Postgraduate

Modules

Modules

CSC8331 : Bio-design Automation

Semesters
Semester 2 Credit Value: 10
ECTS Credits: 5.0

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, focussing 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

Automating the Engineering biological system – overview
Joining up the DBTL
Parts assembly strategies
Automating design using standards and modelling
Combinatorial planning and assembly
Process build automation: including robotics, liquid handling, microfluidics,
Software workflows
Equipment interfacing
Standard approaches to data representation and provenance
Appropriate aspects of machine intelligence
Statistical learning and reasoning over test data
Advanced use case and literature reviews

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion115:0015:00Group practical exercise
Guided Independent StudyAssessment preparation and completion110:0010:00Short report on an aspect of synthetic biology automation
Scheduled Learning And Teaching ActivitiesLecture121:0012:00lectures
Scheduled Learning And Teaching ActivitiesPractical103:0030:00practicals
Scheduled Learning And Teaching ActivitiesSmall group teaching241:0024:00Guidance with practical classes and tutorials
Guided Independent StudyIndependent study19:009:00Background reading
Total100:00
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 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 a large amount of the module is dedicated to a practical exercise. 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 practical classes.

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

Assessment Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Essay1M20Low stakes summative assessment: Short report on an aspect of synthetic biology automation. 500 words.
Practical/lab report1M80Group Practical Exercise -A practical report on the design and automation of synthetic biology exercise. Max 2,000 words
Assessment Rationale And Relationship

The individual, low stakes summative, report will assess the students’ ability to apply the concepts of a range of a synthetic biology automation strategies. The summative report on the group practical exercise will assess the students’ ability to apply the concepts learned to the development of new automation strategies. This group practical exercise hives a group mark.

Students will analyse problems as individuals and in teams.

Reading Lists

Timetable