Single-Course English 5 ECTS

Computer-aided cell factory design

Overall Course Objectives

The course focuses on the modern-contemporary methods and approaches for designing and analyzing cell factories using models and data/driven approaches. The aim of the course is to give the students a fundamental understanding of the interplay between the many different intracellular reactions in a cell factory, and especially how the fluxes through the different pathways are regulated. A special focus is given to pathways leading to industrially relevant products like primary metabolites, antibiotics, sustainable chemicals, industrial enzymes, or pharmaceutical proteins. A central aspect of the course is the computational modeling of cell factories to help identify the optimal strategies for introducing directed genetic changes in the host organism with the aim of obtaining better production strains. Analysis of the metabolic networks composed by the biochemical cellular reactions is a central element in the course, and tools for systems level strain characterization, using omic-data integration, and design will be described. Taking advantage of a number of computational modelling tools, students will learn to model and design cell factories using primarily programming codes.

Learning Objectives

  • 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.

Course Content

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.

Recommended prerequisites

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.

Teaching Method

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.

See course in the course database.





13 weeks




DTU Lyngby Campus

Course code 27410
Course type Candidate
Semester start Week 35
Semester end Week 48
Days Tues 13-17

7.500,00 DKK