ArticleJournal of the Royal Society, Interface2025
Deciphering plasmid replication dynamics: a computational approach to predict ColE1-like plasmid copy number.
Article in Journal of the Royal Society, Interface, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Deciphering plasmid replication dynamics: a computational approach to predict ColE1-like plasmid copy number.Journal of the Royal Society, Interface · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Plasmids constitute a key tool in synthetic biology, providing a versatile framework for various research and industrial ventures. An essential determinant of plasmid functionality is its copy number, impacting both protein production rates and host cell metabolic burden. However, currently there is no model that can computationally predict the plasmid copy number from the sequence of its origin of replication (ORI). We present a novel software solution tailored to simplify plasmid copy number design, poised to redefine plasmid engineering workflows. At the heart of our tool lies a comprehensive machine learning model, informed by numerous features extracted from the ORI. This computational model emphasizes the importance of promoter strength and the RNA folding dynamics of regulatory elements within the ORI. Additionally, we detail a robust protocol for the efficient manipulation of plasmid ORIs used to validate our model's predictive capabilities. This innovation represents a paradigm shift in plasmid-centric methodologies, offering unprecedented avenues for advancement in synthetic biology research and industrial applications. The software is available at: https://pcn-gradient.vercel.app/.
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Registered trials
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