Biotechnology MSc

  • Programme duration: Full-time: 12 months   Part-time: 24 months
  • Programme start: September 2023
  • Entry requirements: Normally, at least a 2.1 Honours degree in a Biological Sciences subject or equivalent. Candidates must have a scientific background acceptable to the Programme Director.
Advanced Biological Sciences mres

Module details

Compulsory modules

Introduction to Research (LIFE702)
LevelM
Credit level30
SemesterWhole Session
Exam:Coursework weighting0:100
Aims

To prepare students for their MSc research project module, this module will give students the opportunity to gain the knowledge and the skills specific to their project area.

Learning Outcomes

(LO1) Demonstrate a systematic knowledge and critical understanding of the essential concepts in their chosen field of study;

(LO2) Demonstrate competency in a range of skills necessary to work in a research laboratory and to complete a successful research project;

(LO3) Develop and present a plan for a research project in the form of a grant proposal.

(LO4) Critically review scientific literature

(S1) Technical writing

(S2) Independence

(S3) Time management

(S4) Reflective learning

MSc Research Project (LIFE703)
LevelM
Credit level60
SemesterSummer (June-September)
Exam:Coursework weighting0:100
Aims

To give students the opportunity to work in a guided but independent fashion on an area of research related to their programme of study, making use of the knowledge and skills acquired elsewhere in the programme.

Learning Outcomes

(LO1) Plan a piece of original scientific research;

(LO2) Design and perform a coherent set of investigations to test a hypothesis;

(LO3) Present work in the form of an oral presentation to a scientific audience and at an oral examination;

(LO4) Write final report in the form of a manuscript that would be suitable for submission as a scientific publication.

(S1) Research management: developing a research strategy, project planning and delivery, risk management, formulating questions, selecting literature, using primary / secondary / diverse sources, collecting and using data, applying research methods, applying ethics.

(S2) Self-management: readiness to accept responsibility (that is, leadership), flexibility, resilience, self-starting, initiative, integrity, willingness to take risks, appropriate assertiveness, time management, readiness to improve own performance based on feedback / reflective learning;

(S3) Team (group) working: respecting others, co-operating, negotiating / persuading, awareness of interdependence with others.

Advanced Statistics for Biological Research (LIFE707)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting50:50
Aims

To enable students to analyse biological data by:

Choice of appropriate statistical approaches to test hypotheses;

Critical understanding of the use of a range of advanced statistical tests for appropriate analysis and  model fitting of a range of biological datasets;

Using the software package, R;

Synthesizing information, summarising statistical findings, and using hypothesis testing to critically review evidence from experimental data to support conclusions.

Learning Outcomes

(LO1) Illustrate and explain the methods of hypothesis testing

(LO2) Critically evaluate experimental design(s) used in data collection and then apply the appropriate statistical test(s).

(LO3) Design data collection methods appropriate to rigorous data analysis

(LO4) Synthesise information from data analysis, test statistical hypotheses and critically review evidence to support conclusions.

(S1) Problem solving skills

(S2) Numeracy

(S3) IT skills

(S4) Communication skills

(S5) Organisational skills

(S6) Lifelong learning skills

Informatics for Life Sciences (LIFE721)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

To provide a broad overview of the use of informatics in the biological sciences, to give students a theoretical and technical grounding in a range of application areas including bioinformatics-related topics.

Learning Outcomes

(LO1) Critically evaluate and utilise core techniques in bioinformatics to support their research

(LO2) Critically analyse, evaluate data analyses results and interpret them within the relevant research context.

(LO3) Evaluate research methods in bioinformatics to solve biological problems

(LO4) Critically evaluate the literature and apply knowledge when analysing data.

(S1) Learning skills online studying and learning effectively in technology-rich environments, formal and informal.

(S2) Problem solving/critical thinking/creativity analysing facts and situations and applying creative thinking to develop appropriate solutions.

(S3) Communication and collaboration online participating in digital networks for learning and research.

Cellular Biotechnology and Biological Imaging (LIFE749)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

To enhance the core skills acquired , including both scientific (presentational and communication) and employability skills, and to provide advice on careers and career development in imaging technologies for cell analysis on the microscopic level as well as for cell imaging and functional analysis in animal models.

To enable students to evaluate the latest scientific literature and technologies in imaging technologies for cell analysis on the microscopic level as well as for cell imaging and functional analysis in animal models of disease and topical issues of particular concern to biotechnologist.

To enhance problem solving skills by data analysis exercises in relation to experimental methods in imaging and cell biotechnology, and develop a deeper understanding of topical issues in the subject.

Learning Outcomes

(LO1) Compare various mammalian cell culture and cell analysis techniques.

