Computational Systems Biology Main Page

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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.

Assignments

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
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...

 

Resources

Course related


 

Contents related

 



 



 

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