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相关概念视频

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Probability Laws01:49

Probability Laws

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Overview
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Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

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Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Aggregation for Computing Multi-Modal Stationary Distributions in 1-D Gene Regulatory Networks.

IEEE/ACM transactions on computational biology and bioinformatics·2017
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相关实验视频

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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在生化网络中有效的概率推理.

Adrien Le Coënt1, Benoît Barbot1, Nihal Pekergin1

  • 1Université Paris Est Créteil, LACL, F-94010 Creteil, France.

Computers in biology and medicine
|October 26, 2024
PubMed
概括

这项研究引入了动态贝叶斯网络来近似生化网络,使有效的参数估计. 这种计算方法可以提高复杂的生物系统的准确性,例如细胞信号通路.

科学领域:

  • 计算生物学 计算生物学
  • 系统生物学 系统生物学
  • 生物化学 生物化学

背景情况:

  • 生物化学网络通常使用普通微分方程 (ODEs) 建模.
  • 对于ODE模型的参数估计是计算密集的,往往导致效率低下或不准确.
  • 这些模型涉及许多变量和参数,使分析复杂化.

研究的目的:

  • 为生物化学网络提出一个替代的建模方法.
  • 为了提高参数估计的计算效率和准确性.
  • 将新方法应用于现实世界的生物系统.

主要方法:

  • 使用动态贝叶斯网络 (DBNs) 进行近似生物化学网络,这是离散概率模型的一类.
  • 在 DBN 框架内使用贝叶斯推理进行参数估计.
  • 开发战略以优化近似和估计过程的准确性和计算性能.

主要成果:

  • 证明DBNs可以有效地接近复杂的生物化学网络.
  • 与传统的ODE方法相比,展示了贝叶斯推理对参数估计的效率和准确性收益.
  • 成功地将DBN方法应用于EGF-NGF蜂信号通路.
关键词:
贝叶斯网络是贝叶斯网络.生物化学网络 生物化学网络马尔科夫链是一种马尔科夫链.基于普通微分方程的模型.参数估计的参数估计.时间均系统是时间均系统.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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相关实验视频

Last Updated: Jun 9, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

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结论:

  • 动态贝叶斯网络为建模生化网络提供了一个计算效率高,准确的替代方案.
  • 贝叶斯推理在这些近似模型中提供了一个强大的参数估计工具.
  • 拟议的方法具有促进系统生物学研究的巨大潜力,特别是分析复杂的信号通路.