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2019/20 Taught Postgraduate Module Catalogue

BIOL5372M Advanced Biomolecular Technologies

20 creditsClass Size: 80

Module manager: Professor Alexander Breeze
Email: a.l.breeze@leeds.ac.uk

Taught: Semesters 1 & 2 View Timetable

Year running 2019/20

Pre-requisite qualifications

B.Sc. in Biological Sciences or equivalent

Module replaces

BIOL5205M

This module is not approved as an Elective

Objectives

To expose students to a range of techniques and technologies that are applicable to the modern biosciences.
To develop data analysis and problem solving skills.
To expose students to current research trends in the biosciences and to leading researchers in the field.

Learning outcomes
On completion of this module, students should be able to:

- describe in-depth the theoretical concepts underpinning a range of techniques and technologies that are applicable to the modern biosciences including molecular biology methods, genetic analyses, genomics, proteomics and structural biology;
- apply this knowledge to analyse data and solve problems and explain how the techniques could be applied to their own research/subject area;
- select appropriate statistical analyses for a range of common forms of data, conduct statistical analyses, and interpret statistical evidence;
- summarise and evaluate current research trends in the field.


Syllabus

This module will consist of two streams.
- In one stream, students will attend a series of lectures focused on specific techniques used in modern biosciences including polymerase chain reaction (PCR), site-directed mutagenesis and sequencing, protein expression, chromatographic techniques, x-ray crystallography, mass spectrometry, nuclear magnetic resonance (NMR), bioimaging, microarrays, atomic force microscopy, wave DNA fragment analysis, isolation and use of stem cells, phylogenetics and flow cytometry. Students will be provided with demonstrations of a range of facilities associated with the techniques and there will be data analysis tutorials linked to four of these sessions.
- In addition, students will attend a series of lectures and computer classes on statistics. Topics will include: uses of statistics, variance analysis, regression and correlation and categorical data analysis.

The second stream will consist of the Faculty research seminars and students will attend at least one research seminar per week from the series of seminars available. Students will also attend the annual Faculty Postgraduate (PhD) symposium.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Workshop12.002.00
Data Handling Session13.003.00
Data Handling Session41.004.00
Lecture231.0023.00
Practical40.502.00
Seminar121.0012.00
Seminar201.0020.00
Tutorial41.004.00
Private study hours130.00
Total Contact hours70.00
Total hours (100hr per 10 credits)200.00

Private study

Students should note that the following information is for guidance only. The actual time required for the various elements will vary between students
2 hours reading per lecture: 46 hours
1.5 hours reading per research seminar: 30 hours
Preparation for statistics practical: 2 hours
Preparation for assessed data analysis/test: 52 hours

Opportunities for Formative Feedback

Progress will be monitored by participation at the data analysis tutorials and performance in assessed coursework. Students will be required to maintain a log of notes for each research seminar and will be required to submit these periodically for assessment.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
In-course Assessment4 data-handling problems associated with specific techniques discussed in the course (2 per semester) - 20% each80.00
In-course AssessmentNotes made during each research seminar will be assessed on a pass/fail basis.0.00
In-course AssessmentTest20.00
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 30/04/2019

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