Optimized oxidative enzyme systems for efficient conversion of lignocellulose to valuable products.

The OXYMOD project will through a transdisciplinary approach define, develop and demonstrate applicability of new enzyme systems for the efficient biocatalytic conversion of lignocellulose from abundant Norwegian biomass into valuable products like sugars and aromatic building blocks. OXYMOD will focus on the still largely underexplored group of redox enzymes and their potential in the depolymerisation of cellulose, hemicellulose and lignin, including aspects such as redox enzyme interplay, co-factors and reaction partners, as well as their interplay with hydrolytic enzymes. OXYMOD will address these enzymes and enzyme systems as they occur and function in, among others, a unique in-house collection of approx. 1000 marine Actinobacteria isolates with genomes recently sequenced.

Redox enzymes require co-factors and redox partners, and there is a considerable degree of cooperativity between different enzyme classes. Enzyme systems-scale understanding and eventually engineering the efficient degradation of lignocellulose by these enzyme systems, requires an integrated transdisciplinary approach far beyond 'simple' enzyme discovery.

OXYMOD combines life sciences (enzyme biochemistry, enzyme production technology, microbial biotechnology, high throughput screening, advanced analytics), ICT (bioinformatics, big data handling), mathematical sciences (enzyme systems modelling, process modelling) and engineering (enzyme evolution, synthetic biology) for producing new and optimized biocatalytic systems for industrial application, primarily within the agricultural and forest sectors.

Besides the enzymes and enzyme systems themselves, additional innovations from OXYMOD concern the generation of well-defined products streams, primarily sugars from (hemi-)cellulose and aromatic building blocks from lignin for a variety of downstream applications (e.g. biofuels & bioplastics).

Project information

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  • Duration:
    Start-up 2017
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