環境ゲノムと機械学習の融合による未知代謝機能の解明と環境工学イノベーションの創出

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We employed a machine learning-based approach that utilizes two foundational biological features of aromatic compound degradation: activation and organization. The fellow and host successfully identified novel metabolic pathways and organisms involved in utilization and conversion of complex aromatics compounds in wastewater treatment processes. Further evolutionary analyses showed how many contemporarily evolved in plastics industry wastewater streams. The results are expected to bring high impact to the microbiology and engineering communities and will be submitted for publication soon.