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

ABSTRACT: As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to

SUBMITTER: Tack DS 

PROVIDER: S-EPMC10057847 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature