Difference between revisions of "Computational Systems Biology Main Page"

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<small>'''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, this site is unlikely to be very useful. If you are here because you are interested in general aspects of bioinformatics or computational biology, you may want to review the [http://en.wikipedia.org/wiki/Bioinformatics Wikipedia article on bioinformatics], or visit [http://www.openwetware.org/wiki/Wikiomics Wikiomics]. Contact boris.steipe(at)utoronto.ca with any questions you may have.</small>
 
<small>'''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, this site is unlikely to be very useful. If you are here because you are interested in general aspects of bioinformatics or computational biology, you may want to review the [http://en.wikipedia.org/wiki/Bioinformatics Wikipedia article on bioinformatics], or visit [http://www.openwetware.org/wiki/Wikiomics Wikiomics]. Contact boris.steipe(at)utoronto.ca with any questions you may have.</small>
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== BCB420 / JTB2020 ==
 
== BCB420 / JTB2020 ==
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<tr class="s2">
 
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<td>[[Eval_Sessions|'''5 Self-evaluation and Feedback sessions''']]("''Quiz''"<ref>I call these activities ''Quiz'' sessions for brevity, however they are not quizzes in the usual sense, since they rely on self-evaluation and immediate feedback.</ref>)</td>
 
<td>[[Eval_Sessions|'''5 Self-evaluation and Feedback sessions''']]("''Quiz''"<ref>I call these activities ''Quiz'' sessions for brevity, however they are not quizzes in the usual sense, since they rely on self-evaluation and immediate feedback.</ref>)</td>
−
<td>40 marks <small>(5 x 9)</small></td>
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<td>40 marks <small>(5 x 8)</small></td>
 
<td>30 marks <small>(5 x 6)</small></td>
 
<td>30 marks <small>(5 x 6)</small></td>
 
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*Signup to mailing list and Student Wiki.
 
*Signup to mailing list and Student Wiki.
  
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*Defining biological ''Systems''
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*Biological ''Systems'' science is based on network analysis
 
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</td>
 
<td class="sc">R Tutorial</td>
 
<td class="sc">R Tutorial</td>
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<td class="sc" ">'''In class: Tuesday, Jan. 24'''</td>
 
<td class="sc" ">'''In class: Tuesday, Jan. 24'''</td>
 
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</tr>
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* Data sources and workflows
 +
* Development principles
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* Writing '''R''' packages
  
 
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<td class="sc">3</td>
 
<td class="sc">3</td>
 
<td class="sc">
 
<td class="sc">
−
*Data sources and workflows
+
&nbsp;
 +
<!--
 +
*Expressing biological relationships as graphs.
 +
*Introduction to graph theory
 +
*Graph measures
 +
*Graphs in '''R'''.
 +
*Multigraphs and hypergraphs.
 +
*Linear and non-linear regression.
 +
* Gene regulatory networks revisited
 +
*Extracting features from properties.
 +
*Dimension reduction and feature discovery.
 +
*Imputing missing features.
 +
*Introduction to information theory.
 +
*Application to semantic similarity measures for GO.
 +
*Clustering concepts - how to construct sets of related elements from similar features.
 +
*Hierarchical clustering.
 +
*Partition clustering.
 +
*Graph clustering.
 +
*Cluster quality.
 +
*Clustering concepts - how to construct sets of related elements from similar features.
 +
*Hierarchical clustering.
 +
*Partition clustering.
 +
*Graph clustering.
 +
*Cluster quality.
 +
*Concepts of machine learning.
 +
*Applying machine learning to classification problems.
 +
-->
 +
 
 
</td>
 
</td>
 
<td class="sc">TBD</td>
 
<td class="sc">TBD</td>
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<td class="sc">4</td>
 
<td class="sc">4</td>
 
<td class="sc">
 
<td class="sc">
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*Development principles
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&nbsp;
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</td>
 
</td>
 
<td class="sc">TBD</td>
 
<td class="sc">TBD</td>
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<td class="sc">5</td>
 
<td class="sc">5</td>
 
<td class="sc">
 
<td class="sc">
−
*Expressing biological relationships as graphs.
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&nbsp;
−
*Introduction to graph theory
+
 
−
*Graph measures
 
−
*Graphs in '''R'''.
 
−
*Multigraphs and hypergraphs.
 