(LO2) Critically review the molecular details of various technologies for the genetic manipulation of mammalian cells .

(LO3) Critically evaluate a broad range of imaging techniques and modalities to analyse cellular features and cell behaviour at different scales, from microscopic analysis to imaging in animal models of disease.

(LO4) Appraise the translational impact of cellular biotechnology approaches and justify their application in medical research.

(S1) Scientific Communication

(S2) Scientific Technical Ability

(S3) Digital Fluency

(S4) Critical Thinking

Computational Biology (LIFE752)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting0:100
Aims

The aims of this module are:
1. To support students develop a comprehensive understanding of complex computational approaches
2. Enable students to identify the correct computational approach to address a wide range of biological questions.

Learning Outcomes

(LO1) Justify the integration of different tools and approaches for the description and analysis of complex data

(LO2) Assess the basis for quantitative and computational techniques

(LO3) Become proficient using computational analysis approaches to model the interaction of cellular molecular components in a wide range of biological systems

(LO4) Assess the application of complex computational approaches for the discovery, development and optimisation of therapies

(S1) Written communication

(S2) Think critically

(S3) Problem solving

(S4) Biological Interpretation

(S5) IT skills

Proteomics Metabolomics and Data Analysis (LIFE754)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting0:100
Aims

To illustrate the value of proteomics and metabolomics towards unbiased, quantitative and high-throughput analysis of biological systems. To offer demonstrations in the design and synthesis of proteomic and metabolomics experiments, including practical demonstrations within the metabolomics and proteomics facilities.

Learning Outcomes

(LO1) Appraise proteomic and metabolome techniques.

(LO2) Evaluate analytical methods and design proteomics or metabolomics experiments to address a given biological problem.

(LO3) Critically appraise data analysis tools and strategies and interpret the experimental data in the biological context

(S1) Problem Solving Skills

(S2) Communication Skills – Professional Report Writing

(S3) Coding and Statistical Analysis

Synthetic Biology and Biotechnology (LIFE756)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting0:100
Aims

The module aims to provide students with an in-depth knowledge of the grand challenges in biotechnological applications and the primary design principles of synthetic biology. The module also aims to teach tools and strategies being developed and applied in the rapidly expanding field of synthetic biology and train students in practical
experience by demonstrating the University’s research facilities and providing research and industry-based projects encompassing green biotechnology.

Learning Outcomes

(LO1) Critically appraise concept and research evidence relating to current grand challenges and industrial needs, and how biotechnological methods are developed and exploited to address the problems

(LO2) Critically evaluate key theoretical and practical knowledge in a diverse range of synthetic biology and biotechnology techniques and applications

(LO3) Design and evaluate appropriate approaches and processes for synthetic biology and biotechnology techniques

(LO4) Evaluate the public concerns and ethical issues in the fields of synthetic biology and biotechnology

(S1) Scientific communication

(S2) Analyse, synthesize, evaluate and interpret information from a variety of sources in a critical manner

(S3) Critical thinking

(S4) Team work


LIFE749 Cellular Biotechnology and Biological Imaging

Level: M
Credit Level: 15
Semester: Semester 1

Modern biotechnology and bioimaging applies novel tools and approaches to address today's global challenges. You'll 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 module will be taught through a combination of lectures, workshops and practical exercises. The module will be assessed via coursework.

LIFE756 Synthetic Biology and Biotechnology 

Level: M
Credit Level: 15
Semester: Semester 2

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 you in practical experience by providing research and industry-based projects encompassing 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 you with the opportunity to analyse relevant data relevant to the biotechnology field. The module will be assessed via coursework.

LIFE754 Proteomics, Metabolomics and Data Analysis 

Level: M
Credit Level: 15
Semester: Semester 2

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 coursework.

LIFE752 Computational Biology 

Level: M
Credit Level: 15
Semester: Semester 2

This module introduces you to a range of computational methodologies to analyse biological data, to make new predictions and support the understanding of biological mechanisms. The module content will include relevant methods of bioinformatics and computational biology ranging from the molecular, cellular, tissue, organism and population level. The overall strategy will support a comprehensive understanding of computational applications for data analysis and simulation providing multiple examples of relevant applications in different fields. Description and understanding of computational applications will be focused on data driven modelling for learning complex biological system with the analysis of multiple cases studies. Methodologies such as quantitative system pharmacology, physiologically based pharmacokinetic modelling pharmacokinetic/pharmacodynamic modelling, strategies for therapy stratification will be described with the overall objective of providing an integrated approach for the application of computational tools in drug development and therapy optimization. The module will be taught through a combination of lectures, workshops and seminars. The module will be assessed via coursework.