Difference between revisions of "Computational Systems Biology Main Page"

From "A B C"
Jump to navigation Jump to search
Line 32: Line 32:
 
<section end=CSB_main_organization />
 
<section end=CSB_main_organization />
  
 +
<section begin=CSB_main_grading />
 
===Grading and Activities===
 
===Grading and Activities===
  
Line 85: Line 86:
 
;A note on marking
 
;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. <small>I may however adjust marks is if we phrase questions ambiguously on quizzes or if I decide that the final exam was too long.</small>
 
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. <small>I may however adjust marks is if we phrase questions ambiguously on quizzes or if I decide that the final exam was too long.</small>
 +
<section end=CSB_main_grading />
  
 
== Prerequisites ==
 
== Prerequisites ==

Revision as of 20:52, 29 December 2011

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

I have prepared a number of assignments for the course that I expect everyone to complete, however there will be no required submissions for these assignments. Assignment-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 assignments 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 we phrase questions ambiguously on quizzes or if I decide that the final exam was too long.


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. 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.


Assignments

Assignment due dates ...

Timetable and syllabus

 


I n t r o d u c t i o n
Week Date Lecturer Contents Activities
1 Jan. 9 - 14 Steipe
  • Introduction to Computational Systems Biology
  • Databases and services
  • Course Organisation
F u n c t i o n a l   A n n o t a t i o n
Week Date Lecturer Contents Activities
2 Jan. 16 - 21 Steipe
  • GO, OMIM and other phenotype databases
  • Data mining
  • Data integration: Loose coupling, combining datasets to improve annotation quality, evidence combination
  • Prediction of function
Quiz 1
"-omics"
Week Date Lecturer Contents Activities
3 Jan. 23 - 28 Steipe
  • 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)
Quiz 2, project concept due
4 Jan. 30 - Feb. 3 Steipe
  • genomics
  • transcriptome
Quiz 3
5 Feb. 6 - 10 Steipe
  • proteome, interactome
  • metabolome, glycome, lipidome
Quiz 4
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 Lecturer Contents Activities
6 Feb. 13 - 17 Steipe
  • Cytoscape
  • Graph theory, graph types and metrics; small-world or random-geometric, date-hubs and party-hubs
Quiz 5, project outline due
  Feb. 20 - 24 Reading Week - School closed
7 Feb. 27 - Mar. 2 Steipe
  • The biology of protein-protein interactions; physical vs. genetic interactions
  • Interaction databases
  • Interaction prediction, Interologs
Quiz 6
S y s t e m s
Week Date Lecturer Contents Activities
8 Mar. 5 - 9 Steipe
  • Gene regulatory networks
  • Signal transduction networks
  • Metabolic networks
  • KEGG, BioCYC
Quiz 7
9 Mar. 12 - 16 Steipe
  • Extracting regulatory networks from gene expression data
  • Extracting systems from -omics datasets through mutual information
Quiz 8
10 Mar. 19 - 23 Steipe Quiz 9, project final submission due
M o d e l s
Week Date Lecturer Contents Activities
11 Mar. 26 - 30 Steipe
  • 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)
12 Apr. 2 - 6 Steipe


Additional Topics

  • ...


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

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

In depth...

 

Resources

Course related


 

Contents related

 



 



 

325C78 7097B8 9BACCF A8A5CC D7C0F0