. "Statistics and Probabilty Primer for Computational Biologists." MIT. BE.490/7.91 Spring 2004. (PDF) Bork, Peer, and Richard Copley. "Genome Speak." Nature 409 (15 Feb 2001): 815.
7	DNA Sequence Comparison and Alignment	Mount. Chap. 7.
8	DNA Motif Modeling and Discovery	Mount. Chap. 4.
9	Markov and Hidden Markov Models for DNA Sequences	Mount. Chap. 4.
10	DNA Sequence Evolution	Mount. Chap. 4.
11	RNA Secondary Structure Prediction	Mount. Chap. 4.
12	Literature Discussion on Predicting the Functions of DNA/RNA Sequences	Kellis, M., N. Patterson, M. Endrizzi, B. Birren, and E. S. Lander. "Sequencing and Comparison of Yeast Species to Identify Genes and Regulatory Elements." Nature 423, no. 6937 (15 May 2003): 241-54. Rivas, E., R. J. Klein, T. A. Jones, and S. R. Eddy. "Computational Identification of Noncoding RNAs in E. Coli by Comparative Genomics." Curr. Biol. 11, no. 17 (4 Sep 2001): 1369-73.
13	Midterm Exam – in class – Protein and DNA Sequence Analysis
14	Protein Secondary Structure Prediction
15	Introduction to Protein Structure and Classification
16	Comparing Protein Structures Molecular Modeling: Methods and Applications
17	Using Computational Methods to Analyze, Predict, and Design Protein Sequences and Structures Solving Structures using X-ray Crystallographpy and NMR	Marti-Renom, et. al. Annu. Rev. Biophys. Biomol. Struct. 29 (2000): 291-325.
18	Solving Structures using X-ray Crystallographpy and NMR (cont.) Homology Modeling	Marti-Renom, et. al. Annu. Rev. Biophys. Biomol. Struct. 29 (2000): 291-325.
19	Methods for Protein Structure Prediction: Homology Modeling and Fold Recognition	Bonneau, and Baker. Annu. Rev. Biophys. Biomol. Struct. 30 (2001): 173-189.
20	Threading and ab initio Structure Prediction Computational Protein Design
21	Introduction to Systems Biology	Ideker, and Lauffenburger. TRENDS in Biotechnology 21 (2003): 255-262.
22	Feedback Systems and Coupled Differential Equations
23	DNA Microarrays and Clustering
24	Literature Discussion on DNA Microarrays and Clustering	Segal, E., et. al. "Module Networks: Identifying Regulatory Modules and their Condition-specific Regulators from Gene Expression Data." Nature Genetics (2003). Beer, M., and S. Tavazoie. "Predicting Gene expression from Sequence." Cell (2004). Background Reading Friedman, N. "Inferring Cellular Networks Using Probabilistic Graphical Models." Science (2004). Appendix of Probability and Statistics Primer.
25	Computational Annotation of the Proteome
26	Literature Discussion on Computational Annotation of the Proteome	Bader, J. S., A. Chaudhuri, J. M. Rothberg, and J. Chant. "Gaining Confidence in High-throughput Protein Interaction Networks." Nat Biotechnol. 22, no. 1 (Jan 2004): 78-85. Epub (14 Dec 2003). Jansen, R., H. Yu, D. Greenbaum, Y. Kluger, N. J. Krogan, S. Chung, A. Emili, M. Snyder, J. F. Greenblatt, and M. Gerstein. "A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data." Science 302, no. 5644 (17 Oct 2003): 449-53.

Study Materials

Media player software, such as QuickTime® Player, RealOne™ Player, or Windows Media® Player, is required to run the .avi files in this section. RasMol software is required to run the .pdb files found in this section. Use the Python Interpreter to run the .py files found in this section.

Lecture 8 Motif Finding Animation

Gibbs Sampler, Weak Motif Animation (AVI) courtesy of Professor Chris Burge.

Python Programming

The majority of the homework assignments will include problems that involve writing simple programs in the scripting language, Python. Python, as well as Perl, is widely used in the fields of bioinformatics and computational biology. Because many students may have little or no programming experience, a hands-on python tutorial to take place across three sessions will be offered by Dr. Peter Woolf during the second week of classes.

Python Tutorial Overview

The aim of this tutorial is to give students a basic working knowledge of the scripting language Python. This course is intended for students with little or no programming experience, and will focus on the tools and utilities needed to do research in bioinformatics and computational biology.

My goal is to make the class informal and hands on, so please speak up if something does not make sense. Programming is not something that can be easily learned by watching, but must be learned by doing.

At minimum, by the end of this class, you should be able to read in a FASTA sequence from a file, parse it, and return the reverse compliment of that sequence to a file.

Tutorial Outline

Session One: Introduction to Unix, Text Editors, Basic Python Commands and Data Structures

Session Two: Flow Control in Python, Input/Output, Files, HTML

Session Three: Modules, Program Organization, and Regular Expressions

Text

Lutz, Mark, and David Ascher. Learning Python. 2nd ed. Beijing; Cambridge, MA: O'Reilly, 2003. ISBN: 0596002815.

Online Resources

The tutorial will roughly follow the structure of the standard documentation tutorial that can be found at: Online Python Tutorial.

If you are already a proficient programmer, look at: Dive into Python.

A good Unix-command Cheat Sheet can be found at: Unix-command Cheat Sheet.

For an introduction to regular expressions: Regular Expression HOWTO.

To quickly test your regular expressions, try the program: Kodos.

Finally, for lots of examples of good Python code related to Bioinformatics and Computational Biology, see: Biopython Web site.

In Class Exercise for Session Two

Review the notes on Unix Commands and Beginner's Python (PDF).

Parse the string in fasta.txt (TXT) to obtain the reverse compliment of the sequence section alone. Output this new string to a file called output.txt.

In Class Exercises for Session Three

In Python you can write programs that can run as a stand alone program or you can import them into other Python code. In fact, you have already been using Python programs every time you use an import command.

As an example of the framework of a basic Python program, see SampleProg.py (PY).

Load SampleProg.py from within the Python Interpreter and test the functions read_format and do_comparison.
Modify SampleProg.py so that it compares two numbers given at command line using do_comparison.

Regular expressions are a powerful text parsing tool that is widely used in bioinformatics. See the notes on regular expressions (PDF) for a summary of the commands.

Write a regular expression to extract all of the carbon atom position data from the file example.pdb (PDB). Print this data out.