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相关概念视频

Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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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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Modeling with Differential Equations01:25

Modeling with Differential Equations

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Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
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相关实验视频

Updated: May 2, 2026

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy
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Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy

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单细胞动态数据的计算建模.

Wenbo Guo1, Zeyu Chen, Jin Gu1

  • 1MOE Key Lab of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, No. 30 Shuangqing Road, Haidian District, Beijing 100084, China.

Briefings in bioinformatics
|June 30, 2025
PubMed
概括
此摘要是机器生成的。

了解细胞动态对于生命科学和医学至关重要. 本综述涵盖了分析动态单细胞测序数据的计算挑战和算法解决方案,以探索复杂的生物过程.

关键词:
算法算法是一种算法.机器学习是机器学习.单细胞动力学 单细胞动力学时间序列数据数据时间序列数据

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Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
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Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics
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相关实验视频

Last Updated: May 2, 2026

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Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
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Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics
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科学领域:

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

背景情况:

  • 单细胞测序技术可以随着时间的推移进行动态测量.
  • 分析动态单细胞数据带来了重大的计算挑战.

研究的目的:

  • 审查分析动态单细胞数据的挑战.
  • 概述了用于表征细胞动态的算法进步.
  • 讨论未来的方向,整合技术和人工智能.

主要方法:

  • 阐述实验限制,数据特征和生物发现挑战.
  • 对推断细胞动态,剖析机制,预测命运和整合谱系追踪的算法的概述.
  • 讨论生物技术和人工智能的进展.

主要成果:

  • 确定动态单细胞数据分析中的关键挑战.
  • 对表征细胞动态的四个关键任务的算法解决方案的分类.
  • 突出新兴技术和人工智能的潜力.

结论:

  • 解决计算挑战对于推进细胞动态研究至关重要.
  • 算法进步是从动态单细胞数据中解锁洞察力的关键.
  • 人工智能和先进生物技术的未来整合将加强对生命过程的时空探索.