 
</td>
 
</td>
 
<td class="sc">TBD</td>
 
<td class="sc">TBD</td>
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<td class="sc">6</td>
 
<td class="sc">6</td>
 
<td class="sc">
 
<td class="sc">
−
*Linear and non-linear regression.
 
 
Note: No class on the following Tuesday, February 20 (Reading Week).
 
Note: No class on the following Tuesday, February 20 (Reading Week).
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</td>
 
</td>
 
<td class="sc">TBD</td>
 
<td class="sc">TBD</td>
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<td class="sc">7</td>
 
<td class="sc">7</td>
 
<td class="sc">
 
<td class="sc">
−
* Project status review
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* Project work
−
<!-- *Introduction to information theory.
+
 
−
*Application to semantic similarity measures for GO. -->
+
-->
 
</td>
 
</td>
 
<td class="sc">TBD</td>
 
<td class="sc">TBD</td>
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<td class="sc">
 
<td class="sc">
 
'''Note: March. 13 - Drop Date for the course.'''
 
'''Note: March. 13 - Drop Date for the course.'''
−
* Gene regulatory networks revisited
+
 
−
<!-- *Extracting features from properties.
+
* Project work
−
*Dimension reduction and feature discovery.
 
−
*Imputing missing features. -->
 
  
 
</td>
 
</td>
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<td class="sc">9</td>
 
<td class="sc">9</td>
 
<td class="sc">
 
<td class="sc">
−
*Introduction to information theory.
+
* Project work
−
*Application to semantic similarity measures for GO.
 
−
<!-- *Clustering concepts - how to construct sets of related elements from similar features.
 
−
*Hierarchical clustering.
 
−
*Partition clustering.
 
−
*Graph clustering.
 
−
*Cluster quality. -->
 
  
 
</td>
 
</td>
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<td class="sc">10</td>
 
<td class="sc">10</td>
 
<td class="sc">
 
<td class="sc">
−
 
+
* Project work
−
*Clustering concepts - how to construct sets of related elements from similar features.
 
−
*Hierarchical clustering.
 
−
*Partition clustering.
 
−
*Graph clustering.
 
−
*Cluster quality.
 
−
 
 
−
<!-- *Concepts of machine learning.
 
−
*Applying machine learning to classification problems. -->
 
  
 
</td>
 
</td>
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<td class="sc">11</td>
 
<td class="sc">11</td>
 
<td class="sc">
 
<td class="sc">
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*Integrating project elements.
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* Project work
  
 
</td>
 
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<td class="sc">12</td>
 
<td class="sc">12</td>
 
<td class="sc">
 
<td class="sc">
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*Presentation and documentation of results.
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* Project work
  
 
</td>
 
</td>

Revision as of 20:42, 10 January 2017

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, this site is unlikely to be very useful. 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.


 

Warning – this page and all associated course pages currently under intense revision. (2016-12-17)


 


 


 

BCB420 / JTB2020

These are the course pages for BCB420H (Computational Systems Biology). Welcome, you're in the right place.

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. When this course was conceived as a required part of the (then so called) Collaborative PhD Program in Proteomics and Bioinformatics in 2003, there was an urgent need to bring graduate students to a minimal level of computer skills and programming; prior experience was virtually nonexistent. Fortunately, the field has changed and our current graduate students are usually quite competent at least in some practical aspects of computational biology. In this course we profit from the rich and diverse knowledge of the problem-domain our graduate students have, while bringing everyone up to a level of competence in the practical, computational aspects.


The 2017 course...

In this course we pursue a wholly problem oriented format:

  • We start from an interesting challenge in computational systems biology;
  • We'll formulate an approach to this challenge as a project, defining the the resources we need – data sources, algorithms, programming- and collaboration support;
  • We'll define students' roles in the project according to their skills and experience;
  • Then we will implement the project.



Organization

First lecture this term: Tuesday, January 10. 2017 at 16:00 (4 pm), MSB 4171.

Attendance in person at the first lecture is mandatory. You will loose three participation marks if you are not present in person.[1]

This may seem silly but it is unfortunately necessary - I can't get this course started effectively if you are not present when we work out the organization of the course, sign you up to mailing list and Student Wiki, and discuss the syllabus for this term.


Dates
BCB420/JTB2020 is a Winter Term course.
Lectures: Tuesdays, 16:00 to 18:00. (Classes start at 10 minutes past the hour.)
Final Exam: None for this course.


Location
MS 4171 (Medical Sciences Building).


