MD9A5-10 Mathematical Modelling of Biomedical Systems
Introductory description
Module aims
Mathematical models play a central role in understanding mechanisms underpinning a wide range of biological systems. These models are used for the analysis and interpretation of large and varied biological data, but also for the prediction of the dynamic behaviour of these biological processes.
The module aims to:
- Equip students with mathematical and computational methods/tools (e.g.; MATLAB and/or similar software) for analysing, modelling and
predicting dynamic systems essentially related to biochemical problems. - To unable students to develop their problem-solving skills in particular areas of biomedical research, working in group.
- Equip students with analytical skills by developing biomedical systems models from experimental data.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Students will learn to translate real-world problems in biomedical sciences into a mathematical framework and to derive a model from experimental data using different methods. This will be supported by key elements of the mathematical background material. Students will learn fundamental mathematical methods for solving single and multivariable ordinary and partial differential equations. They will decipher the use of numerical methods to solve particular biological problems. Students will also learn to solve problem using computational tools. Cognitive skills will be gained through computer sessions where material delivered by lectures will be directly applied and during self-study.
Learning outcomes
By the end of the module, students should be able to:
- Translate a real-world problem in biomedical sciences into a mathematical context.
- Critically appraise the current practice in application of numerical methods to a wide range of problems at the interface of life and physical sciences
- Choose appropriate mathematical methods to interpret data
- Efficiently solve problems using computational tools.
Indicative reading list
- R.L. Burden, Numerical Analysis, Brooks/Cole, 2001
- J.H. Mathews, Numerical Methods Using Matlab, Pearson, 2004
- P. Fall, Computational Cell Biology, Springer, 2002
Subject specific skills
Sound understanding of subject
Critically evaluate
Reflection
Transferable skills
Numeracy
Thinking and problem solving
written communication
oral communication
Teamwork
Organisation & time management
Use of tools and technology
Commercial awareness
Independence and initiative
Adaptability/Flexibility
Study time
| Type | Required |
|---|---|
| Lectures | 10 sessions of 2 hours (33%) |
| Tutorials | 10 sessions of 2 hours (33%) |
| Private study | 20 hours (33%) |
| Total | 60 hours |
Private study description
Self-directed studies: 20 hours that include preparation for next session (e.g.; formative problem-solving exercises)
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Problem-solving assignment 1 | 50% | 20 hours | Yes (extension) |
|
To develop students’ analytical skills to solve biomedical problems using computational tools and /or model experimental data using appropriate mathematical methods. |
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| Problem-solving assignment 2 | 50% | 20 hours | Yes (extension) |
|
To demonstrate advanced analytical skills in solving biomedical problems using computational tools and /or model experimental data using appropriate mathematical methods. |
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Feedback on assessment
Staff teaching on the module will mark the coursework. Marks and individualized feedback on each coursework will be moderated by the Module Lead, in line with WMS assessment criteria (including submission for plagiarism). Feedback will be available to students on request throughout the module. Any student failing an element of assessment will be offered an appointment with the module lead for face-to-face feedback.
Courses
This module is Core optional for:
- Year 1 of TMDA-B91Z Postgraduate Taught Interdisciplinary Biomedical Research
This module is Option list A for:
- Year 4 of UMDA-CF10 Undergraduate Integrated Natural Sciences (MSci)