Computational Systems Biology Main Page

Computational Systems Biology

Course Wiki for BCB420 (Computational Systems Biology) and JTB2020 (Applied Bioinformatics).

This is our main tool to coordinate information, activities and projects in University of Toronto's computational systems biology course BCB420. If you are not one of our students, you can still browse this site, however only users with a login account can edit or contribute or edit material. If you are here because you are interested in general aspects of bioinformatics or computational biology, you may want to review the Wikipedia article on bioinformatics, or visit Wikiomics. Contact boris.steipe(at)utoronto.ca with any questions you may have.

BCB420 / JTB2020

These are the course pages for BCB420H (Computational Systems Biology). Welcome, you'll feel right at home here.

These are also the course pages for JTB2020H (Applied Bioinformatics). How come? Why is JTB2020 not the graduate equivalent of BCB410 (Applied Bioinformatics)? Let me explain.

Organization

The course will begin on Wednesday, January 9. 2013.
Classes end on Wednesday, April 3.

Dates

Lectures: Wednesdays, 14:00 to 16:00.

Exam: None for this course.

Location

MS3290 (Medical Sciences Building).

General

For BCB420 see the BCB420 Course Web page for general information.

For JTB2020 see the JTB2020 Course Web page for general information.

Recommended textbooks

Depending on your background, various levels of textbooks may be suitable. I will bring my evaluation copies to class so you can decide what may work for you.

Understanding Bioinformatics (Zvelebil & Baum) is a decent general introduction to many aspects of bioinformatics. It was published in 2007, an updated version is urgently needed. Still, some of the basics (like the algorithm for optimal sequence alignment) don't change. (Amazon) (Indigo) (ABE books)

Practical Bioinformatics (Agostino) covers some of the material of the BCH441 exercises. Expect a no-nonsense introduction to the very most basic stuff. I have my pet peeves about this book (as I have for many others, eg. why in the world do they still teach CLUSTAL when all available studies demonstrate it to be the least accurate MSA algorithm by a margin???), but if you haven't taken BCH441, this may serve you well. And if you did take BCH441, it may consolidate some ideas that I wasn't clear about. (Amazon) (Indigo) (ABE books)

If you are aware of recent good textbooks, or have your own opinions about these or other books, let me know.

Submissions

This is an electronic submission only course; but if you must print material, you might consider printing double-sided. Learn how, at the Print-Double-Sided Student Initiative.

Grading and Activities

Exercises for the course will be linked from Assignment Pages. I expect everyone to complete them, however there will be no required submissions for exercises. Exercise-related questions as well as pre-reading related questions will be part of the weekly quizzes. Don't expect to do well on the quizzes unless you have done the exercises and completed the pre-reading carefully. This course demands a lot of your discipline and time-management. A large portion of your grade will be contributed by the Open Project. JTB2020 students will also complete a number of Applied Bioinformatics Exercises. Deliverables for the course will be completed well before end-of-term crunch time and there will be no final exam.

Activity	Weight BCB410 - (Undergraduates)	Weight JTB2020 - (Graduates)
9 In-class quizzes	54 marks (9 x 6)	36 marks (9 x 4)
Open project	30 marks	30 marks
"Classroom" participation	16 marks	16 marks
Applied Bioinformatics Exercises		18 marks
Total	100 marks	100 marks

A note on marking

It is not my policy to adjust marks towards a target mean and variance (i.e. there will be no "belling" of grades). I feel strongly that such "normalization" detracts from a collaborative and mutually supportive learning environment. If your classmate gets a great mark because you helped him with a difficult concept, this should never have the effect that it brings down your mark through class average adjustments. Collaborate as much as possible, it is a great way to learn. I may however adjust marks is if I phrase questions ambiguously on quizzes.

Prerequisites

You must have taken an introductory bioinformatics course as a prerequisite, or otherwise acquired the necessary knowledge. Therefore I expect familiarity with the material of my BCH441 course. If you have not taken BCH441, please check the requirements below and update your knowledge and skills before the course starts. I will not make accommodations for lack of prerequisites. Please check the syllabus for this course below to find whether you need to catch up on additional material, and peruse this site to find the information you may need.

• Solid knowledge of the properties of the proteinogenic amino acids, their names and one-letter code;
• Understanding of the nature of sequence and structural data and the common formats in which such data is stored;
• Familiarity with the molecular visualization program VMD;
• Ability to view molecular structures in (wall-eyed) stereo;
• Familiarity with NCBI, EBI and PDB databases and services;
• Use of the EMBOSS suite of sequence analysis tools;
• Familiarity with the computation of multiple sequence alignments and the Jalview alignment editor;
• Computation and analysis of phylogenetic trees;
• Concepts of protein structure prediction; homology modeling;
• An introduction to statistics with R.

