Our research revolves around metabolic systems biology, where 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 genetic engineering. We are working on a variety of different projects, both in computational dry-lab and experimental wet-lab.
We use computational analysis of metabolism to answer various research questions. We mostly use constraint-based approaches, and for this we reconstruct and curate genome-scale metabolic models (GEMs) for various organisms (yeasts, bacteria, human) using our RAVEN Toolbox. These models are often combined with omics analyses (primarily RNAseq and proteomics), either directly or using enzyme-constrained models. Machine learning approaches are used to fill the gaps of our knowledge, or to make sense of our observations.
Applications of the metabolic models includes devising new strategies for metabolic engineering, investigations of human disease, and fundamental studies on the role of evolution on metabolism.
Sustainable production of chemicals
Beyond computational analysis, we perform wet-lab genetic engineering to develop microbes as cell factories for sustainable production of chemicals. Our preferred hosts are oleaginous yeasts (such as Yarrowia lipolytica) as they are able to accumulate large amount of lipids. These lipids can either be directly used as product, or we perform genetic engineering to rewire the metabolic network to produce other high-value chemicals.