Computer-aided cell factory design

• Describe the role of the cell factory design stage and how it interfaces with the other elements of the cell factory engineering cycle (build, test, learn).
• Analyze cell factories collaboratively using Python code and employ a modern data science approach to reproducible computational analyses.
• Describe the concepts behind metabolic flux analysis and discuss advantages and disadvantages with different methods.
• Describe the principles of omics (transcriptome, proteome, and metabolome, fluxome) analysis and integration with models and how data from these analyses can be applied in cell factory engineering.
• Apply genome-scale models in design of optimal cell factories.
• Construct genome-scale metabolic models to predict a cell factory’s performance.
• Describe how machine learning can be used in cell factory design with some examples.
• Design cell factory optimization strategies based on metabolic knowledge, quantitative physiology and omics data.

The course gives an overview of the different elements of cell factory design with a number of examples on how directed metabolic modifications have been introduced with the aim of obtaining improved strains for production of different compounds in the bioindustry. There is especially focus on the different tools of cell factory design, and the course covers the following topics: Introduction to cell factory design. Overview of biochemical pathways. Regulation of pathways. Examples of cell factory design. Design of experiments for characterization of strains. Metabolic flux analysis: Theory and applications.. Methods for evaluation of omics data. Omics data integration (metabolomics, transcriptomics, proteomics, …) with computational models for the cell factory. Different case stories are used to illustrate the topics. Students will work independently with examples and with a group task, which will be presented both orally and in a written report. In the group task, students are introduced to a real-life cell factory design case and are supposed to suggest innovative strain improvement strategies, utilizing the taught techniques.

27460/22111/22110/22101, Basic programming skills in Python, R or similar (loops, conditionals, basic data types like lists, dictionaries etc.) are critical for this course.

Lectures, group exercises, seminars, and problem solving

The course is aimed at biotechnology students, but the course can also be taken by students with a chemical engineering background, who wishes to get an introduction to aspects of bioinformatics and optimization of biocatalysts. Basic knowledge of Python (or other programming languages) is highly (a must) recommended (loops, conditionals, basic data types like lists, dictionaries etc.).
The students are recommended to take Synthetic biology course 27640, which introduces the basic concepts of Synthetic Biology and molecular biological tools for the genetic engineering of production organisms. This course is complementary to the computer-aided cell factory course and can be taken either before, after, or concurrently with it. Both courses together provide an overall view of the Design-Build-Test-Learn cycle for cell factory engineering.
There is a chance to carry out some strategies suggested in the group assignment in practice in the 3-weeks course 27432.