Exercises and Pre-reading

All course units will have associated exercises that are topics for the following week's quiz.

All course units will have assigned pre-readings that are topics for the current week's quiz.

Timetable and syllabus

I n t r o d u c t i o n
Week	Date	Topics	Activities	Assignment
1	Jan. 7 - 13	Computational Biology aims to bring biology from its descriptive beginnings to a truly predictive science, based on consistent and well understood principles. In the systems biology field of computational biology, we deal primarily with large-scale, cross-sectional data, its relationships and hierarchies. Course Organisation Introduction to Computational Systems Biology	—	Assignment 1
D a t a
Week	Date	Topics	Activities	Assignment
2	Jan. 14 - 20	In principle, most of the data of interest to us is freely available on the Web, in public repositories. However, the number of databases and associated Web services is large and in constant flux and integrating the data has its own issues. The most important issue is the question of how to define function and make this concept computable; from this arises the central role that ontologies play in the field. CSB on the Web: Databases and services Data integration Ontologies and phenotypes Prediction of function	Quiz 1	Assignment 2
3	Jan. 21 - 27	The large volume of data in any given systems biology experiment basically precludes the manual, gene-by-gene analysis of results. Questions arise regarding computational strategies for gene lists, especially the statistical tools and strategies we have at our disposal, and a minimum set of programming and automation skills. Lists of genes - a common outcome of high-throughput experiments Gene lists Enrichment analysis The GSEA approach Statistics Principles of Statistics in molecular biology R Bioconductor Clustering and Classification Exploratory Data Analysis Data mining Programming Informal programming Using an IDE (Integrated Development Environments)	Quiz 2, project concept due	Assignment 3
"-omics"
Week	Date	Topics	Activities	Assignment
4	Jan. 28 - Feb. 3	Genome sequencing brought the first complete overview of the information underlying life; the associated concept of the transcriptome - a description of which portion of the genome is expressed at what time - is the most basic functional description of this information. -omics Genome Transcriptome	Quiz 3	Assignment 4
5	Feb. 4 - 10	Many more holistic, or cross-sectional descriptions of the molecular composition and assembly of the cell are being worked on. Proteome Metabolome Glycome Lipidome	Quiz 4, project outline due	Assignment 5
I n t e r a c t i o n s ,   P a t h w a y s   a n d   N e t w o r k s
Week	Date	Theme	Activities	Assignment
6	Feb. 11 - 17	Fundamentally, -omics descriptions provide us with lists of components. However, to understand how things work, we need to address the relationships of the components - how things are put together: the molecular blueprints. At its most basic level, this is the question of molecular interactions in the cell. A quantitative description of molecular interactions relies heavily on the mathematical discipline of graph theory. Cytoscape is a visualization and analysis platform for molecular interactions. Graph theory Cytoscape	Quiz 5	Assignment 6
	Feb. 18 - 24	Reading Week - School closed
7	Feb. 25 - Mar. 3	Here we focus on the biological objects of "Interaction Science". The Interactome Interaction databases Pathways and Networks Interaction prediction	Quiz 6	Assignment 7
S y s t e m s
Week	Date	Topics	Activities	Assignment
8	Mar. 4 - 10	Several key networked systems exist in the cell. Here we discuss their paradigms, how they are constructed from experimental data and examples of how they can be organized in databases. Gene regulatory networks Metabolic networks Signaling networks Developmental networks Pathway and network databases KEGG BioCyc	Quiz 7	Assignment 8
9	Mar. 11 - 17	Information theory has proven to be one of the cornerstones of biological analysis. A good example of its power and utility is the definition of systems in large-scale biological datasets. Extracting systems from -omics datasets through mutual information	Quiz 8	Assignment 9
10	Mar. 18 - 24	Life is not static, only death is. Yet, the dynamic nature of biological systems is often overlooked. Systems dynamics	Quiz 9, project final submission due	Assignment 10
M o d e l s
Week	Date	Theme	Activities	Assignment
11	Mar. 25 - 31	The notion that computational biology will at some time become predictive is tied to the idea that we will be able to model the cell's systems. Many approaches exist, each with its own strengths and weaknesses; how to integrate such models - preferrably across multiple scales - is a question of its own. Modelling principles Model representations Modelling methods Modelling examples	—	Assignment 11
12	Apr. 1 - 7	Do we really understand systems biology? Computational Synthetic Biology is the ultimate test. Computational Synthetic Biology	—	Assignment 12

In depth...

• Glossary
• Mutation Data Matrices
• Amino acids

Resources

Course related

• The Course Google Group.
• Netiquette for the Group mailing list

Contents related

• The VMD tutorial
• A Stereo Vision tutorial

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