Drew S. Tack

Epistasis in Allosteric Proteins: Can Biophysical Models Provide a Better Framework for Prediction and Understanding?

The paper compares biophysical and phenomenological models to predict epistasis in allosteric proteins, finding biophysical models better capture mutational effects with less epistasis.

Precision engineering of biological function with large-scale measurements and machine learning

The paper presents two methods for precision engineering of genetic sensors using large-scale data and machine learning to achieve quantitative dose-response specifications.

The genotype-phenotype landscape of an allosteric protein

Dose-response curves for 62 472 LacI coding sequences train a recurrent network for EC50 and G∞, enable EC50 targeting within 1.25-fold, and reveal band-stop sensors.

Improved stability of an engineered function using adapted bacterial strains

The paper shows that adapting bacterial strains to growth conditions improves the stability of engineered functions across generations without genomic changes.