A defined online quantification and identification method for cell concentrations and heterogeneous cell states and types was developed using automated flow cytometry. To achieve this, a combination of OC-300 automation system coupled with DAPI staining and analysis process were designed. Besides, a developed hyperspectral monitoring system was achieved that enables the quantification of biomass, pigment, and PHB. This system was applied to monitor biomass and PHB production in real samples from partners.
By combining proteomics, metabolomics, and stable isotope labelling techniques, a comprehensive omics analysis platform was developed aiming to understand individuals within complex microbial consortia. This platform was successfully applied for analysis of pure culture of engineered strains, coculture of phototrophs, and natural consortia. The results provided a deep insight into microbial interactions and, thus, could help to improve efficiency towards final products.
A computational workflow was established to identify and measure the abundance of taxonomies within target microbiomes. Real environmental samples were collected and examined using the workflow to understand microbial composition.
A hybrid modelling platform based on Physics-informed neural networks for dynamic modelling and control of natural microbiomes was established. This modelling platform used the deep learning method of adaptive moment estimation and was applied further for automatic control of a bioreactor.
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