| Instructor(s) | Eric Harley [Coordinator] Office: ENG 287B Phone: TBA Email: eharley@torontomu.ca Office Hours: Thursdays 2-4 pm | ||||||||||
| Calendar Description | Introduction to analysis, management, and visualization of cellular information at the molecular level. The course includes an overview of mathematical modeling and simulation, pattern matching, methods for phylogenetics, gene recognition, distributed and parallel biological computing, designing and managing biological databases (both relational and object-oriented), linking disparate databases and data, data mining, reasoning by analogy, hypothesis formation and testing by machine. | ||||||||||
| Prerequisites | BLG 601 and CEN 199 and MTH 312 | ||||||||||
| Antirequisites | CPS 501 | ||||||||||
| Corerequisites | None | ||||||||||
| Compulsory Text(s): |
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| Reference Text(s): |
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| Learning Objectives (Indicators) | At the end of this course, the successful student will be able to:
NOTE:Numbers in parentheses refer to the graduate attributes required by the Canadian Engineering Accreditation Board (CEAB). | ||||||||||
| Course Organization | 3.0 hours of lecture per week for 13 weeks | ||||||||||
| Teaching Assistants | TBA | ||||||||||
| Course Evaluation |
Note: In order for a student to pass a course, a minimum overall course mark of 50% must be obtained. In addition, for courses that have both "Theory and Laboratory" components, the student must pass the Laboratory and Theory portions separately by achieving a minimum of 50% in the combined Laboratory components and 50% in the combined Theory components. Please refer to the "Course Evaluation" section above for details on the Theory and Laboratory components (if applicable). | ||||||||||
| Examinations | Midterm exam is in Week 8 during class time. Final exam is during exam period and is comprehensive, The exams are closed book and typically include multiple-choice, short-answer and programming questions. | ||||||||||
| Other Evaluation Information | None | ||||||||||
| Teaching Methods | Teaching methods: in person lectures with slides and discussion, problem-based learning using laptop to teach Python, implement algorithms for bioinformatics and run data mining algorithms, internet to do database searches. Student presentations of project results. | ||||||||||
| Other Information | None |
Week | Hours | Chapters / | Topic, description |
|---|---|---|---|
1-2 | 6 | 1 | Introduction |
3 | 3 | 2 | Computational Manipulation of DNA |
4-5 | 6 | 3 | Sequence Alignment |
6 | 3 | 4 | Database Searching and Multiple Sequence Alignment |
7 | 2 | D2L notes | (Reading week for CPS501 cohort) |
8 | 3 | 5 | Midterm (Thursday, Oct 24, 2h) |
9 | 3 | 5 | Substitution Matrices and Protein Alignments |
10 | 3 | 6 | Distance Measurement in Molecular Phylogenetics |
11 | 3 | 7 | Tree-building in Molecular Phylogenetics |
12 | 3 | 8 | DNA Sequencing and Assembly |
13 | 3 | 9 | Sequence-Based Gene Prediction |
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Refer to the Departmental FAQ page for furhter information on common questions.
The Library provides research workshops and individual assistance. If the University is open, there is a Research Help desk on the second floor of the library, or students can use the Library's virtual research help service to speak with a librarian.
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