Biomedical Sciences MBiol

Data skills are essential for a career in modern biology. Biological studies increasingly involve the generation of large or complex sets of data, and the ability to analyse data is a core component of a successful biologist’s skill set. Digital fluency is also required more widely outside biological research and a grounding in data analysis is in demand by a broad range of employers. Here you will learn the ability to visualise data, critically test hypotheses, and to interpret and present results.

The learning and teaching materials are delivered as an online set of resources (available through Canvas) coupled with computer-based practical workshops. The module will also introduce students to the powerful open access statistical software package, R.

The module will be assessed by a written data analysis report and an open-book exam

For any students studying off-campus – due to a placement in industry or studying at an overseas University – on-line drop-in sessions will be provided instead of the practical workshops.

This module will provide students with an understanding of the processes and methods required for the successful planning and delivery of research projects. It will also Introduce students to the latest, cutting edge technologies that will support their research field of interest. It will develop in students the transferrable communication skills that will enable them to disseminate their findings to both scientific and general audiences.

The internship is undertaken as placement in the summer period between years 3 and 4 of the MBiolSci programme. The student will work with a research group working on a specific project or a set of related  projects. A senior member of the project team will provide day today  supervision. The purpose of the  internship is for the student to understand the objectives of the research (in of light of the research strategy  of the group or institute), how the research is conducted and managed, and how the data are recorded and  analysed. The student will also learn how project design is influenced by factors such as, the business  strategy of the company, the research priority areas of the country, and ultimately the funding opportunities  available to support the research. ​

Data skills are essential for a career in modern biology. Biological studies increasingly involve the generation of large or complex sets of data, and the ability to analyse data is a core component of a successful biologist’s skill set. Digital fluency is also required more widely outside biological research and a grounding in data analysis is in demand by a broad range of employers. Here you will learn the ability to visualise data, critically test hypotheses, and to interpret and present results.

The learning and teaching materials are delivered as an online set of resources (available through Canvas) coupled with computer-based practical workshops. The module will also introduce students to the powerful open access statistical software package, R.

The module will be assessed by a written data analysis report and an open-book exam

For any students studying off-campus – due to a placement in industry or studying at an overseas University – on-line drop-in sessions will be provided instead of the practical workshops.

Bioinformatics is a key skill needed in many research settings. This module gives students a theoretical and technical grounding in a range of application areas including bioinformatics-related topics such as sequence analysis, phylogenetics, and the modelling of proteins, and others. While lectures are provided on core topics, there is a strong emphasis on practical exercises to demonstrate the application of common tools and data sources in these contexts. Teaching is delivered in the form of a weekly lecture and workshops. Students will be given guided reading and online activities to support their learning. The module will be assessed by two coursework assessments. The assessments will allow students to demonstrate their understanding of the tools used in workshops and interpretation of results.

This module is aimed at postgraduate students in the Biosciences, wishing to learn about methods for use in data-intensive research. The module provides a broad introduction to the use of Python coding for performing basic tasks in the biological sciences. The student will get practical experience in writing their own Python scripts for basic bioinformatics tasks, such as manipulating DNA, RNA and protein sequences, file input/output and working with other programs, such as BLAST. There is also an introduction to data visualisation using Python, and simple techniques used in data science.

Around 10 hours of lectures will be provided on core topics, with a strong emphasis on practical activity in workshops (totalling around 40 hours), allowing students to gain confidence in writing scripts for their own tasks. The module will be assessed by two short coding assignments, one team working coding assignment building a bioinformatics pipeline, and a data science mini-project.

Modern biotechnology and bioimaging applies novel tools and approaches to address today’s global challenges.
You will learn a variety of methods in mammalian cell biotechnology as well as imaging technologies that range from the microscopic scale to cellular and organ imaging in vivo. You will develop knowledge of a diversity of cell analysis techniques. Furthermore, the use of reporter genes for various types of imaging will be explained, including imaging technologies for cell analysis on the microscopic level as well as for cell imaging and functional analysis in animal models of disease.

The lectures will convey basic knowledge and include examples of applications from actual research publications, or the lecturer’s own research work, in equal measure. The students will have learning tutorials on critical appraisal of literature. There will also be a practical workshop on contemporary microscopy.
The module will be taught through a combination of lectures, workshops and practical exercises. There are two written assessments in this module.

This is a key module for students on the MSc Infection and Immunity Programme and might also be taken by other MSc, MBioSci and MRes students whose interests include infection and immunology. The module is topical in light of the pandemic and will address areas of research-connected infection biology teaching across areas of broad relevance to infectious disease, as well as to coronavirus. The module includes research connected lectures, workshops and structured discussions on selected texts as student-led topics. The content will focus on areas of infectious disease that support the programme and are relevant in that: (i) they are current/topical or (ii) they address fundamental questions of general importance. In addition, students will also be supported with key research and ideas in emerging infections and pandemics, showing how interconnected interconnected nature of health and disease through integrating aspects of biology and society. The module assessments are aimed at: 1) writing a report on a selected emerging pathogen that communicates the multifactorial considerations for researchers and society 2) presentation of a key factor that contributes to or affects disease emergence and the response by society or an agency. The factor focused on will be selected from workshops and student-led discussions

The module will address three main topics: hallmarks of cancer, cancer diagnosis and biomarkers, and cancer therapies & current challenges. These topics will be taught using various cancer models that have been selected based on the expertise at the University of Liverpool and to illustrate research, diagnostic and therapeutic problems.

This module will be taught by both scientists and clinicians who are experts in cancer research. The module will be taught through a combination of lectures, seminars, case-based learning tutorials and workshops. The lectures will convey basic knowledge and include examples of applications from actual research publications and the lecturer’s own research work.

