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Title: Optimizing complex phenotypes through model-guided multiplex genome engineering

Journal Article · · Genome Biology (Online)

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.ΔA. 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.

Research Organization:
Harvard Univ., Boston, MA (United States). Harvard Medical School; Harvard Univ., Cambridge, MA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Contributing Organization:
AWS Cloud Credits for Research Program
Grant/Contract Number:
FG02-02ER63445
OSTI ID:
1618923
Alternate ID(s):
OSTI ID: 1371697; OSTI ID: 1389296
Journal Information:
Genome Biology (Online), Journal Name: Genome Biology (Online) Vol. 18 Journal Issue: 1; ISSN 1474-760X
Publisher:
Springer Science + Business MediaCopyright Statement
Country of Publication:
United Kingdom
Language:
English
Citation Metrics:
Cited by: 15 works
Citation information provided by
Web of Science

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Figures / Tables (5)


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