Metabolic Engineering & Systems Biology (MESB26)
Description
The advanced course in Metabolic Engineering & Systems Biology (MESB) is organized by the Systems and Synthetic Biology division at Chalmers University of Technology, and is intended for MSc and PhD students, postdocs, and industrial researchers that are interested in computational aspects of metabolic engineering, or applications of genome-scale modelling in studies of human disease.
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
Learning Outcomes
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.
Prerequisites & Technical Requirements
Prerequisites
There are no formal prerequisites for this course
Technical requirements
You should bring your own laptop computer-
Topics & Tags
Affiliations & Networks
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