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相关概念视频

Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
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Updated: May 5, 2026

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
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从单细胞数据重建基因网络结构和动态.

Feng Chen1,2, Chunhe Li1,2,3

  • 1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.

Bioinformatics (Oxford, England)
|November 3, 2025
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概括

GGANO是一个新的框架,使用高斯图形模型和神经常规微分方程,从杂的单细胞数据中准确推断基因调节网络 (GRNs). 它揭示了对细胞命运决定和疾病进展的关键见解.

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科学领域:

  • 系统生物学 系统生物学
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 基因调节网络 (GRNs) 控制着生物功能.
  • 从高维度,杂的单细胞数据推断GRNs是一个挑战.
  • 现有的方法缺乏对复杂的生物过程的稳定性和解释性.

研究的目的:

  • 为GRN推理开发一个强大的和可解释的框架.
  • 为了使动态建模和推断从单细胞数据.
  • 揭示细胞命运决定和疾病中的调节机制.

主要方法:

  • 提出了GGANO,这是一个融合高斯图形模型 (GGM) 和神经常规微分方程 (NODE) 的混合框架.
  • 条件独立学习的GGM.
  • 用于动态建模和推理的NODE.

主要成果:

  • 与现有方法相比,GGANO的准确性和稳定性更高,尤其是在高噪音条件下.
  • 能够从单细胞数据中推断随机动态.
  • 鉴定了表皮细胞-介质细胞过渡 (EMT) 中的中间细胞状态和关键调节基因.

结论:

  • GGANO提供了一种强大的新方法,用于从单细胞数据中推断GRN.
  • 该框架增强了对复杂的生物过程的理解,如细胞命运决定和疾病.
  • GGANO为系统生物学研究提供了有价值的工具.