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

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

Towards the construction of a virtual yeast.

Nature·2026
Same author

Dissecting oral premalignant carcinogenesis: spatial omics mechanisms and nanomedicine-driven therapeutic innovation.

Molecular cancer·2026
Same author

Determination and Ecological Risk Assessment of Organophosphate Esters in Drinking and Environmental Waters by Automated Liquid-Liquid Extraction Coupled with GC-MS/MS.

Molecules (Basel, Switzerland)·2026
Same author

Interactions between dissolved organic matter of different molecular weights and hexabromocyclododecanes.

Marine environmental research·2026
Same author

Response of Benthic Foraminifera to Cadmium Pollution Assessed via Morphological and Metabarcoding Analyses.

Microorganisms·2026
Same author

A novel HN-AD strain Paenibacillus glycanilyticus DQ-1 with high-efficiency nitrogen removal capacity: genomic insights into its nitrogen metabolic mechanism.

BMC microbiology·2026
Same journal

Metabolic set theory: a generalized model of microbial interactions.

NPJ systems biology and applications·2026
Same journal

Gene prioritization across ancestries uncovers distinct molecular pathophysiology and therapeutic landscape in polycystic ovary syndrome.

NPJ systems biology and applications·2026
Same journal

A mathematical model of folate-mediated one-carbon metabolism in Down syndrome.

NPJ systems biology and applications·2026
Same journal

A minimal mechanically consistent model of smoothly dividing disk-shaped cells.

NPJ systems biology and applications·2026
Same journal

Virtual twins and the future of human developmental biology.

NPJ systems biology and applications·2026
Same journal

Characterizing open-ended evolution through undecidability mechanisms in random Boolean networks.

NPJ systems biology and applications·2026
查看所有相关文章

相关实验视频

Updated: Jan 9, 2026

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations
10:55

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Published on: December 16, 2017

9.1K

使用时空空间单细胞转录组数据解读细胞酸盐轨迹.

Zhenyi Zhang1, Zihan Wang2, Yuhao Sun3

  • 1LMAM and School of Mathematical Sciences, Peking University, Beijing, China.

NPJ systems biology and applications
|December 4, 2025
PubMed
概括
此摘要是机器生成的。

本综述探讨了使用单细胞和空间奥米克数据进行动态细胞过程的计算建模. 先进的技术揭示了基因表达如何随着时间和空间的变化而变化,增强了我们对细胞生物学的理解.

更多相关视频

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

19.0K
Single Cell Fate Mapping in Zebrafish
07:53

Single Cell Fate Mapping in Zebrafish

Published on: October 5, 2011

13.9K

相关实验视频

Last Updated: Jan 9, 2026

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations
10:55

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Published on: December 16, 2017

9.1K
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

19.0K
Single Cell Fate Mapping in Zebrafish
07:53

Single Cell Fate Mapping in Zebrafish

Published on: October 5, 2011

13.9K

科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 细胞生物学 细胞生物学

背景情况:

  • 细胞过程是动态的,在时间和空间之间发生变化.
  • 单细胞和空间奥米克技术提供高分辨率的基因表达数据.
  • 描述动态细胞状态需要先进的分析方法.

研究的目的:

  • 对时间序列和时空转录数据的最新建模策略进行审查.
  • 突出动态系统,生成模型和生物见解之间的联系.
  • 展示计算工具如何加深对单细胞动态的理解.

主要方法:

  • 对计算建模方法的审查.
  • 时间序列转录组数据的分析.
  • 时间空间转录基因数据的分析.

主要成果:

  • 确定动态转录基因数据的关键建模策略.
  • 强调了动态系统和生成模型的集成.
  • 演示计算工具在发现生物见解中的实用性.

结论:

  • 计算建模对于理解动态细胞过程至关重要.
  • 时间序列和时空转录组数据分析正在迅速发展.
  • 这些方法为单细胞的动态性质提供了更深入的见解.