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相关概念视频

Cell Signaling Feedback Loops01:07

Cell Signaling Feedback Loops

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Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
Negative feedback loops
Most signaling systems have negative feedback loops that can perform different functions such as output limiter, and adaptation.
Output limiter
Upon receiving an input signal, the cellular response rapidly increases until a threshold is reached. Beyond this threshold, a negative feedback loop...
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Positive and Negative Feedback Loops01:18

Positive and Negative Feedback Loops

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Animal organs and organ systems constantly adjust to internal and external changes through a process called homeostasis ("steady state"). Examples of these changes include regulation of the level of glucose or calcium in the blood or internal responses to external temperatures. Homeostasis requires  maintaining an internal dynamic equilibrium:
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Root Loci for Positive-Feedback Systems01:23

Root Loci for Positive-Feedback Systems

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The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
The construction rules for the root locus in positive feedback systems are similar to those in...
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Feedback Loops01:01

Feedback Loops

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In most cases, excessive hormone production is prevented by negative feedback—a loop that starts with a stimulus inducing the release of a particular substance, like a hormone, to maintain a certain level before triggering a signal that results in a decrease in further release of the hormone.
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Operon Model01:23

Operon Model

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The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
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相关实验视频

Updated: Sep 10, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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基因网络结构和动态:识别简单的负反循环

Theodore J Perkins1, Roderick Edwards2, Leon Glass3

  • 1Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.

Interface focus
|August 27, 2025
PubMed
概括
此摘要是机器生成的。

研究人员开发了从观察到的细胞动态中识别基因相互作用的方法. 这种方法分析遗传网络模型,特别是简单的负反系统,从数据模式中推断相互作用.

关键词:
布尔交换网络遗传网络反向问题非线性动力学

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Reliably Engineering and Controlling Stable Optogenetic Gene Circuits in Mammalian Cells
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相关实验视频

Last Updated: Sep 10, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Reliably Engineering and Controlling Stable Optogenetic Gene Circuits in Mammalian Cells
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科学领域:

  • 系统生物学
  • 计算生物学
  • 遗传学

背景情况:

  • 基因相互作用调节细胞的基本过程,如分化和新陈代谢.
  • 实验研究通常与复杂的模型相结合,以了解这些相互作用.
  • "反向问题"旨在仅从观察到的系统动态推断基因相互作用.

研究的目的:

  • 扩展现有分析遗传网络动态的方法.
  • 将这些方法应用于卡明斯及其同事提出的具体模型.
  • 通过观察到的系统行为来确定基因相互作用.

主要方法:

  • 分析普通微分方程作为布尔交换网络的连续类型.
  • 基于逻辑结构的动态的分类.
  • 应用技术来解决基因网络的反向问题.
  • 分析简单的负反系统与循环相互作用图和奇数的抑制环节.
  • 通过从时间序列数据中分析最大值和最小值的序列来推断网络结构.
  • 基于第一个导数来确定逻辑状态的动力学分离.
  • 评估每个变量的变化率与其他变量的依赖性.

主要成果:

  • 对于简单的负反系统,如果精确采样,可以从时间序列数据中推断出网络结构.
  • 最大值和最小值的序列提供了一种结构确定方法.
  • 基于第一个导数的分离动力学提供了一个替代方法.
  • 分析一个变量的变化率对其他变量的依赖是关键技术.

结论:

  • 开发的方法有效地扩展了遗传网络动态的分析.
  • 这些技术适用于遗传网络的模型方程.
  • 准确的时间序列数据和适当的分析方法可以推断基因相互作用网络.