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相关概念视频

Evolution of Microbial Genome01:08

Evolution of Microbial Genome

Microbial genome evolution is a highly dynamic process shaped by continual gene gain and loss across species and strains. This genomic flexibility allows microorganisms to adapt rapidly to environmental pressures and interactions with other organisms. Central to understanding this diversity is the distinction between the core and pan genomes.The core genome comprises the genes shared by all sampled strains of a species, representing essential functions needed for fundamental cellular processes.
Introduction to Microbial Ecology01:28

Introduction to Microbial Ecology

Microbial ecology examines the complex web of interactions and diversity among microorganisms within various ecosystems. This field seeks to understand how microbial populations adapt to and influence their environments and how these interactions shape broader ecological processes. Microbes are integral to ecosystem function, participating in nutrient cycling, energy flow, and the maintenance of environmental homeostasis.An ecosystem represents a dynamic interaction between living organisms...
Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
Microenvironments01:22

Microenvironments

Microorganisms inhabit highly localized spaces known as microenvironments, which are defined by distinct physical and chemical characteristics. These include oxygen concentration, pH, temperature, light availability, and nutrient levels. The conditions within a microenvironment can differ markedly from those in the surrounding area and significantly influence microbial growth, metabolism, and community structure.Microenvironments often display sharp physicochemical gradients over small spatial...
Marine Microbial Ecology01:30

Marine Microbial Ecology

Marine microbial ecosystems are shaped by distinct physicochemical limits, including high salinity, low nutrient availability, and fluctuating oxygen levels. These conditions favor smaller microbial cell sizes, which maximize their surface-to-volume ratio for efficient nutrient uptake.Microbial activity and community composition are closely linked to biogeochemical cycles, particularly in dynamic environments like estuaries, where halotolerant microbes thrive in response to variable salinity...
Deep Sea Microbial Ecology01:18

Deep Sea Microbial Ecology

The deep ocean and its underlying sediments represent vast, largely unexplored microbial habitats that extend far beyond the sunlit photic zone. The photic (euphotic) zone typically spans the upper ~100–200 meters of pelagic waters in the open ocean, but its depth varies geographically and seasonally, where sufficient light supports photosynthetic life. Below this lies the deep sea, spanning roughly 1000–6000 meters (bathypelagic to abyssal zones), with deeper hadal trenches extending beyond...

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使用MDSINE2从微生物组数据中学习生态系统规模的动态.

Travis E Gibson1,2,3,4, Younhun Kim5,6,7, Sawal Acharya5

  • 1Division of Computational Pathology, Brigham and Women's Hospital, Boston, MA, USA. tegibson@bwh.harvard.edu.

Nature microbiology
|September 9, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了微生物动态系统推理引擎2 (MDSINE2),这是贝叶斯的方法,可以从时间序列数据中创建微生物生态系统的可解释模型. MDSINE2提供了对肠道微生物群相互作用的见解,优于现有的方法.

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科学领域:

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

背景情况:

  • 动态系统模型对于分析微生物生态系统非常有价值.
  • 从复杂的微生物组数据中学习和解释这些模型带来了重大挑战.

研究的目的:

  • 介绍微生物动态系统推理引擎2 (MDSINE2),这是一个贝叶斯的方法学习紧和可解释的生态系统规模的动态系统模型.
  • 解决微生物组时间序列数据分析和解释模型输出方面的挑战.

主要方法:

  • MDSINE2将微生物动态模型作为交互模块驱动的随机过程.
  • 模型包含数据的噪声特征.
  • 开发了一套开源软件包,其中包含用于模型解释的工具 (模块的族系/分类学,稳定性,交互拓,关键性).

主要成果:

  • MDSINE2是使用微生物组时间序列数据从被干扰的小鼠队列进行基准测试的.
  • 在学习动态系统模型方面,MDSINE2的性能优于最先进的方法.
  • 确定了交互模块,提供了对肠道微生物群生态系统规模交互的见解.

结论:

  • MDSINE2为建模微生物生态系统动态提供了一种强大且易于解释的方法.
  • 该方法增强了对肠道微生物群相互作用和微生物群体行为的理解.