MTE-40055 - Disease Modelling & Therapy for Regenerative Medicine
Coordinator: Vinoj George Tel: +44 1782 674383
Lecture Time: See Timetable...
Level: Level 7
Credits: 15
Study Hours: 150
School Office: 01782 734414

Programme/Approved Electives for 2025/26

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2025/26

The core module ‘Disease Modelling and Therapy’ draws upon the current trends in Regenerative Medicine research by applying translational cell biology for personalised medicine through disease modelling and therapy. The module aims to skill students for a growing market, in response to the growing demand for novel regenerative therapies, fuelled by advances in cell biology, biomaterials, tissue engineering and gene-editing technology. This is a fast-paced area of medicine in the 21st century, that through translational learning and entrepreneurial skills which this module offers, it exposes students to creative ideas for addressing current healthcare needs in the society. The module will combine expertise of academics and stakeholders for training next generation of science entrepreneurs for Regenerative Medicine.

Aims
The module aims to enhance students evidence-based knowledge and to apply their entrepreneurial skills in the fields of disease modelling and therapy for regenerative medicine.

Intended Learning Outcomes

Build and develop entrepreneurial skills for the field of regenerative medicine by participating in module-associated entrepreneurial programme.: 2
Apply critical thinking through a business plan for a regenerative medicine application by bringing logical connections of concepts, ideas and skills relevant for this module.: 2
Demonstrate understanding of in silico gene engineering for disease modelling through practical training and critically evaluate limitation of approach for the chosen disease.: 1
Evaluate the strategies required to develop gene, cell and drug therapy through exemplars.: 1
Appraise concepts and advances in disease modelling for regenerative medicine.: 1
Assess and critique developments and regulations for disease modelling and therapy for regenerative medicine.: 1,2

Study hours

Direct contact hours = 60h which consist of
Lectures = 15h
Tutorials = 10h
Practical = 5h
Entrepreneurship tutorial sessions (with LSC-40145) = 30h
Independent study hours (F4) = 90h which consist of
Directed reading to support lectures/tutorials (including private study) = 40h
Report preparation = 25h
Business Plan preparation = 25h

School Rules

Students taking this module as optional module, should also take associated module MTE-40028 (Stem Cells) as a supplementary optional module or show proof of knowledge in stem cells at Level 7.

Description of Module Assessment

1: Report weighted 70%
Report on disease modelling and therapy in regenerative medicine
Report (3000 words) with indicative content to capture (a) Disease modelling - where students have to apply the practical skills (in silico modelling for genetic engineering) to a genetic mutation-linked disease of choice and describe aspects of modelling that disease using stem cells or cancer cells. (b) Therapy - where students will critically appraise therapy for the same disease, with focus on the advantages, limitations and regulations for cell, gene or drug therapy for that disease. Report will need to reflect learning from practicals, relevant lecture and tutorials.

2: Presentation weighted 30%
Business Plan and Pitch
Group work; Business Plan (10%) and Business pitch presentation (20%). Ideas will be developed by the team but individual's contribution will be assessed. For the Business plan, each team will produce an executive summary (2 A4 pages and a Gantt chart) using a template provided for the mock panel, in advance of their business pitch presentation. For the Business Pitch presentation to the mock panel (maximum 20 minutes talk by a team), assessment will be based on performance of each team member's contribution to the talk and their response to a random set of predesigned questions.