Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

ABSTRACTThere is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent co...

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Bibliografiske detaljer

Principais autores:	Andrew J. Borchert, Alissa C. Bleem, Hyun Gyu Lim, Kevin Rychel, Keven D. Dooley, Zoe A. Kellermyer, Tracy L. Hodges, Bernhard O. Palsson, Gregg T. Beckham
Format:	Artigo
Sprog:	Inglês
Udgivet:	American Society for Microbiology 2024-03-01
Serier:	mSystems
Fag:	transposon insertion sequencing RB-TnSeq independent component analysis machine learning Pseudomonas putida aromatic catabolism
Online adgang:	https://journals.asm.org/doi/10.1128/msystems.00942-23
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