Automated Learning and Testing with Synthetic Biology

The Design-Build-Test-Learn (DBTL) cycle is an efficient workflow in engineering studies. For example, DBTL is commonly followed in biotechnology to improve bioprocesses, under industrial conditions with the help of synthetic biology and cell factories. This engineered system has, e.g., improved product yields and titres compared to natural systems. Recent developments in synthetic biology, as well as automation and robotics, have drastically increased the throughput of DBTL cycles and associated generated data. In this course the emphasis lies on the automated test and learn phases. This includes how to design high-throughput screening (HTS) for (biotechnologically) relevant parameters and demonstration of available technologies for this, including an introduction to the use of laboratory robots. You will also learn how to efficiently analyse datasets generated by HTS using hybrid modelling approaches (including statistical and metabolic/mathematical models). To highlight the use of these methods in current research, students will review and assess recent publications. After introduction, the new concepts will be practiced in dry labs using simulation environments. Eventually this newly gained knowledge can be used to design a DBTL strategy to improve a specific bioprocess. The results of this analysis will be presented in a scientific paper format, and with conference-style poster presentations.

After successful completion of this course students are expected to be able to:

• Analyse large experimental datasets with modelling approaches
• Integrate automated test and learn procedures in DBTL strategies
• Explain key elements of DBTL (design-build-test-learn) cycles in biotechnology
• Design high-throughput screening strategies to test biotechnological relevant parameters
• Explain different experimental screening methodologies commonly applied in DBTL-based strain engineering and optimisation
• Implement Design of Experiment approaches
• Assess and peer-review scientific research

Assumed Knowledge:

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.