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!

Topics:
- Constraint-based genome-scale modelling
- Proteome- and enzyme-constrained modelling
- Fermentation technologies
- Proteomics technologies
- 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 and 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
- Learn the 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
- Learn about the benefits of proteome- and enzyme-constrained modelling of metabolism
- Get hands-on experience in simulating enzyme-constrained models with GECKO

Bioreactor 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

Proteomics technologies
- The various different approaches by which microbial proteomics can be performed
- The use of proteomics in the development and improvement of microbial cell factories
- Learn how to determine absolute quantitative protein levels, to use as input for enzyme-constrained models

RNAseq data generation and analysis
- Learn about the principles of RNAseq for differential gene expression analysis
- What to consider when designing an RNAseq experiment
- How to process the RNAseq 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

Fee info