Applied Biomedical Science MSc

Through all core modules you will develop the Graduate Attributes, five core skillsets that will help you launch a successful, fulfilling career. Find out more about the Graduate Attributes.

• Case studies in Drug Discovery and Development (15 credits)

  This module examines the "bench-to-bedside" process of drug discovery and development, tracing how breakthroughs in basic science progress through target identification, pre-clinical and clinical trials, and into clinical practice. The first half of the module focuses on malaria as a detailed case study, exploring the parasite’s life cycle, the natural history of the disease, and the development of antimalarial drugs, including clinical studies and strategies to address drug resistance. In the second half, students will explore a variety of translational research projects conducted by researchers at City St George’s, providing real-world insights into the complexities of drug development.

• Clinical Trials (15 credits)

  This module provides a comprehensive introduction to clinical trials, focusing on their principles, design, and management. Students will explore how randomized controlled trials are conducted and learn to assess their validity and reliability in real-world settings. Teaching includes lectures, expert-led sessions (internal and external to City St George's), practical activities, quizzes, and online courses, such as a research ethics and Good Clinical Practice (GCP) course, with certification upon completion. Students will complete practical sessions aligned with lecture topics, documenting their work in a notebook that contributes to the in-course assessment (ICA). The ICA also includes designing a mini clinical trial protocol, testing their understanding of trial design, ethics, implementation, analysis, and reporting. The module concludes with expert presentations on landmark clinical trials, bringing together key themes and insights.

• Data Analysis Skills (15 credits)

  Much of modern biomedical research generates vast amounts of data, including genomics, proteomics, and metabolomics data, as well as electronic health records and imaging data. Efficiently managing, processing, and interpreting these large and complex datasets require robust data analysis skills. This introductory module runs in term 1 and will provide students with an understanding of fundamental concepts and skills that will lay the foundation for more specialised, data-based modules in term 2 such as Computational Biology and Population Health Research.

• Personalised Medicine (15 credits)

  This module introduces the principles and applications of personalised medicine, focusing on how genomic technologies are transforming modern healthcare. Students will explore the integration of molecular information with clinical practice to advance screening, diagnosis, treatment, and disease classification. Real-world examples from oncology, infection, and other specialties will demonstrate the practical impact of these approaches. Personalised medicine extends beyond pharmacogenetics, examining how molecular profiling influences health outcomes. The module also addresses ethical and social considerations, including patient responses to genetic testing, genetic discrimination, and regulatory challenges, showcasing its role in shaping the future of medicine.

• Foundations of Population Health Research (15 credits)

  This module explores the health research spectrum, focusing on clinical trials and observational studies to understand "real-world" data. Students will learn principles, challenges, and solutions in epidemiology, public health, and study design, including cross-sectional, cohort, case-control, and longitudinal studies. Key topics include measures of disease-risk associations, statistical methods to address bias and confounding, and an introduction to Bayesian inference. The R programming language will be used for data analysis, emphasizing when and why specific statistical techniques are applied. At the end of the course, students will gain the essential skills to analyse population data and inform evidence-based public health policies.

• Laboratory Research Skills (15 credits)

  The current module complements the Data Analysis Skills and Hot Topics in Biomedical Science modules by providing students with training in basic laboratory skills commonly required for bench-based biomedical research. The module will run over the first two terms.

• Hot Topics in Biomedical Science (15 Credits)

  This module focuses on two key skills for biomedical scientists: analysing scientific literature and presenting research findings. Students will practice reading, evaluating, and presenting high-impact papers in contemporary biomedical science, with an emphasis on identifying significant publications and understanding advanced scientific techniques. Through peer discussions and group presentations, students will enhance their critical thinking, teamwork, and communication abilities while also gaining exposure to professional standards in scientific writing and data documentation. Introductory lectures will cover paper selection and evaluation methods, followed by group discussions based on self-guided analysis. Students will actively select papers for discussion, with tutor feedback provided throughout the process.

• Computational Biology (15 credits)

  Since the first human genome was sequenced in 2003, the biomedical field has witnessed a dramatic surge in DNA sequence data, functional genomics, and epidemiological information. This explosion was spurred by new sequencing technologies, innovative methods for probing gene function, and advanced tools for managing and analysing substantial datasets. In this module, you’ll explore how these developments have shaped “systems biology,” an approach that tackles complex biological and medical challenges holistically. With the demand for scientists capable of analysing big data growing in both industry and academia, this course equips you with the expertise needed to thrive in a rapidly evolving research landscape.

• Research Project (60 credits)

  The supervised research project constitutes a central learning activity by providing immersive, work-based training in translational biomedical science. A research project involves choosing a subject, formulating a specific research question or aim, devising a research strategy to address this question, performing the research and analysing the resulting data. Project background, experimental procedures, results and discussion are written up as a 10,000-word dissertation and presented orally to an audience with the aid of a poster or PowerPoint presentation.