Francesca Ceroni

The paper develops a sequencing-based framework and a neural network model to predict plasmid propagation from sequence, revealing sequence-encoded interactions that influence plasmid stability and host compatibility.

STAMPS designs novel mammalian signal peptides to enhance protein secretion, outperforming existing benchmarks in multiple cell lines.

The paper introduces a scalable platform to generate large-scale sequence-to-expression datasets in human cells and trains deep learning models to predict gene expression from DNA sequences.

This paper introduces RE array plasmids and the CTRL method to control gene expression by sequestering transcription factors in mammalian cells.

This study investigates degradation bottlenecks and resource competition in mammalian cells using degron tags, showing that degradation rates of proteins are coupled due to competition for cellular resources.

The paper designs an intracellular aptamer-based biosensor to monitor burden in E. coli by evaluating aptamer performance across promoters and strains.

This paper develops a non-invasive plate reader assay for high-throughput mammalian cell analysis, showing its applicability to different cell lines and conditions.

The paper quantifies resource load from genetic constructs in mammalian cells and provides a framework for designing more efficient synthetic circuits.

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

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.

The paper introduces an in vivo monitor to quantify cellular capacity in E. coli and identifies gene expression designs with reduced burden that outperform less efficient designs.