Computational Systems Biology Main Page

Computational Systems Biology

Welcome to the Computational Systems Biology Course Wiki.

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.

The Course

Organization

Dates

Lectures: Monday, 10:00 to 11:00 and Wednesday, 10:00 to 11:00 (... according to the Calendar. However we will decide on more suitable times at the first meeting and most likely we will meet in one two-hour slot rather than in two slots.)

First lecture: Monday, January 9. 2012 at 10:00 - Organization. Whatever you do, don't miss this lecture or you will immediately fall behind.

Location

TBD (Medical Sciences Building) - watch this space or e-mail the coordinator at (boris.steipe(at)utoronto.ca)

General

See the Course Web page for general information.

Textbook

???

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

Activity	Weight (Undergraduates)	Weight (Graduates)
5 Assignments	15 marks (5 x 3)	10 marks (5 x 2)
5 In-class quizzes	35 marks (5 x 7)	25 marks (5 x 5)
Open project	7 marks	5 marks
"Classroom" participation	3 marks	3 marks
Graduate project		17 marks
Final exam	40 marks	40 marks
Total	100 marks	100 marks

Weekly assignment and pre-reading quiz (50%), 3-stage project (30%), participation (20%), no final exam.

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. However I may adjust marks is if we phrase questions ambiguously on quizzes or if I decide that the final exam was too long.

Timetable and syllabus

I n t r o d u c t i o n
Week	Date	Lecturer	Contents
1	Jan. 9 - 14	Steipe	Organisation and Orientation
A r e a
Week	Date	Lecturer	Contents
2	Jan. 16 - 21	Steipe	Text
3	Jan. 23 - 28	Steipe	Text
4	Jan. 30 - Feb. 3	Steipe	Text
5	Feb. 6 - 10	Steipe	Text
6	Feb. 13 - 17	Steipe	Text
	Feb. 20 - 24		Reading Week - School closed
7	Feb. 27 - Mar. 2	Steipe	Text
8	Mar. 5 - 9	Steipe	Text
9	Mar. 12 - 16	Steipe	Text
10	Mar. 19 - 23	Steipe	Text
11	Mar. 26 - 30	Steipe	Text
12	Apr. 2 - 6	Steipe	Text

Topics collection

Functional annotation

• Prediction of function
• Data integration: Loose coupling, combining datasets to improve annotation quality, evidence combination
• GO
• text mining
• OMIM and other phenotype databases

Complete sets of components

-omics

• genomics
• transcriptome
• proteome, interactome
• metabolome, glycome, lipidome
• Working with -ome scale data
  • R and Bioconductor
  • Clustering and partitioning
  • Enrichment analysis
  • informal programming with perl/php/MySQL/
  • IDE - Komodo, KDevelop (also Eclipse, except no Perl)

Interactions, Pathways and Networks

• The biology of protein-protein interactions; physical vs. genetic interactions
• Interaction databases
• Interologs
• graph theory, graph types and metrics; small-world or random-geometric, date-hubs and party-hubs
• Cytoscape

Systems

• Gene regulatory networks
• Signal transduction networks
• Metabolic networks
• KEGG, BioCYC
• Extracting regulatory networks from gene expression data
• Extracting systems from -omics datasets through mutual information
• Structural network analysis http://www.csb.ethz.ch/research/structural
• Systems dynamics http://www.csb.ethz.ch/research/dynamic

Computable Models

• methods (ODEs, PDEs and their stochastic counterparts, Petri Nets, Cellular Automata)
• methods process calculi (pi-calculus, )
• representation (SBML, CellML)
• examples (E-Cell, M-Cell, Virtual Cell)
• constraint based modelling, Flux balance analysis
• Computational Synthetic Biology http://www.csb.ethz.ch/research/synthetic

BCH441

• Quantitative evolution: signals of recent change and selective pressure

BCB410

• GPU and Cloud computing

http://www.elsevier.com/wps/find/bookdescription.cws_home/707460/description

• Introducing Computational Systems Biology
• Enabling Information and Integration Technologies for Systems Biology:
  • Databases for Systems Biology.
  • Natural Language Processing and Ontology-enhanced Biomedical Literature Mining for Systems Biology
  • Integrated Imaging Informatics
  • Simpathica: A Computational Systems Biology Tool within the Valis Bioinformatics Environment
  • Standards, Platforms and Applications
• Foundations of Biochemical Network Analysis and Modeling
  • Introduction to Computational Models of Biochemical Reaction Networks
  • Biological Foundations of Signal Transduction and the Systems Biology Perspective
  • Reconstruction of Metabolic Network from Genome Information and Its Structural and Functional Analysis
  • Integrated Regulatory and Metabolic Models
• Computer Simulations of Dynamic Networks
  • A Discrete Approach to Network Modeling
  • Gene Networks: Estimation, Modeling and Simulation
  • Computational models for circadian rhythms: Deterministic versus stochastic approaches
• Multi-Scale Representations of Cells and Emerging Phenotypes
  • Multistability and Multicellularity: Cell Fates as High-dimensional Attractors of Gene Regulatory Networks
  • Spatio-Temporal Systems Biology
  • Cytomics—from cell states to predictive medicine
  • The IUPS Physiome Project: Progress and Plans.

In depth...

• Glossary
• Mutation Data Matrices
• Amino acids
• Database Identifiers

 

Resources

Course related

• The Course Web site.
• The Course Google Group.
• Netiquette for the Group mailing list
• Previous Exam_questions
• 2007 course feedback
• 2008 course feedback

 

Contents related

• The VMD tutorial
• A Stereo Vision tutorial
• MetaDatabase
• NAR January-2008 Database issue
• NAR July-2008 Web server issue

 

 

 

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