在选择性细菌生长的培养基优化中采用主动学习技术

Shuyang Zhang, Honoka Aida, Bei-Wen Ying
{"title":"在选择性细菌生长的培养基优化中采用主动学习技术","authors":"Shuyang Zhang, Honoka Aida, Bei-Wen Ying","doi":"10.3390/applmicrobiol3040091","DOIUrl":null,"url":null,"abstract":"Medium optimization and development for selective bacterial cultures are essential for isolating and functionalizing individual bacteria in microbial communities; nevertheless, it remains challenging due to the unknown mechanisms between bacterial growth and medium components. The present study first tried combining machine learning (ML) with active learning to fine-tune the medium components for the selective culture of two divergent bacteria, i.e., Lactobacillus plantarum and Escherichia coli. ML models considering multiple growth parameters of the two bacterial strains were constructed to predict the fine-tuned medium combinations for higher specificity of bacterial growth. The growth parameters were designed as the exponential growth rate (r) and maximal growth yield (K), which were calculated according to the growth curves. The eleven chemical components in the commercially available medium MRS were subjected to medium optimization and specialization. High-throughput growth assays of both strains grown separately were performed to obtain thousands of growth curves in more than one hundred medium combinations, and the resultant datasets linking the growth parameters to the medium combinations were used for the ML training. Repeated rounds of active learning (i.e., ML model construction, medium prediction, and experimental verification) successfully improved the specific growth of a single strain out of the two. Both r and K showed maximized differentiation between the two strains. A further analysis of all the data accumulated in active learning identified the decision-making medium components for growth specificity and the differentiated, determinative manner of growth decisions of the two strains. In summary, this study demonstrated the efficiency and practicality of active learning in medium optimization for selective cultures and offered novel insights into the contribution of the chemical components to specific bacterial growth.","PeriodicalId":8080,"journal":{"name":"Applied microbiology","volume":"91 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Employing Active Learning in Medium Optimization for Selective Bacterial Growth\",\"authors\":\"Shuyang Zhang, Honoka Aida, Bei-Wen Ying\",\"doi\":\"10.3390/applmicrobiol3040091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Medium optimization and development for selective bacterial cultures are essential for isolating and functionalizing individual bacteria in microbial communities; nevertheless, it remains challenging due to the unknown mechanisms between bacterial growth and medium components. The present study first tried combining machine learning (ML) with active learning to fine-tune the medium components for the selective culture of two divergent bacteria, i.e., Lactobacillus plantarum and Escherichia coli. ML models considering multiple growth parameters of the two bacterial strains were constructed to predict the fine-tuned medium combinations for higher specificity of bacterial growth. The growth parameters were designed as the exponential growth rate (r) and maximal growth yield (K), which were calculated according to the growth curves. The eleven chemical components in the commercially available medium MRS were subjected to medium optimization and specialization. High-throughput growth assays of both strains grown separately were performed to obtain thousands of growth curves in more than one hundred medium combinations, and the resultant datasets linking the growth parameters to the medium combinations were used for the ML training. Repeated rounds of active learning (i.e., ML model construction, medium prediction, and experimental verification) successfully improved the specific growth of a single strain out of the two. Both r and K showed maximized differentiation between the two strains. A further analysis of all the data accumulated in active learning identified the decision-making medium components for growth specificity and the differentiated, determinative manner of growth decisions of the two strains. In summary, this study demonstrated the efficiency and practicality of active learning in medium optimization for selective cultures and offered novel insights into the contribution of the chemical components to specific bacterial growth.\",\"PeriodicalId\":8080,\"journal\":{\"name\":\"Applied microbiology\",\"volume\":\"91 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied microbiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/applmicrobiol3040091\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied microbiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/applmicrobiol3040091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

培养基的优化和开发对于分离微生物群落中的单个细菌和使其功能化至关重要;然而,由于细菌生长和培养基成分之间的未知机制,它仍然具有挑战性。本研究首先尝试将机器学习(ML)与主动学习相结合,对两种不同细菌(即植物乳杆菌和大肠杆菌)的选择性培养培养基成分进行微调。构建考虑两种菌株多种生长参数的ML模型,预测培养基组合的微调,以获得更高的细菌生长特异性。以指数生长率(r)和最大生长量(K)为生长参数,根据生长曲线计算生长参数。对市售介质MRS中的11种化学成分进行了介质优化和专一化。对两株菌株分别进行高通量生长试验,在100多种培养基组合中获得数千条生长曲线,并将生长参数与培养基组合联系起来的所得数据集用于ML训练。反复的主动学习(即ML模型构建、培养基预测和实验验证)成功地提高了两个菌株中单个菌株的特定生长。r和K在两个菌株之间表现出最大的分化。进一步分析在主动学习中积累的所有数据,确定了生长特异性的决策介质成分和两种菌株生长决策的差异化、决定性方式。总之,本研究证明了主动学习在选择性培养基优化中的效率和实用性,并为化学成分对特定细菌生长的贡献提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Employing Active Learning in Medium Optimization for Selective Bacterial Growth
Medium optimization and development for selective bacterial cultures are essential for isolating and functionalizing individual bacteria in microbial communities; nevertheless, it remains challenging due to the unknown mechanisms between bacterial growth and medium components. The present study first tried combining machine learning (ML) with active learning to fine-tune the medium components for the selective culture of two divergent bacteria, i.e., Lactobacillus plantarum and Escherichia coli. ML models considering multiple growth parameters of the two bacterial strains were constructed to predict the fine-tuned medium combinations for higher specificity of bacterial growth. The growth parameters were designed as the exponential growth rate (r) and maximal growth yield (K), which were calculated according to the growth curves. The eleven chemical components in the commercially available medium MRS were subjected to medium optimization and specialization. High-throughput growth assays of both strains grown separately were performed to obtain thousands of growth curves in more than one hundred medium combinations, and the resultant datasets linking the growth parameters to the medium combinations were used for the ML training. Repeated rounds of active learning (i.e., ML model construction, medium prediction, and experimental verification) successfully improved the specific growth of a single strain out of the two. Both r and K showed maximized differentiation between the two strains. A further analysis of all the data accumulated in active learning identified the decision-making medium components for growth specificity and the differentiated, determinative manner of growth decisions of the two strains. In summary, this study demonstrated the efficiency and practicality of active learning in medium optimization for selective cultures and offered novel insights into the contribution of the chemical components to specific bacterial growth.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Production of Functional Vinegar Enriched with γ-Aminobutyric Acid through Serial Co-Fermentation of Lactic Acid and Acetic Acid Bacteria Using Rice Wine Lees Genomic Characterization of Selected Escherichia coli Strains from Catfish (Clarias gariepinus) in Nigeria Metatranscriptomic Analysis of Argentinian Kefirs Varying in Apparent Viscosity Multiplex-PCR Detection of an Atypical Leuconostoc mesenteroides subsp. jonggajibkimchii Phenotype Dominating the Terminal Spoilage Microbial Association of a Fresh Greek Whey Cheese Stored at 4 °C in Vacuum Interaction between Trichoderma sp., Pseudomonas putida, and Two Organic Amendments on the Yield and Quality of Strawberries (Fragaria x annanasa cv. San Andreas) in the Huaral Region, Peru
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1