Apply through UCAS
- UCAS course code
- B9R9
- UCAS institution code
- M20
Bachelor of Science (BSc)
BSc Biomedical Sciences with a Modern Language
Develop your language skills while you study the biomedical sciences to enhance your employability.
Duration: 48 Months. [Full-Time]
Year of entry: 2025
UCAS course code: B9R9 / Institution code: M20
- Key features:
Study abroad
Study with a language
Accredited course
- Typical A-level offer: AAA-AAB including specific subjects
- Typical contextual A-level offer: AAB-ABB including specific subjects
- Refugee/care-experienced offer: ABB including specific subjects
- Typical International Baccalaureate offer: 36-35 points overall with 6, 6, 6 to 6, 6, 5 at HL, including specific requirements
Fees and funding
Fees
Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £34,500 per annum. For general information please see the undergraduate finance pages.
Tuition fees are considerably lower for your placement year. Please see the fees page
for full details.
Additional expenses
Tuition fees are considerably lower for your placement year. Please see the fees pages
for full details.
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Scholarships/sponsorships
Students participating in placements outside the UK may be able to apply for funding from the UK's Turing scheme
depending on eligibility. Priority will be given to students from low income households.
Course unit details:
Computational Approaches to Biology
| Unit code | BIOL33021 |
|---|---|
| Credit rating | 10 |
| Unit level | Level 3 |
| Teaching period(s) | Semester 1 |
| Available as a free choice unit? | No |
Overview
Computational methods are increasingly used in all areas of biology in order to better understand complex living systems and develop models that generate testable predictions. This unit introduces a range of computational techniques, including differential equations, machine-learning, network and constraint-based analyses, which are used for a wide range of biomedical and biotechnological applications, from understanding how intracellular signalling pathways are disrupted in diseases to redesigning organisms by metabolic engineering.
Pre/co-requisites
| Unit title | Unit code | Requirement type | Description |
|---|---|---|---|
| MSci Project Literature Review and Research Proposal | BIOL33000 | Co-Requisite | Recommended |
| MSci Bioinformatics Tools and Resources | BIOL33011 | Co-Requisite | Recommended |
A-Level Mathematics required.
A limited number of places will be available for BSc students, the following rules apply:
- The student MUST have A’Level Maths (or its international equivalent - but AS or GCSE Maths are not sufficient).
- The student cannot join the unit in the 2-week grace period at the start of the semester, they would have to be pre-accepted in early September.
- The student must first discuss with their PD whether taking the unit is appropriate for them or not.
- Where demand exceeds capacity, acceptance onto the unit will be based on the student’s Year-2 average mark.
Aims
This unit aims to introduce students to a wide range of computational methods and tools required to carry out interdisciplinary research in the biological sciences.
Syllabus
The unit will start with an introduction to the Python programming language. Students will be introduced to the Jupyter Notebook system, a widely used online application allowing the development of code for data analysis and numerical simulation. The core of the unit will be structured along four main sections, each covering a particular set of techniques and applications: Section 1: Dimensionality reduction and clustering • Dimensionality reduction: Principal Component Analysis (PCA) and interactive plotting with applications to visualising single-cell expression data • Clustering: hierarchical, k-means and mixture model clustering • Non-linear dimensionality reduction methods: GPLVM and t-SNE for non-linear dimensionality reduction and visualisation of single-cell data Section 2: Models of large cellular systems • Network reconstruction and analysis: protein-protein interaction networks, metrics for network analysis, integration of high-throughput biological data. • Logical modelling: Boolean models, logical steady state analysis, applications to cancer systems. • Constraint-based modelling of metabolic networks. Section 3: Dynamic models • Introduction to differential equations-based modelling • Modelling gene regulatory pathways: gene transcription and cellular signalling pathways Section 4: Next-generation sequencing Next-generation sequencing I: from laboratory experiments to computational analysis. Next-generation sequencing II: RNA-seq workflow.
Teaching and learning methods
The unit will be delivered as a succession of lectures (1 hour) and practical sessions (2 hours), where each lecture will introduce the theory behind a method/tool, and students will apply the method/tool to solve a particular biological problem in the practical. Students will be assessed by completing three written assessments, one for each of the main sections of the unit. These assessments will consist of a series of short questions and mini-project reports, some of which will require some computer code to be written.
Knowledge and understanding
Students should: • Understand essential mathematical concepts required for biological research. • Understand and apply differential equations modelling of intracellular systems. • Understand and apply constraint-based modelling of metabolic systems. • Understand and apply network analysis and logical modelling of molecular systems. • Understand and apply data analysis and machine learning methods. • Understand the applications and limitations of different modelling techniques and tools. • Understand and use RNA-sequencing data analysis methods.
Intellectual skills
Students should:
- Develop problem-solving skills
- Construct models and design experiments to test biological hypotheses.
Practical skills
Students should:
- Use the Python language and develop models using the Jupyter Notebook system.
- Construct models of signalling, regulatory and metabolic systems.
- Infer computational models from biological data
Transferable skills and personal qualities
Students should:
- Develop computational skills.
- Develop report writing skills.
- Learn to communicate computational results.
Employability skills
- Analytical skills
- Critical appraisal of research papers.
- Project management
- To be able to meet deadlines for written and experimental work.
- Problem solving
- Planning of modelling strategies to test a specific hypothesis.
- Research
- Learning computational techniques and applying these to your planned goals.
- Written communication
- Scientific writing skills preparing a report.
Assessment methods
Three online modules worth 30, 40 and 30% respectively .
Feedback methods
Verbal feedback will be communicated during the practical sessions.
Written feedback will be communicated through annotated comments for each online assessment.
Recommended reading
Specific material will be provided with each lecture.
Study hours
| Scheduled activity hours | |
|---|---|
| Lectures | 12 |
| Practical classes & workshops | 24 |
| Independent study hours | |
|---|---|
| Independent study | 64 |
Teaching staff
| Staff member | Role |
|---|---|
| Jean Marc Schwartz | Unit coordinator |