Our research revolves around metabolic systems biology, where computational model-driven analysis of experimental data is used to understand, predict and engineer biology. With a particular focus on metabolism we bridge the gap between in silico prediction and in vivo validation through data-driven genetic engineering. We are working on a variety of different projects, from developing microbes as cell factories for sustainable production of chemicals, to investigating metabolic aspects in human disease.
Computational analysis of metabolism helps us to come up with strategies for metabolic engineering. We reconstruct and curate genome-scale metabolic models (GEMs) for various organisms (yeasts, bacteria, human) using our RAVEN Toolbox. Our model development is tracked on GitHub, and important models are those for S. cerevisiae, Y. lipolytica, S. coelicolor and Homo sapiens. These models are combined with omics analyses (primarily RNAseq and proteomics), either directly or through the use of enzyme-constrained models using our GECKO Toolbox. In addition to biotechnological applications, we have also been using our approaches to investigate for instance evolution of the yeast subphylum, and prediction of kcat values through deep learning.
Besides computational research, we also investigate the oleaginous yeast Y. lipolytica as microbial cell factory, for instance to produce itaconic acid. This promising platform chemical can be used as monomer to e.g. aid bioleaching, or as a range of innovative polymers. We perform this through genetic engineering, integrative omics analysis, modeling of metabolism and fermentation optimization.
Cheewin Kittikunapong (PhD student)
Simone Zaghen (PhD student)