{"title":"随机噪声对肠道菌群抗生素耐药性的影响。","authors":"Aofei Hu, Ling Yang, Jie Yan","doi":"10.1038/s41540-025-00548-4","DOIUrl":null,"url":null,"abstract":"<p><p>Antibiotic treatment is widely used for gastrointestinal diseases, often leading to drug resistance. However, the underlying mechanisms of drug resistance remain unclear. Mathematical modeling provides a powerful tool to explore the dynamics of antibiotic resistance, yet few models have considered the effect of biological noise, which originates from microscopic interactions between bacteria. In this study, we constructed a stochastic model based on the chemical master equations to investigate how stochastic noise influences the development of antibiotic resistance. Our simulations demonstrated that antibiotic resistance developed stepwise: while effective antibiotic treatments maintained the host's total pathogen numbers at healthy levels, the compositional balance shifted significantly through progressive increases in resistant pathogen proportions. Stochastic noise further amplified this shift and accelerated resistance by exacerbating post-treatment changes in the sensitive-to-resistant pathogen ratio. Finally, we found that the presence of coupling between different microbial communities can delay the onset of resistance and might even prevent its development. These results highlight noise's critical role in resistance development and suggest enhancing microbial interactions as a potential mitigation strategy.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"77"},"PeriodicalIF":3.5000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12264058/pdf/","citationCount":"0","resultStr":"{\"title\":\"The effect of stochastic noise on antibiotic resistance in intestinal flora.\",\"authors\":\"Aofei Hu, Ling Yang, Jie Yan\",\"doi\":\"10.1038/s41540-025-00548-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Antibiotic treatment is widely used for gastrointestinal diseases, often leading to drug resistance. However, the underlying mechanisms of drug resistance remain unclear. Mathematical modeling provides a powerful tool to explore the dynamics of antibiotic resistance, yet few models have considered the effect of biological noise, which originates from microscopic interactions between bacteria. In this study, we constructed a stochastic model based on the chemical master equations to investigate how stochastic noise influences the development of antibiotic resistance. Our simulations demonstrated that antibiotic resistance developed stepwise: while effective antibiotic treatments maintained the host's total pathogen numbers at healthy levels, the compositional balance shifted significantly through progressive increases in resistant pathogen proportions. Stochastic noise further amplified this shift and accelerated resistance by exacerbating post-treatment changes in the sensitive-to-resistant pathogen ratio. Finally, we found that the presence of coupling between different microbial communities can delay the onset of resistance and might even prevent its development. These results highlight noise's critical role in resistance development and suggest enhancing microbial interactions as a potential mitigation strategy.</p>\",\"PeriodicalId\":19345,\"journal\":{\"name\":\"NPJ Systems Biology and Applications\",\"volume\":\"11 1\",\"pages\":\"77\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12264058/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NPJ Systems Biology and Applications\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1038/s41540-025-00548-4\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Systems Biology and Applications","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1038/s41540-025-00548-4","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
The effect of stochastic noise on antibiotic resistance in intestinal flora.
Antibiotic treatment is widely used for gastrointestinal diseases, often leading to drug resistance. However, the underlying mechanisms of drug resistance remain unclear. Mathematical modeling provides a powerful tool to explore the dynamics of antibiotic resistance, yet few models have considered the effect of biological noise, which originates from microscopic interactions between bacteria. In this study, we constructed a stochastic model based on the chemical master equations to investigate how stochastic noise influences the development of antibiotic resistance. Our simulations demonstrated that antibiotic resistance developed stepwise: while effective antibiotic treatments maintained the host's total pathogen numbers at healthy levels, the compositional balance shifted significantly through progressive increases in resistant pathogen proportions. Stochastic noise further amplified this shift and accelerated resistance by exacerbating post-treatment changes in the sensitive-to-resistant pathogen ratio. Finally, we found that the presence of coupling between different microbial communities can delay the onset of resistance and might even prevent its development. These results highlight noise's critical role in resistance development and suggest enhancing microbial interactions as a potential mitigation strategy.
期刊介绍:
npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology.
We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.