Nitin Bohra

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 develops an in vitro transcription-based biosensing workflow to quantify glycolate production in the CETCH cycle, showing high correlation with LC-MS at lower cost and faster speed.

This study constructs a metabolic and genetic linked in vitro network that generates its own building blocks from non-living matter and demonstrates life-like behavior.