The students will take part in case-based learning tutorials on critical appraisal of scientific seminars provided by cancer researchers. Workshops will cover literature search, referencing, and preparation of oral scientific presentations in preparation for the final assignment which is a conference style talk. A practical workshop will also cover tumour pathology and will train students in the identification and interpretation of tumour biopsies.

The module will be assessed via two assessments. The first assessment consists of a seminar report, based on a pre-recorded seminar provided by a cancer researcher. The final assessment will be an oral presentation, in which students will be required to give a conference-style lecture on an emerging cancer research topic related to one of the lectures and provide an abstract of their presentation.

Clinical trials are the key final step to translate medical research into the benefit of patients. The Liverpool Clinical Trials Unit is one of the largest in the UK, running a wide variety of surgical and oncology studies, that range from phase I studies to large, practice-changing phase III studies, as well as more novel trial designs. Since new therapies have become available in the treatment of cancer, the methods used in clinical trials have also evolved. The module in Cancer Clinical Trials is aimed at developing the knowledge and understanding of decisions affecting the design, delivery and assessment of clinical trials. This module will be taught by clinicians, researchers, statisticians and trial methodologists at the Liverpool Trials Unit, and cover fundamentals of clinical trials and designs, as well as the challenges that arise at each of these strategies. The module in Cancer Clinical Trials is ideal for current in-service health professionals looking to broaden their role in the design, management, analysis and reporting of clinical trials. It is also suited to those wishing to gain an understanding of clinical trials. The module will be taught through a combination of lectures, case-based learning tutorials and workshops, and assessed via a poster presentation and a written assessment, involving the design of a clinical trial.

This is a key module for students on the MSc Infection and Immunity Programme and might also be taken by other MSc, MBioSci and MRes students whose interests include infection and immunology. The module is topical in light of the pandemic and will address areas of research-connected immunology teaching across areas of broad relevance to infectious disease, as well as to coronavirus. The module includes research connected lectures, workshops and structured discussions on selected texts as student-led seminars. The content will focus on areas of immunology that support the programme and are relevant in that: (i) they are current/topical or (ii) they address fundamental questions of general importance. In addition, students will also be supported with key research and ideas in immune mechanisms and host defence, showing how research fields progress and our understanding of defence mechanisms develop. The module assessments are aimed at: 1) using graphics to show illustratively specific and complex immunological host-pathogen interactions and will aid skills in image design; and 2) poster presentation to demonstrate immunological interactions based on the student-led workshops on host defence and microbial evasion.

For students with interests in infection biology, the module will enhance skills and enable critically evaluation of key concepts, technologies and multifactorial considerations circumscribing diagnostics, therapeutics and vaccines. The module is topical in light of the current pandemic. The research-connected teaching will focus on prevention, limitation and treatment of infectious disease.
The module comprises lectures, workshops and seminars and uses active-learning delivery methods to ensure students can synthesise and evaluate relative merits, attributes, issues and applications of the topics. There are two coursework assessments in the module: 1) writing a report on a selected emerging pathogen that communicates the multifactorial considerations for researchers and society 2) presentation of a key factor that contributes to or affects disease emergence and the response by society or an agency. Materials will be included on the VLE to develop digital fluency and promote assimilation and appraisal of the module content.

With the advent of genomics and functional genomics, biology has become a quantitative data-rich discipline. This has created unprecedented opportunities in virtually every area of life sciences. With the right tools, it is now possible to address fundamentally important biological questions simply analysing already available datasets. This module is designed to prepare students for this very challenge. The module covers the most important aspects of computational biology. These range from the analysis of large datasets to infer biological mechanisms to the use of mathematical modelling to conceptualize and simulate complex biological phenomena. In addition to providing an intuitive overview of the basic theoretical principles, the module will focus on real life applications through multiple cases studies. Among these, students will learn how to identify drug targets and mechanisms of drug resistance and how to understand mathematical models of biological systems. They will then learn aspects of quantitative system pharmacology and physiologically based pharmacokinetic modelling pharmacokinetic/pharmacodynamic modelling.

The module will be taught through a combination of lectures, workshops and seminars. The module will be assessed via two written reports.

Proteomics and metabolomics represent powerful tools towards unbiased, quantitative and high-throughput analysis of biological systems. Rapid “omic” technological developments in the post‐genomic era have provided insights into protein structures, biosynthesis and interactions, as well as the complex metabolic processes that are of significant importance in biological and medical research. The aims of this course are to provide a comprehensive understanding of proteomic and metabolomic techniques and related data analysis, and to illustrate how they can be applied in fundamental biological research and industrial applications. The module will be taught by lectures and workshops. The module will be assessed via two a scientific reports.

Synthetic Biology and Biotechnology will provide an in-depth understanding of the grand challenges in biotechnological applications and the principles underlying synthetic biology and modern biotechnological techniques that are designed to sustainably address specific problems. The module also aims to teach tools and strategies being developed and applied in the rapidly expanding field of synthetic biology and train students with practical experience in green biotechnology.

The module will be taught through a combination of lectures and workshops. The lectures will convey basic knowledge or the lecturer’s own research work. The workshops will provide students with the opportunity to analyse relevant data relevant to the biotechnology field. The module will be assessed via a scientific report and a scientific review.

Bioinformatics is a key skill needed in many research settings. This module gives students a theoretical and technical grounding in a range of application areas including bioinformatics-related topics such as sequence analysis, phylogenetics, and the modelling of proteins, and others. While lectures are provided on core topics, there is a strong emphasis on practical exercises to demonstrate the application of common tools and data sources in these contexts. Teaching is delivered in the form of a weekly lecture and workshops. Students will be given guided reading and online activities to support their learning. The module will be assessed by two coursework assessments. The assessments will allow students to demonstrate their understanding of the tools used in workshops and interpretation of results.