Undergraduate Course: Biomedical Sciences 3 (BIME09008)
Course Outline
| School | Deanery of Biomedical Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 9 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 40 |
ECTS Credits | 20 |
| Summary | The course will attempt to develop students' understanding of how current biomedical knowledge is generated from experiment and disseminated through the research literature, to prepare students for the transition to senior Honours. It aims to provide students with a secure grounding in the core skills of designing scientifically valid experiments, collecting, analysing and interpreting data, communicating scientific ideas and results, and in being able to critically evaluate primary research papers. It will cover a variety of experimental techniques commonly used in the biomedical sciences, so that students have an appreciation of when such techniques can be used, their strengths and weaknesses, and the type of data they produce. To illustrate some broad themes within contemporary biomedical sciences and the power of interdisciplinary approaches, the course will also cover the drug discovery and development process, the use of computational modelling approaches, the growing importance of large datasets (eg from next-generation sequencing and microarrays), and ethical issues in biomedical research.
Teaching will be through a combination of lectures, practicals (both wet and dry), and tutorials. Each practical and tutorial will be linked to associated material covered in the lecture series. Extensive use will also be made of online learning environments to provide learning resources, self-assessment exercises, and peer-feedback mechanisms (PeerWise). A variety of in-course assessments (in both semesters) will give an opportunity to students to assess their understanding of material and to receive both formative and summative feedback. |
| Course description |
Lectures will be structured around several themes:
Keynote lectures: 4 lectures scheduled across the year illustrating how integrated application of the approaches covered in this course are furthering understanding of key issues in biomedical science.
Contemporary themes: 6 lectures covering the drug discovery and development process, the use of modelling approaches, the growing importance of large datasets (eg in bioinformatics), and ethical issues in biomedical research.
Obtaining data from experiments: 7 lectures on experimental design and key experimental techniques (including PCR, immunohistochemistry, in situ hybridization, transgenic animals, loss/gain of function analyses, imaging techniques, electrophysiology, and high-throughput methods of molecular analysis). Lectures will frame the techniques in the context of specific biomedical topics (eg role of genes in diseases such as cystic fibrosis, Alzheimers and stroke). The emphasis will be on a 'problem-driven' rather than 'technique-driven' mode of teaching.
Interpreting data (getting knowledge from it): 3 lectures on data handling, statistical analysis (use and misuse), formal hypothesis testing. Each lecture will be linked to associated learning resources, online self-assessment materials, or a practical.
Evaluation of research papers: 4 lectures focusing on the research literature, peer review, how to quickly assimilate key points of a paper, plus critical evaluation of papers ie assessing whether the various steps in the research study were both carried out in an appropriate manner and reported in a sufficiently detailed way (considering issues of experimental design, choice of experimental techniques, statistical analysis, and interpretation). These lectures prepare students for tutorial sessions where these skills will also be developed.
Scientific communication: 1 or 2 lectures on how to effectively communicate biomedical data and knowledge in a variety of formats. These prepare students for elements of ICA in semester 2 that are also linked to semester 2 tutorials.
Additional lectures will serve to introduce the concepts involved in specific practicals and to give rapid (class-wide) feedback on assignments (prior to detailed individual feedback delivered in other ways).
Practical 1 (Sem.1) (Laboratory) Haemoglobin Concentration and Red Blood Cells
Estimating haemoglobin concentration in students' blood, using the HemoCue automatic measuring system, and (on another blood sample) by spectrophotometry. Counting RBCs with a haemocytometer. Quantitative analysis.
Practical 2 (Sem.1) (Computer-based) Data Analysis and Statistics
Using the SPSS package to perform basic exploratory analysis of (supplied) datasets, plus performing formal statistical tests (t, 1-way ANOVA, 2-way ANOVA, correlation) of several hypotheses
Practical 3 (Sem.1) (Laboratory). Gene expression and function
Studying genes using transgenic mice. Research a mutant phenotype identified in a forward genetic screen using genetic, anatomical, and molecular methods. Hypothesis testing, quantitative analysis, and data presentation.
Practical 4 (Sem.1) (Computer-based) Gene Data-mining
Following up forward genetic screens and high throughput molecular approaches. Extracting information on a specific gene from online resources, including systematic literature searches.
Practical 5 (Sem.2) (Computer-based) Simulation Models of Neuroendocrine Function
Obtaining data from a simulation model of the pituitary, formulating and testing hypotheses.
Practical 6 (Sem.2) (Computer-based) Bioinformatics
Extracting datasets (eg on gene expression and sequence variants) from online databases and using them to test specific quantitative hypotheses, with appropriate statistical controls.
Two tutorials in semester 1 will focus on training in reading and evaluating research papers. The papers used will be common to the whole class. Assessment of this learning outcome will then be tested in a December exam, based on a paper that will be provided to the class several weeks beforehand. Questions will address issues of experimental design, choice of techniques, hypotheses, statistical analysis.
