Module Catalogue 2024/25

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

To describe the application of advanced engineering principles to the engineering of biological systems.
To educate students about the different computational modelling strategies that can be applied to the synthetic biology life-cycle
To cover theoretical computational approaches underlying the concepts and techniques of synthetic biology.
To train students in the more advanced approaches to computational design, build and testing strategies

Synthetic biology is the application of engineering principles to the design and implementation of biological systems. Synthetic biology is paradigm shift in biology allowing biological systems to be built at a genome scale from parts derived from a diverse range of organisms or even completely synthetic devices. The field has potential applications in areas as diverse as biotechnology, bioremediation, agriculture and medicine. Computational design approaches are required because these systems are complex, stochastic and nonlinear.

This module provides an a more advanced understanding of the theoretical underpinnings of synthetic biology and experience in practically applying that theory. The module builds on the basic concepts of the application of the synthetic biology design, build, test, learn (DBTL) cycle introduced to the students in CSC8327 by providing a stronger emphasis on both computation and the interplay with practical aspects of engineering biological systems.
It introduces a number of many different tools and their usage, and touches on analysis algorithms behind some of them, focusing on the practical aspects of design. This module also incorporates a significant laboratory-based component since synthetic biology systems require implementation and testing in addition to design.

Outline Of Syllabus

Engineering biological systems.
More advanced aspects of the ethical, legal and social implications of synthetic biology.


Advanced aspects of the synthetic biology life cycle:

- Computational approaches to biological systems specification
- Model based design and simulation
- Modelling for system verification.
- Advanced strategies for part, device, system and genome implementation.
- An introduction to biodesign automation
- Advanced omics techniques for part, device and system characterisation.
- Feedback from systems level characterisation to design including model-based reasoning
- Advanced research-based use cases in synthetic biology

Intended Knowledge Outcomes

To be able to describe and discuss:

The application of engineering and model-based design in synthetic biology
The basic algorithms underpinning tools for biological systems design, testing and iterative learning
The technology for studies in modern post-genomic biology and the data that is generated by such studies in their application to synthetic biology testing.
Advanced research-based use cases in synthetic biology

Intended Skill Outcomes

To be able to:

Design and model new biological systems
Plan implementation and build strategies for engineering biological systems
Identify appropriate testing strategies
Employ standardised approaches to sharing design and data arising from the DBTL cycle

Teaching Rationale And Relationship

Lectures will be used to introduce the learning material and for demonstrating the key concepts by example. Students are expected to follow-up lectures within a few days by re-reading and annotating lecture notes to aid deep learning.

Tutorials will be used to emphasise the learning material and its application to the solution of problems and exercises set as coursework, during which students will analyse problems as individuals and in teams.

This is a very practical subject, and it is important that the learning materials are supported by hands-on opportunities provided by practical classes. Students are expected to spend time on coursework outside timetabled practicals.

Students aiming for 1st class marks are expected to widen their knowledge beyond the content of lecture notes through background reading.

Assessment Rationale And Relationship

Lectures will be used to introduce the learning material and for demonstrating the key concepts by example. Students are expected to follow-up lectures within a few days by re-reading and annotating lecture notes to aid deep learning.

Online discussion, both using canvas and in drop-in surgeries will be used to emphasise the learning material and its application to the solution of the coursework.

General Notes

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