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相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
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...
64
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

111
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...
111
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

79
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
79
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

130
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
130
Uncertainty: Overview00:59

Uncertainty: Overview

595
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
595
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
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...
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相关实验视频

Updated: Jul 16, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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先进的错误建模和贝叶斯的不确定性量化在机械液色谱建模的机械液色谱建模.

William Heymann1, Juliane Glaser2, Fabrice Schlegel3

  • 1Institute of Bio- and Geosciences (IBG-1), Forschungszentrum Jülich, Wilhelm-Johnen-Str., Jülich 52428, Germany; RWTH Aachen University, Aachen 52062, Germany; Operations Digital Technology and Innovation Process Development (Ops DTI PD), Amgen Research Munich, Staffelseestr. 2, München 81477, Germany.

Journal of chromatography. A
|September 15, 2023
PubMed
概括

本研究引入了一种新的方法,通过考虑现实的实验错误来量化色谱过程模型中的不确定性. 这提高了模型参数和预测的可靠性,以改善流程理解.

关键词:
贝叶斯的不确定性量化方法在CADET CADET中使用.染色学建模的染色学建模错误建模中的错误建模

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Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
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相关实验视频

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 过程建模过程建模
  • 染色体学 染色体学 是一种染色学.

背景情况:

  • 机械色谱过程模型往往忽略了检测器噪声以外的实验错误.
  • 模型参数和预测的不确定性可能来自于诸如延迟和可变料成分等因素.
  • 准确的不确定性量化对于可靠的过程模拟和优化至关重要.

研究的目的:

  • 开发和展示机械色谱过程模型的不确定性量化方法.
  • 将现实的实验错误,如延迟和可变的料组成,纳入模型校准.
  • 确定校准模型参数的概率分布及其对预测色谱的影响.

主要方法:

  • 采用了贝叶斯定理和马尔科夫链蒙特卡洛 (MCMC) 与集体采样器.
  • 该方法通过确定模型参数的概率分布来量化不确定性.
  • 这种方法解释了实验错误的多个现实的来源.

主要成果:

  • 开发的不确定性量化方法证明了它的稳定性和可扩展性.
  • 该方法使用合成和工业染色学数据进行了验证.
  • 成功确定了模型参数的概率分布及其对预测的影响.

结论:

  • 提出的方法有效地解决了当前染色学建模在实验误差方面的局限性.
  • 这种方法通过提供参数和预测的不确定性估计来提高机械模型的可靠性.
  • 开源软件的实现有助于在染色学过程开发中的更广泛的采用和应用.