Jonathan D. Finn

This paper introduces the EDEN family of foundation models for AI-programmable therapeutic design, demonstrating their ability to generate functional gene insertions, antibiotic peptides, and synthetic microbiomes.

This paper introduces a novel genome engineering method for iPSCs that enables efficient multiplex gene editing and clone selection, improving the precision and speed of iPSC-based therapies.

This paper introduces L-PGI, a ligase-mediated gene editing system that enables efficient and precise genomic integration in both dividing and nondividing cells, overcoming limitations of reverse transcriptase-based methods.

The paper introduces T-PGI, a system for scarless, programmable genome integration, showing high-fidelity insertion with minimal structural variants.

This paper develops circular AAV vectors for DSB-free genomic integration in the liver, demonstrating seamless gene insertion in human hepatocytes and in vivo.

The paper identifies low fidelity in RT-based genome editing in non-dividing mouse hepatocytes and proposes solutions using neonatal mice and transgenic models.

The paper develops Bxb1 variants with improved integrase activity and fidelity, enhancing in vivo efficiency and reducing off-target effects.

This paper demonstrates programmable genomic integration in non-human primate liver, achieving curative levels of gene expression in over 50% of liver cells.

The paper introduces IntQuery, a deep learning model that predicts LSI off-target activity genome-wide using data from Cryptic-seq.

The paper develops and validates methods to detect off-target recombination by Large Serine Integrases, finding higher sensitivity than whole genome sequencing.