Statistical develoPments and ApplicatioN on Oxygenic photogRAnules for longitudinal Multi-omICS data
Oxygenic photogranules (OPGs) could turn wastewater treatment in circular bioeconomy by capturing much of the energetic and chemical value of wastewater in their biomass. But essential knowledge on their formation and life cycle is still missing and needed for conducting a bioprocess based on OPGs. Omics (in particular metabolomics) are the best tools to understand photogranulation but still require a specific effort in statistical modelling to make full use of them. This project will tackle both challenges: the development of automatic and reproducible data analysis methods for metabolomics, the implementation of a longitudinal multi-omics data integration methodology and the use of these new approaches to investigate the life cycle of OPGs, mainly their switch from growth activity to storage of compounds which is one of the major remaining challenges for the industrial use of OPGs in depollution of wastewater.
Oxygenic photogranules (OPGs) could turn wastewater treatment in circular bioeconomy by capturing much of the energetic and chemical value of wastewater in their biomass. But essential knowledge on their formation and life cycle is still missing and needed for conducting a bioprocess based on OPGs. Omics (in particular metabolomics) are the best tools to understand photogranulation but still require a specific effort in statistical modelling to make full use of them. This project will tackle both challenges: the development of automatic and reproducible data analysis methods for metabolomics, the implementation of a longitudinal multi-omics data integration methodology and the use of these new approaches to investigate the life cycle of OPGs, mainly their switch from growth activity to storage of compounds which is one of the major remaining challenges for the industrial use of OPGs in depollution of wastewater.

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