Performance Prediction of Fundamental Transcriptional Programs.

antirepressors biological circuit prediction synthetic gene circuits synthetic transcription factors transcriptional programming

Journal

ACS synthetic biology
ISSN: 2161-5063
Titre abrégé: ACS Synth Biol
Pays: United States
ID NLM: 101575075

Informations de publication

Date de publication:
21 04 2023
Historique:
medline: 24 4 2023
pubmed: 21 3 2023
entrez: 20 3 2023
Statut: ppublish

Résumé

Transcriptional programming leverages systems of engineered transcription factors to impart decision-making (

Identifiants

pubmed: 36935615
doi: 10.1021/acssynbio.2c00593
pmc: PMC10127286
doi:

Substances chimiques

Transcription Factors 0

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

1094-1108

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Auteurs

Prasaad T Milner (PT)

School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-2000, United States.

Ziqiao Zhang (Z)

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-2000, United States.

Zachary D Herde (ZD)

School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-2000, United States.

Namratha R Vedire (NR)

School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia 30332-2000, United States.

Fumin Zhang (F)

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-2000, United States.

Matthew J Realff (MJ)

School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-2000, United States.

Corey J Wilson (CJ)

School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-2000, United States.

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Classifications MeSH