Hai He

This paper combines adaptive laboratory evolution and machine learning to engineer a methylsuccinate module for synthetic C1 metabolism in E. coli, achieving growth in three weeks versus six months with evolution alone.

This paper presents a computational workflow for designing and testing E. coli metabolic sensors that detect glyoxylate and glycolate across a wide concentration range, with applications in bioengineering and environmental monitoring.

The paper engineers a synthetic C1-assimilation cycle in E. coli to enable growth on formate, demonstrating the feasibility of synthetic carbon fixation from CO2.

This study identifies a new carbon fixation pathway in E. coli using only native enzymes and demonstrates its in vivo feasibility through genetic engineering.