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相关概念视频

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

254
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
254
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
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...
126
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
149
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

884
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
884
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
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...
86
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

110
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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使用贝叶斯的多模型推理在系统生物学模型中增加确定性.

Nathaniel Linden-Santangeli1, Jin Zhang2, Boris Kramer3

  • 1Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, USA.

Nature communications
|August 12, 2025
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概括
此摘要是机器生成的。

贝叶斯的多模型推理 (MMI) 通过结合细胞内信号网络的多个模型来提高系统生物学预测的确定性. 这种方法提高了对数据不确定性和模型集变化的稳定性,用于信号通路分析.

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相关实验视频

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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
  • 数学建模的数学建模

背景情况:

  • 细胞内信号网络是复杂的,需要数学模型来研究.
  • 通常使用现象学近似,导致同一途径的多个模型.
  • 模型选择和预测确定性受到多个可能不完整的模型的挑战.

研究的目的:

  • 研究贝叶斯多模型推理 (MMI) 以提高系统生物学预测的确定性.
  • 为了提高预测能力,利用一组潜在的不完整模型.
  • 为了确定细胞下特定位置的细胞外调节激酶 (ERK) 活动的机制.

主要方法:

  • 应用贝叶斯的多模型推断 (MMI) 对现有的细胞外调节激酶 (ERK) 途径模型.
  • 评估了MMI预测器对模型集变化和数据不确定性的稳定性.
  • 利用MMI来分析实验测量的细胞下特定位置ERK活动.

主要成果:

  • MMI成功地结合了ERK路径的多个模型.
  • MMI产生了可靠的预测器,用于建模集合变化和数据不确定性.
  • 通过MMI,可以更容易地识别潜在的机制,这些机制是ERK特定地点活动的基础.

结论:

  • 贝叶斯的多模型推理是一种有纪律的方法,用于提高系统生物学预测的确定性.
  • MMI提供了一个强大的方法来整合来自多个,可能不完整的路径模型的信息.
  • 这项工作证明了MMI在剖析复杂的信号动态,如特定位置的ERK活动中的实用性.