Advanced Bioengineering

This course explores the state-of-the-art in modern bioengineering, where automated workflows, high throughput experimentation, and statistical analysis come together to accelerate bioprocess developments. These technologies enable the optimisation of microbial strains for industrial conditions, often resulting in significantly improved yields and titres compared to natural systems. The throughput and power of bioengineering have accelerated with the help of recent advances in lab automation and computational tools. Accordingly, generated data sets can be vast and span large design spaces. In this course, we emphasise how biological systems can be tested at scale and how to use these datasets to guide new experiments and strain design. This includes how to design high-throughput screening (HTS) for biotechnologically-relevant parameters and strain optimisation. You will learn how to efficiently analyse these datasets generated by HTS using statistical models. To highlight the use of these methods in current research, students will review and assess recent publications. After the introductory lectures, the new concepts will be practiced in dry labs using simulation environments. Eventually this newly gained knowledge can be used to design hypothetical bioengineering interventions and a design-build-test-learn (DBTL) workflow aimed at improving microbial strains for better bioprocesses. The results of this analysis will be presented with reports and conference-style poster presentations.

• Analyse experimental datasets with modelling approaches

• Design strategies to test biotechnological relevant parameters

• Integrate bioengineering and statistics to design new, optimised microbial strains

• Explain key elements of the strain design process for biotechnology

• Explain different experimental screening methodologies commonly applied in strain engineering and optimisation

• Implement Design of Experiment approaches

• Critically review scientific literature

• ? (25%) Students produce a poster detailing their project work that will be presented at a course symposium. Posters will be assessed by examiners and peers following a rubric. Students can resubmit this work during resit periods or the following year.
• ? (20%) Students will be assessed by examiners and asked to explain their project idea and defend related questions. Oral presentation will take place during course symposium. Students can resubmit this work during resit periods or the following year.
• ? (10%) Students will be asked to provide peer-reviewed feedback to others in their project team following a rubric.
• ? (45%) Students will write up their project research in the form of a scientific paper. This will be assessed by examiners following a rubric. Students can resubmit this work during resit periods.

Basic computer programming (as in, e.g., INF22306), mathematical skills (as taught in, e.g., BCT20306 or SSB30806), and statistics (as in, e.g., MAT20306) are required. Basic molecular biology knowledge is also required. It is advantageous to have followed one of the following: Introduction to Systems & Synthetic Biology (SSB32806), Applied Molecular Microbiology (MIB30806), Molecular Systems Biology (SSB30306) or Metabolic Engineering of Industrial Microorganisms (BPE34306) before the course, but these are not required.

• All material will be made available to students during the course and via the course Brightspace page.