Aims

The aims of the course are to:

• introduce the technologies that enable large-scale engineering of living systems.
• Examine the engineering constraints that limit synthetic biological systems.
• Develop an understanding of how design, mesurement, optimisation, and scale influence biological engineering.
• Expose students to industrial, infrastructural, and governance considerations in bioengineering.
• Equip engineers to collaborate productively across biology, biotechnology, and industry

Objectives

As specific objectives, by the end of the course students should be able to:

• understand the capabilities and limitations of modern genome sequencing and genome engineering technologies
• evaluate which genome engineering or evolutionary strategy is appropriate for a given engineering goal
• design synthetic genetic circuits while accounting for resource limitations, burden, evolution, and host-circuit interactions
• analyse the impact of stochasticity and noise on engineered biological systems
• propose experimental strategies to characterise and measure circuit performance at single-cell and population levels
• understand the principles of design-build-test-learn (DBTL) optimisation in biological systems
• appreciate the challenges of scaling engineered systems from laboratory to industrial bioprocessing
• understand the role of automation, open technologies, and infrastructure in engineering biology
• recognise biosafety, dual-use, and governance considerations in bioengineering

Content

The course is structured around the technological foundations and engineering challenges of Lectures 1-5: Writing, Editing, and Rewriting Genomes (GM)synthetic biology.

Lectures 1-5: Writing, Editing, and Rewriting Genomes (GM)

• Genome sequencing technologies and biological information
• Sequence alignment, assembly, and annotation
• CRISPR-based genome editing
• Prime editing and base editing
• Off-target detection technologies
• Genome-scale engineering

Lectures 6-10: Engineering Genetic Circuits Under Constraints (SB)

• From gene editing to synthetic genetic circuits
• Abstraction hierarchies in biological engineering
• Host-circuit interactions and resource competition
• Fitness costs, burden-driven feedback, and growth coupling
• Experimental characterisation: sequencing-based assays, imaging, microfluidics, and machine-learning approaches
• Design-Build-Test-Learn pipelines and optimisation strategies

Lectures 13-14: Cell-free and Artificial Cells (LDM)

• Cell-free circuit engineering and optimisation
• Artificial cells and engineering biology
• Cell-free phage engineering and infection on artificial cells

Lectures 13-14: Bioprocessing and Scale (LDM)

• Bioreactor design and scale-up principles
• Metabolic load and stability at scale
• Translating engineered circuits to manufacturing environments