Mark Isalan

This study improves TEV protease activity through loop engineering using a phagemid-based selection system combined with deep learning.

This paper demonstrates physical learning in engineered bacteria using a genetically encoded local learning rule that updates analogue weights through environmental inputs and differential growth.

The paper engineers a synthetic three-node Turing gene circuit in E. coli that generates periodic spatial patterns, validated by a differential equation model.

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

This paper describes a system of engineered bacteria that evolve to play tic-tac-toe, demonstrating complex decision-making through gene circuits.

The paper shows antibiotic resistance genes in E. coli exhibit dose-responsive expression under increasing antibiotic concentration, analogous to Ohm's law, and identifies mechanisms involving mRNA/protein variation and cell memory.

This paper engineers a 63-amino acid peptide that functions as both an activator and repressor, demonstrating that minimal transcription factor activity is achievable through directed evolution.

The paper shows that different gene regulatory networks in E. coli constrain phenotypic variation differently, even when producing the same phenotype.

The paper explores Turing pattern design space to determine how common and robust they are, finding that they are common but sensitive to parameter changes.