Howard M. Salis

The paper develops a low-cost, high-throughput pipeline for genetic systems engineering, achieving high success rates in plasmid assembly and identifying factors affecting protein expression.

The paper quantifies how genetic mutations affect enhancer activity and gene expression using a novel method and identifies TF-specific regulatory programs and enhancer-gene relationships.

This study demonstrates engineered bacteria can detect TNT in soil over 21 days with stable activation and exponential decay dynamics.

The paper develops a 346-parameter model to predict transcription initiation rates for bacterial promoters and enables automated promoter design.

The paper quantifies how cosolutes affect gene expression dynamics in TX-TL systems and shows how to optimize them for different applications.

The paper quantifies sequence and structural factors affecting mRNA stability in bacterial operons through synthetic biology experiments and biophysical modeling.

The paper introduces an automated model test system that evaluates gene expression models using statistical methods and a database of genetic systems, improving model accuracy and hypothesis testing.

The paper develops a machine learning model to predict DNA fragment synthesis success and identifies sequence determinants affecting synthesis outcomes.

The paper develops an algorithm to predict optimal enzyme expression levels for many-enzyme metabolic pathways using sparse datasets, demonstrating accuracy and noise tolerance.

The paper quantifies how mRNA structures overlapping with the ribosomal footprint inhibit translation in E. coli, showing up to 1410-fold repression.