Eduard Kerkhoven

Chalmers University of Technology

Key publications

Chen Y, Gustafsson J, Rangel AT, Anton M, Domenzain I, Kittikunapong C, Li F, Yuan L, Nielsen J, Kerkhoben EJ
(2024) Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0.
Nature Prot. doi:10.1038/s41596-023-00931-7

Fu J, Zaghen S, Lu H, Konzock O, Poorinmohammad N, Kornberg A, Koseto D, Wentzel A, Di Bartolomeo F, Kerkhoven EJ
(2023) Reprogramming Yarrowia lipolytica metabolism for efficient synthesis of itaconic acid from flask to semi-pilot scale.
bioRxiv. doi:10.1101/2023.07.17.549194

Zaghen S, Konzock O, Fu J, Kerkhoven EJ
(2023) Abolishing storage lipids induces protein misfolding and stress responses in Yarrowia lipolytica. J
Ind Microbiol Biotechnol. doi:10.1093/jimb/kuad031

Han Y, Rangel AT, Pomraning KR, Kerkhoven EJ, Kim J
(2023) Advances in genome-scale metabolic models of industrially important fungi.
Curr Opin Biotechnol. doi:10.1016/j.copbio.2023.103005

Li F, Yuan L, Lu H, Li G, Chen Y, Engqvist MKM, Kerkhoven EJ, Nielsen J
(2022) Deep learning based kcat prediction enables improved enzyme constrained model reconstruction.
Nat Catalysis. doi:10.1038/s41929-022-00798-z

Sulheim S, Kumelj T, van Dissel D, Salehzadeh-Yazdi A, Du C, Nieselt K, Almaas E, Wentzel A & Kerkhoven EJ
(2020) Enzyme-constrained models and omics analysis of Streptomyces coelicolor reveal metabolic changes that enhance heterologous production.
iScience: 23: 9. doi:10.1016/j.isci.2020.101525

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.

Group members

Cheewin Kittikunapong (PhD student)
Simone Zaghen (PhD student)

Last updated: 2024-01-26

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