Yeast Modelling and Machine Learning
In this group we aim to better understand microbial (specifically yeast) metabolism and to use this knowledge to engineer cell factories for a sustainable production of food and chemicals.
What we try to achieve
We generate computational models that represent the entire set of metabolic reactions within a microbial cell. These models are constantly improved to become better in-silico representations of the biological cell and to be able to more accurately predict genetic engineering strategies.
Why our research is important and how it can be used
The world needs more sustainable modes to produce fuels, chemicals, food ingredients and pharmaceuticals. Microbial cell factories play an important part in this transition. However, microbial metabolism is complex and internal constraints can limit the production of titers and yields necessary to make a production process economically viable. Genome-scale metabolic models and machine learning approaches can help in identifying these constraints and suggest strategies to engineer individual enzymes, biochemical pathways or entire metabolic networks within the cell factories.
How we achieve our aims – methods, tools, technologies
We reconstruct and curate genome-scale metabolic models (GEMs) using our RAVEN Toolbox. Our model development is tracked on GitHub. The models are combined with omics analyses (primarily RNAseq and proteomics), either directly or through the use of enzyme-constrained models using our GECKO Toolbox, as well as with machine-learning approaches.
The group is headed by Verena Siewers and Eduard Kerkhoven and is located at Chalmers University of Technology in Gothenburg, Sweden.
We generate computational models that represent the entire set of metabolic reactions within a microbial cell. These models are constantly improved to become better in-silico representations of the biological cell and to be able to more accurately predict genetic engineering strategies.
Why our research is important and how it can be used
The world needs more sustainable modes to produce fuels, chemicals, food ingredients and pharmaceuticals. Microbial cell factories play an important part in this transition. However, microbial metabolism is complex and internal constraints can limit the production of titers and yields necessary to make a production process economically viable. Genome-scale metabolic models and machine learning approaches can help in identifying these constraints and suggest strategies to engineer individual enzymes, biochemical pathways or entire metabolic networks within the cell factories.
How we achieve our aims – methods, tools, technologies
We reconstruct and curate genome-scale metabolic models (GEMs) using our RAVEN Toolbox. Our model development is tracked on GitHub. The models are combined with omics analyses (primarily RNAseq and proteomics), either directly or through the use of enzyme-constrained models using our GECKO Toolbox, as well as with machine-learning approaches.
The group is headed by Verena Siewers and Eduard Kerkhoven and is located at Chalmers University of Technology in Gothenburg, Sweden.