Metabolic Engineering & Systems Biology

About

This course covers various (computational) aspects of metabolic engineering. including modelling of metabolism, fermentation technologies, proteomics and integrative omics analysis. The course dinner is a BBQ on a pier!

The aim of the course is to provide participants with a strong primer of various (computational) aspects of metabolic engineering. In particular, the course covers the following topics:

- Constraint-based genome-scale modelling
- Enzyme-constrained modelling
- Models of microbial communities
- Fermentation technologies
- Proteomics and RNAseq data generation and analysis
- Integrative gene-expression data analysis

A central question in all the activities is **How can I use this in my metabolic engineering project**?

Course leader

Eduard Kerkhoven

Target group

PhD students
Postdocs
Academic researchers
Industrial researchers

Course aim

Course content and learning goals:

Metabolic engineering
- Microbial cell factory development through metabolic engineering.
- The use of computational modelling and omics data in metabolic engineering.

Computational modelling of metabolism
- Principles of constraint-based modelling, including flux balance analysis and model reconstruction.
- Get hands-on experience in performing simulations with a genome-scale model using the RAVEN Toolbox.
- Proteome- and enzyme-constrained modelling of metabolism.
- Get hands-on experience in simulating enzyme-constrained models with GECKO Toolbox.
-Visualization of flux and other omics data on metabolic pathway maps.
- Constraint-based simulation of microbial communities.

Fermentation technologies
- The various different modes by which microbial bioreactor cultivations can be done.
- Suitability of the different cultivation modes for use with microbial cell factories.
- Learn how to calculate rates from bioreactor cultivations, to use as input for constraint-based models.

Transcriptomics and proteomics analysis
- Learn about the principles of transcriptomics and proteomics for differential gene expression analysis.
- What to consider when designing an experiment.
- How to process the data to ensure high quality analysis.
- Get hands-on experience in converting raw RNAseq data into differential gene expression results.

Integrative data analysis
- How various types of data can be combined to extract new hypotheses from your data.
- Get hands-on experience in performing gene-set enrichment analysis with RNAseq data.
- Learn how proteomics data can be used to constrain enzyme-constrained models.

Fee info