Departmental information
For BCB420 see the BCB420 Biochemistry Department Course Web page.
For JTB2020 see the JTB2020 Course Web page for general information.


Preparation

Undergraduate students in this course will often have taken my BCH441 course; graduate students come from a wide variety of backgrounds. The course requires (i) a solid understanding of molecular biology, (ii) introductory level knowledge of bioinformatics, (iii) a working knowledge of the 'R programming language. I am only weakly enforcing prerequisites, they are basically your own responsibility. Knowledge of molecular biology is something you have to acquire on your own and bring to class. If you are comfortable reading the introductory literature listed below, and understanding the biology, you should be good to go.

The preparation material detailed below will be the subject of our first Quiz in the second week of class. Please have a look at the three topics below in order to get a head start.


1 – A working knowledge of R ...
  • Work through the R tutorial on this site and complete the tasks and exercises in the tutorial and the associated scripts.


2 – A basic knowledge of Bioinformatics ...
  • Here is a list of detailed, introductory bioinformatics tutorials


3 – Project specific prereading ...

Read the following papers:




Grading and Activities

 
Activity Weight
BCB410 - (Undergraduates)		 Weight
JTB2020 - (Graduates)
5 Self-evaluation and Feedback sessions("Quiz"[2]) 40 marks (5 x 8) 30 marks (5 x 6)
Class project contributions 45 marks 45 marks
Participation 15 marks 15 marks
Contribution to Project Manuscript Draft   10 marks
Total 100 marks 100 marks


A note on marking

I do not 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.


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 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. A (non-exhaustive) overview of topics and useful links is linked here.


Timetable and syllabus

Syllabus and assignments will still be in flux for a few weeks.


 


Week In class: Tuesday, Jan. 10 Readings Assignment In class: Tuesday, Jan. 17
1
  • Syllabus
  • Projects
  • Important dates
  • Grading
  • Organization
  • Signup to mailing list and Student Wiki.
  • Biological Systems science is based on network analysis
 R Tutorial Quiz 1

Perspectives:

Customizing R and R Studio. Subsetting and filtering of vectors, arrays and lists.


 


  • Data sources and workflows
  • Development principles
  • Writing R packages
Week In class: Tuesday, Jan. 17 Readings Assignment In class: Tuesday, Jan. 24
2
  • Collaboration tools
  • Defining the class project
 TBD Quiz 2

Perspectives ... TBD.


 


Week In class: Tuesday, Jan. 24 Readings Assignment In class: Tuesday, Jan. 31
3

 

TBD Quiz 3

Perspectives ... TBD.


 


Week In class: Tuesday, Jan. 31 Readings Assignment In class: Tuesday, Feb. 7
4

 

TBD Quiz 4

Perspectives ... TBD


 


Week In class: Tuesday, Feb. 7 Readings Assignment (In class): Tuesday, Feb. 14
5

 

TBD Quiz 5

Perspectives ... TBD


 


Week (In class): Tuesday, Feb. 14 Readings Assignment In class: Tuesday, Feb. 28
6

Note: No class on the following Tuesday, February 20 (Reading Week).

TBD –

Perspectives ... TBD


 


Week In class: Tuesday, Feb. 28 Readings Assignment In class: Tuesday, March 7
7
  • Project work
-->
TBD –

Perspectives ... TBD


 


Week In class: Tuesday, March 7 Readings Assignment In class: Tuesday, March 14
8

Note: March. 13 - Drop Date for the course.

  • Project work
 TBD –

Perspectives ... TBD


 


Week In class: Tuesday, March 14 Readings Assignment In class: Tuesday, March 21
9
  • Project work
 TBD –

Perspectives ... TBD


 


Week In class: Tuesday, March 21 Readings Assignment In class: Tuesday, March 28
10
  • Project work
 TBD –

Perspectives ... TBD


 


Week In class: Tuesday, March 28 Readings Assignment In class: Tuesday, April 4
11
  • Project work
 TBD –

Perspectives ... TBD


 


Week In class: Tuesday, April 4 Readings
12
  • Project work
 TBD


 




In depth...


Resources

Course related



325C78 7097B8 9BACCF A8A5CC D7C0F0


 

Notes

  1. ↑ Only in case you are sick will you be excused. But in that case you must contact me before class.
  2. ↑ I call these activities Quiz sessions for brevity, however they are not quizzes in the usual sense, since they rely on self-evaluation and immediate feedback.


 

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