Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Bacterial Flora of the Large Intestine01:29

Bacterial Flora of the Large Intestine

661
The gut microbiome is formed by a vast and diverse community of bacteria that colonizes our large intestine. These bacteria start residing in the gut from birth and continue diversifying throughout life, influenced by factors such as diet, lifestyle, and stress. The gut bacterial community also includes bacteria from food and those that enter the colon through the anus.
The normal gut flora of the colon plays a critical role in generating essential vitamins such as vitamins K, B5, and B7.
661

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Random-with-constraints: Constructing minimal models for high-dimensional biology.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Characterization of the Complete Genome of <i>Arthrobacter globiformis</i> Cluster FF Bacteriophage QuinnAvery.

microPublication biology·2026
Same author

The role of innate immunity, antibiotics, and bacteriophages in the course of bacterial infections and their treatment.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Physics-tailored machine learning reveals unexpected physics in dusty plasmas.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

A mathematical model for ketosis-prone diabetes suggests the existence of multiple pancreatic β-cell inactivation mechanisms.

eLife·2025
Same author

Millisecond-scale motor coding precedes sensorimotor learning in songbirds.

bioRxiv : the preprint server for biology·2024

相关实验视频

Updated: Sep 19, 2025

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions
09:54

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions

Published on: July 22, 2022

3.2K

在C. elegans肠道细菌负荷的变异表明了复杂的宿主-微生物动态.

Satya Spandana Boddu1, K Michael Martini1,2, Ilya Nemenman1,2,3

  • 1Department of Physics, Emory University, Atlanta, Georgia, United States of America.

PLoS computational biology
|June 9, 2025
PubMed
概括

宿主中的微生物社区组合是复杂的. 我们的研究表明,细菌动力学取决于高和低生长率之间的随机切换,而不仅仅是简单的模型.

更多相关视频

Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome
08:38

Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome

Published on: March 31, 2023

851
Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans
07:19

Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans

Published on: September 13, 2022

2.3K

相关实验视频

Last Updated: Sep 19, 2025

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions
09:54

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions

Published on: July 22, 2022

3.2K
Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome
08:38

Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome

Published on: March 31, 2023

851
Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans
07:19

Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans

Published on: September 13, 2022

2.3K

科学领域:

  • 微生物学 微生物学
  • 系统生物学 系统生物学
  • 计算生物学 计算生物学

背景情况:

  • 了解驱动宿主相关细菌群落变异的因素至关重要,但具有挑战性.
  • 目前的微生物社区组装模型往往简化了复杂的生态动态.

研究的目的:

  • 解构导致宿主内细菌组成变化的因素.
  • 开发和测试解释微生物动态的数学模型.

主要方法:

  • 将实验方法与数学建模结合起来.
  • 分析了与宿主相关的细菌群落的时间序列数据.
  • 开发了新的随机模型以捕捉观察到的动态.

主要成果:

  • 人口随机性和静止异质性不能完全解释观察到的细菌变异.
  • 细菌社区的动态更好地通过高和低生长率表型之间的随机切换来解释.
  • 开发了数学模型,以量化解释经验数据.

结论:

  • 微生物组组装动态比传统的物流增长模型建议的要复杂得多.
  • 增长表型之间的随机切换是宿主-细菌相互作用的关键因素.
  • 时间序列数据和高级建模对于理解宿主内的微生物动态至关重要.