Evangelos-Marios Nikolados

The paper designs synthetic promoters that respond to TFs differentially expressed in activated and exhausted T cells, enabling functional state classification and dynamic gene expression control.

This study compares model accuracy and data efficiency across machine learning models for predicting protein expression from DNA sequences using experimental data from E. coli and S. cerevisiae.

The paper models how synthetic gene circuits affect bacterial growth and shows that cellular capacity limits circuit function.