{"title":"解码质粒复制动力学:预测cole1样质粒拷贝数的计算方法。","authors":"Rinat Saban, Ofri Tfilin, David Haggiag, Yam Tawachi, Matan Arbel, Tamir Tuller","doi":"10.1098/rsif.2024.0783","DOIUrl":null,"url":null,"abstract":"<p><p>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/.</p>","PeriodicalId":17488,"journal":{"name":"Journal of The Royal Society Interface","volume":"22 228","pages":"20240783"},"PeriodicalIF":3.5000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12303113/pdf/","citationCount":"0","resultStr":"{\"title\":\"Deciphering plasmid replication dynamics: a computational approach to predict ColE1-like plasmid copy number.\",\"authors\":\"Rinat Saban, Ofri Tfilin, David Haggiag, Yam Tawachi, Matan Arbel, Tamir Tuller\",\"doi\":\"10.1098/rsif.2024.0783\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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/.</p>\",\"PeriodicalId\":17488,\"journal\":{\"name\":\"Journal of The Royal Society Interface\",\"volume\":\"22 228\",\"pages\":\"20240783\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12303113/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of The Royal Society Interface\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1098/rsif.2024.0783\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/7/16 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Royal Society Interface","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1098/rsif.2024.0783","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/7/16 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Deciphering plasmid replication dynamics: a computational approach to predict ColE1-like plasmid copy number.
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/.
期刊介绍:
J. R. Soc. Interface welcomes articles of high quality research at the interface of the physical and life sciences. It provides a high-quality forum to publish rapidly and interact across this boundary in two main ways: J. R. Soc. Interface publishes research applying chemistry, engineering, materials science, mathematics and physics to the biological and medical sciences; it also highlights discoveries in the life sciences of relevance to the physical sciences. Both sides of the interface are considered equally and it is one of the only journals to cover this exciting new territory. J. R. Soc. Interface welcomes contributions on a diverse range of topics, including but not limited to; biocomplexity, bioengineering, bioinformatics, biomaterials, biomechanics, bionanoscience, biophysics, chemical biology, computer science (as applied to the life sciences), medical physics, synthetic biology, systems biology, theoretical biology and tissue engineering.