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Curricular information is subject to change
On completion of this module students will have a comprehensive overview of the analytical tools, methods and approaches and skills that the pharma industry will need to apply systems biology approaches. In particular students should be able to:
1 Perform statistical analysis of proteomics high-throughput data.
2 Use different bioinformatic tools such as Geneset Enrichment Analysis , String and L100CDS2.
3. Be familiar with the state-of–the-art OMIC technologies used in drug development.
4. Have a comprehensive view of signal transduction networks and the way this network can be targeted to develop new drugs against common diseases such as cancer, Alzheimer’s and cardiovascular disease.
5. Present Omics analysis in written and oral reports.
Student Effort Type | Hours |
---|---|
Lectures | 16 |
Tutorial | 6 |
Computer Aided Lab | 8 |
Autonomous Student Learning | 80 |
Total | 110 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: High-throughput analysis written assignment | Unspecified | n/a | Graded | Yes | 40 |
Group Project: Poster/presentation of case study | Unspecified | n/a | Graded | Yes | 25 |
Group Project: Poster/presentation of case study Group written report |
Unspecified | n/a | Graded | Yes | 20 |
Attendance: Attendance of CAL sessions and participation in peer review activities. | Throughout the Trimester | n/a | Graded | No | 15 |
Remediation Type | Remediation Timing |
---|---|
In-Module Resit | Prior to relevant Programme Exam Board |
• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Peer review activities
• Self-assessment activities
Feedback will be on acontinuous basis with the aid of tutorials and in class discussion
Name | Role |
---|---|
Dr Luis Fernando Iglesias Martinez | Lecturer / Co-Lecturer |
Dr Aleksandar Krstic | Lecturer / Co-Lecturer |
Dr Jens Rauch | Lecturer / Co-Lecturer |
Dr Oleksii Rukhlenko | Lecturer / Co-Lecturer |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 24, 25, 26, 31, 32, 33 | Thurs 11:00 - 12:50 |
Computer Aided Lab | Offering 1 | Week(s) - 23 | Thurs 11:00 - 12:50 |
Computer Aided Lab | Offering 1 | Week(s) - 29, 30 | Thurs 11:00 - 12:50 |