Shenghu Zhou

The paper develops a dynamic regulation network in E. coli to balance malonyl-CoA for enhanced (2S)-naringenin synthesis, achieving an 8.4-fold production improvement with balanced growth.

The paper constructs a synthetic promoter library in E. coli and uses machine learning to predict promoter strength, finding that an XgBoost model achieves optimal performance.

This study develops a rational design platform using deep learning to predictably tune biosensor dynamic range by optimizing ribosome-binding sites in bacteria.