Olivier Borkowski

The paper develops a cell-free transcriptomics platform using active learning to optimize buffer conditions, significantly increasing mRNA yield and enabling deeper insights into genome regulation.

The paper uses active learning to explore a large combinatorial space of cell-free compositions to maximize protein production and identify critical parameters.

The paper predicts the cost of protein expression in E. coli using a cell-free assay and computational model to assess resource consumption and differentiate between gene expression and metabolic pathway burdens.

The paper identifies heat-shock promoters that activate rapidly in response to synthetic gene expression in E. coli and uses them to build a CRISPR/dCas9-based feedback system that improves protein yield by adjusting synthetic construct expression based on cellular burden.

This paper develops a system for autonomous control of heterologous gene expression in batch cultures using endogenous promoters and recombinase switches.