Postgraduate Course: Systems Approach to Modelling Cell Signal Transduction (PGBI11081)
Course Outline
| School | School of Biological Sciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | **Online Learning Course**
A major hope of the human genome project was that a fuller understanding of the genes associated with disease states would lead to a more rapid production of new lead compounds. In fact we also have to include the knowledge of how the biological organism responds to drugs. An aim of systems biology is to describe and understand the operation of complex biological systems. Current models can still only describe relatively simple systems, however the techniques associated with systems biology give useful insights to the drug discovery process. |
| Course description |
Not entered
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2019/20, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 2 |
| Course Start Date |
17/02/2020 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Online Activities 20,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
78 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
50% = report
50% = electronic portfolio comprising learning log and contribution to Skills Profile
|
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand the input of the various technologies to determine which proteins, genes and phosphorylation states of proteins are expressed or up regulated in a disease state. Be aware of the extent to which mathematical/computational models of pathways can be used to predict their behavior under differing conditions
- Be aware of the extent to which mathematical/computational models of pathways can be used to predict their behavior under differing conditions.
- Understand the limitations of pathway modelling with regards to systemic disease state modelling.
- Appreciate the value of literature mining in the generation of initial exploratory models, and the use of text mining technologies to infer new pathway/disease relationships
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | SystApp |
Contacts
| Course organiser | Dr Ramon Grima
Tel:
Email: Ramon.Grima@ed.ac.uk |
Course secretary | Mrs Claire Black
Tel: (0131 6)50 8637
Email: Claire.Black@ed.ac.uk |
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