通过预测性信息分解来研究生理网络中的动态高阶相互作用
概括
本研究引入了一种新的信息理论方法来分析复杂的网络相互作用. 该方法量化了生理系统中的冗余和协同动态,揭示了对心肺和心血管调节的洞察力.
科学领域:
- 生理学 生理学 生理学
- 信息理论 信息理论
- 网络科学 网络科学
背景情况:
- 了解生理系统内的复杂相互作用对于诊断健康状况至关重要.
- 现有的方法可能无法完全捕捉多变体生理过程的微妙相互作用.
- 信息理论为量化动态系统中的关系提供了强大的工具.
研究的目的:
- 开发和验证信息理论框架,以评估网络系统中的冗余和协同相互作用.
- 将共享的信息分解为系统子组的独特,冗余和协同贡献.
- 将这种方法应用于生理学数据,以更深入地了解调节机制.
主要方法:
- 利用信息理论分析来量化多变量生理过程中共享的信息.
- 根据子组动态开发了一种将信息分解为独特,多余和协同组件的策略.
- 用线性相互作用的高斯过程说明了该方法,并将其应用于人类心肺呼吸系统数据.
主要成果:
- 高斯过程中的冗余性和协同性与单向和双向合相对应.
- 在健康受试者中,冗余性主导心血管相互作用,而协同作用在休息时的心血管相互作用中是显著的.
- 姿势应激增强生理相互作用中的预测信息和冗余性.
结论:
- 建议的信息理论方法有效量化了网络系统中的复杂相互作用.
- 冗余和协同作用在生理调节中起着不同的作用,根据系统类型和生理状态而有所不同.
- 这一框架为心肺和心血管控制的动态提供了新的见解.
更多相关视频
10:44Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
2.2K
07:28JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
3.2K
相关概念视频
Protein Networks
4.0K
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,...
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,...
4.0K
Pharmacokinetic Models: Comparison and Selection Criterion
74
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
74
Pharmacokinetic Models: Overview
709
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
709
Protein-protein Interfaces
12.5K
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...
12.5K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
71
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
71
Mechanistic Models: Overview of Compartment Models
86
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
86
