Lignocellulosic material (LCM)-derived D-xylose, common in the second generation biomass, cannot be catabolized by commonly used microorganisms like Saccharomyces cerevisiae. By genetic engineering, enzymes from a bacterium are expressed, that lead to an S. cerevisiae host that is flexible and carbon efficient, especially for TCA cycle derived metabolites. The engineered host is able to convert D-xylose into value added products, such as 4-hydroxybutanal, 1,4-butanediol and α-ketoglutarate. The project aims at omics characterisation of D-xylose utilising production strains under process-relevant conditions. The project has a high potential for innovative industrial applications, and it is expected to contribute to improve the competitiveness of the European biotechnological industry.
Combination of genetic engineering and systems biology are used to demonstrate the potential of a rational system-level approach for metabolic engineering of S. cerevisiae. Model-driven optimised growth-decoupled production with engineered S. cerevisiae strain for carbon efficient and flexible usage of D-xylose. Advanced metabolomics, isotopomer pathway analysis and 13C-metabolic flux analysis will be used in this project.
Comprehensive multi-omics datasets of D-xylose utilising production strains:
This project will use steady-state and dynamic cultivations as well as specialised devices for sampling and cultivation. In this project the bioscope-bioreactor will be applied to obtain dynamic measurements after a substrate perturbation. The bioscope plug flow bioreactor is crucial for enabling concentration and labelling measurements (van Heerden et al., 2014). The short-term response data will allow the identification of kinetic mechanisms and enables the identification of putative side reactions leading to losses of the substrate. Extensive metabolomics approaches that allow the measurement of intracellular metabolite concentrations as well as labelling enrichments based on GC-MS/MS and LC-MS/MS and 13C internal standards.
Isotopomer pathway analysis and 13C-metabolic flux analysis:
Intracellular flux distributions of selected 1,4-butanediol/ α -ketoglutarate producer strains. And mechanistic models for simulating steady-state and dynamic pertubation experiments. Modeling will be performed using MatLab and/or gPROMS software
- Schumacher R & Wahl SA (2015) Effective Estimation of Dynamic Metabolic Fluxes Using 13C Labeling and Piecewise Affine Approximation: From Theory to Practical Applicability. Metabolites 5(4):697-719.
- Abate, A.; Hillen, R. &Wahl, A.S., Piecewise affine approximations of fluxes and enzyme kinetics from in vivo 13C labelling experiments. International Journal of Robust and Nonlinear Control, 2012, 2210, 1120-1139.
- van Heerden JH, et al. (2014) Lost in Transition: Start-Up of Glycolysis Yields Subpopulations of Nongrowing Cells. Science 343(6174):1245114.