Reconstruction of Eukaryotic Compartmentalized Genome-Scale Metabolic Models Using Deep Learning for over 700 Fungi
Synthetic Biology Engineering Evolution Design SEED
2021
2021 Synthetic Biology: Engineering, Evolution & Design (SEED)
Poster Session
Poster Session
We integrated protein localization prediction using a novel deep learning method and functional annotation to top down genome-scale metabolic model reconstruction for creating species-specific compartmentalized genome-scale metabolic models. We further developed an annotated universal fungal metabolic model for top down reconstruction of species-specifically compartmentalized models for 765 fungal species. The universal fungal model provides a platform for community development.
Fungal kingdom encompasses species important for human health, environment, and industrial biotechnology. The reconstructed model set offers valuable hypothesis generation tools for understanding the role of eukaryotic microbes in microbial communities and metabolic capabilities of mushrooms, designing optimized eukaryotic hosts for industrial biotechnology, and identifying drug targets against pathogenic fungi. Beyond fungi, integrating protein localization prediction to model reconstruction allows combining other universal models for reconstructing cell type specific models for other eukaryotes including plants, insects, and mammals.