Two tutorials in semester 2 will then cover papers/topics specific to the particular Programmes. The first of these (Tutorial 3) will lead to an assessed poster presentation, just prior to Innovative Learning Week. The exercise will centre on a topic within the Programme discipline: the tutor will select suitable papers and the student must then select one of these and produce an A3 poster which summarises the research topic, identifies the resulting research questions, and provides a plan for how these can be addressed in the next study in that particular field. Submission will be just prior to Innovative Learning Week, so the posters could then be displayed (and assessed) during ILW, and feedback provided. (Development of the skill in poster design & execution will come in the second part of Semester 2, with submission of a second poster, on the results from Practical 5 on Simulation Models of Neuroendocrine Function.)
Tutorial 4 will be an unassessed 'journal-club' style session, where groups of students present on key papers in their specific Honours discipline (following a preparatory teaching session specific to each Honours Programme). This is intended to help prepare students for giving assessed oral presentations in Year 4.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Biomedical Sciences 2 (BIME08007)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | none |
Information for Visiting Students
| Pre-requisites | N/A |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2017/18, Available to all students (SV1)
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Quota: None |
| Course Start |
Full Year |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
400
(
Lecture Hours 31,
Seminar/Tutorial Hours 8,
Supervised Practical/Workshop/Studio Hours 19,
Online Activities 4,
Feedback/Feedforward Hours 3,
Summative Assessment Hours 4,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 8,
Directed Learning and Independent Learning Hours
322 )
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| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
ICA:Exams 50:50 weighting
Exams:
December Exam: 25% of course
May Exam: 25% of course
ICA elements:
Report on Practical 1 (Haemoglobin and Red Blood Cells) 13%
Report on Practical 3 (Genes and Transgenic Mice) 13%
Poster 1 on a data analysis topic 6%
Poster 2 arising from Practical 5 (Simulations of Neuroendocrine Function) 13%
PeerWise engagement 5% |
| Feedback |
Feedback will be provided for the in-course assessments. A feedback session for the December exam will be held, and feedback for the May exam will be provided upon request. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | Biomedical Sciences 3 Semester 1 Degree Examination | 1:30 | | | Main Exam Diet S2 (April/May) | Biomedical Sciences 3 Semester 2 Degree Examination | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Students successfully completing the course should: * develop an understanding of broad themes within contemporary biomedical sciences as well as an understanding of key experimental approaches, plus an appreciation of when they can be used, their strengths and weaknesses, and the type of data they produce
- * acquire the ability to efficiently search for, understand, interpret, and evaluate primary biomedical research papers ('paper analysis') as well as the ability to frame scientific hypotheses and to design scientifically valid experiments to test them using appropriate experimental techniques
- * gain experience in collecting sets of data, analysing them, and testing formal hypotheses using statistical software programs
- * demonstrate technical skill in writing up concise and accurate practical reports and show understanding of the theory relating to the practicals
- * gain competence in the accurate and effective communication of biomedical concepts and data as well as experience of working as a team member
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Reading List
It is not easy to recommend books for this course as there is a diverse background in knowledge and previous courses taken. It is not necessary to buy any books at all.
If your basic mathematics is a bit rusty, you should first read the "Quantitative Skills Refresher Notes" posted on Learn.
The following may be useful for those who find maths and statistics a challenge:
Maths & Stats for the life and medical sciences, M. Harris et al., 2005, Scion Publishing Ltd. This is part of the "CatchUp" series.
Intuitive Biostatistics, H. Motulsky (2nd Edn) 2010, OUP.
A good book covering experimental design, statistical analysis, and much else besides is:
Asking Questions in Biology, C. Barnard, F.Gilbert, P.McGregor, (4th edn) 2011, Pearson.
(The 3rd edition, published in 2007, would also be fine.)
Check that the books are appropriate for your knowledge background by consulting a library copy before purchase. Note that you don¿t always need the most recent edition for statistics/maths books ¿ things have not moved on that much at this level. Second hand books will do.
A fairly comprehensive set of papers on statistics by Bland et al. can be found at:
http://www-users.york.ac.uk/~mb55/pubs/pbstnote.htm
Note that after following the link to a particular paper it is best to select the PDF version rather than viewing the default HTML version.
Also, the British Medical Journal has produced an excellent collection of material regarding experimental design and statistics: http://www.bmj.com/collections/statsbk/
Other papers will also be listed by individual lecturers. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | BMS3 |
Contacts
| Course organiser | Dr Thomas Pratt
Tel: (0131 6)51 1707
Email: t.pratt@ed.ac.uk |
Course secretary | Miss Alexandra Lowe
Tel: (0131 6)51 1513
Email: Alexandra.Lowe@ed.ac.uk |
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