Optimizing complex phenotypes through model-guided multiplex genome engineering.

ABSTRACT: We present a method for identifying genomic modifications that optimize a complex phenotype through multiplex genome engineering and predictive modeling. We apply our method to identify six single nucleotide mutations that recover 59% of the fitness defect exhibited by the 63-codon E. coli strain C321.∆A. By introducing targeted combinations of changes in multiplex we generate rich genotypic and phenotypic diversity and characterize clones using whole-genome sequencing and doubling time measurements. Regularized multivariate linear regression accurately quantifies individual allelic effects and overcomes bias from hitchhiking mutations and context-dependence of genome editing efficiency that would confound other strategies.

SUBMITTER: Kuznetsov G 

PROVIDER: S-EPMC5445303 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature