Encoding Genetic Circuits with DNA Barcodes Paves the Way for Machine Learning-Assisted Metabolite Biosensor Response Curve Profiling in Yeast.
ACS Synth Biol
; 11(2): 977-989, 2022 02 18.
Article
em En
| MEDLINE
| ID: mdl-35089702
ABSTRACT
Genetically encoded biosensors are valuable tools used in the precise engineering of metabolism. Although a large number of biosensors have been developed, the fine-tuning of their dose-response curves, which promotes the applications of biosensors in various scenarios, still remains challenging. To address this issue, we leverage a DNA trackable assembly method and fluorescence-activated cell sorting coupled with next-generation sequencing (FACS-seq) technology to set up a novel workflow for construction and comprehensive characterization of thousands of biosensors in a massively parallel manner. An FapR-fapO-based malonyl-CoA biosensor was used as proof of concept to construct a trackable combinatorial library, containing 5184 combinations with 6 levels of transcription factor dosage, 4 different operator positions, and 216 possible upstream enhancer sequence (UAS) designs. By applying the FACS-seq technique, the response curves of 2632 biosensors out of 5184 combinations were successfully characterized to provide large-scale genotype-phenotype association data of the designed biosensors. Finally, machine-learning algorithms were applied to predict the genotype-phenotype relationships of the uncharacterized combinations to generate a panoramic scanning map of the combinatorial space. With the assistance of our novel workflow, a malonyl-CoA biosensor with the largest dynamic response range was successfully obtained. Moreover, feature importance analysis revealed that the recognition sequence insertion scheme and the choice of UAS have a significant impact on the dynamic range. Taken together, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Saccharomyces cerevisiae
/
Técnicas Biossensoriais
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
ACS Synth Biol
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China