adaptive laboratory evolution
data-driven strain design
genome design variables
meta-analysis
mutation functional analysis
structural biology
Journal
ACS synthetic biology
ISSN: 2161-5063
Titre abrégé: ACS Synth Biol
Pays: United States
ID NLM: 101575075
Informations de publication
Date de publication:
17 12 2021
17 12 2021
Historique:
pubmed:
12
11
2021
medline:
1
4
2022
entrez:
11
11
2021
Statut:
ppublish
Résumé
Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological systems. Adaptive Laboratory Evolution (ALE) leverages nature's problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study describes how novel strain designs, or genome sequences not yet observed in ALE experiments or published designs, can be extracted from aggregated ALE data and demonstrates this by designing, building, and testing three novel
Identifiants
pubmed: 34762392
doi: 10.1021/acssynbio.1c00337
pmc: PMC8870144
doi:
Substances chimiques
Escherichia coli Proteins
0
Types de publication
Journal Article
Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3379-3395Subventions
Organisme : NIAID NIH HHS
ID : U01 AI124316
Pays : United States
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