Joshua A. Urrutia

The paper describes the neuroimaging component of the A2CPS project, detailing data collection, processing, and initial findings linking brain measures to clinical variables.

The paper identifies synthetic gene network-induced phenotypes by analyzing gene expression in E. coli and validates them through antibiotic resistance experiments.

This paper introduces DART, a DBTL loop for synthetic genetic circuit design, showing that complex circuits can be more robust and reproducible than simpler ones in budding yeast.

The paper develops a computational method to predict synthetic circuit robustness across growth conditions and validates it with experimental results in budding yeast.

The paper introduces a machine learning model to predict whole-cell transcriptional responses to combinations of perturbations using data from single inducers.