Jove
Visualize
联系我们

相关概念视频

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
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...
101
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

87
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...
87
Methods of Classification and Identification01:28

Methods of Classification and Identification

201
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
201
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

228
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
228
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

167
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...
167
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

721
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
721

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Optimal control for anti-abeta treatment in Alzheimer's disease using a reaction-diffusion model.

Journal of the Royal Society, Interface·2026
Same author

ZENN: A thermodynamics-inspired computational framework for heterogeneous data-driven modeling.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Identifiability-Guided Assessment of Digital Twins in Alzheimer's Disease Clinical Research and Care.

bioRxiv : the preprint server for biology·2025
Same author

Integrating multiscale mathematical modeling and multidimensional data reveals the effects of epigenetic instability on acquired drug resistance in cancer.

PLoS computational biology·2025
Same author

Gauss Newton Method for Solving Variational Problems of PDEs with Neural Network Discretizaitons.

Journal of scientific computing·2024
Same author

Bifurcation analysis of a free boundary model of vascular tumor growth with a necrotic core and chemotaxis.

Journal of mathematical biology·2023
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Sep 14, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K

一个系统的计算框架,用于从生物学产生的数学模型中的实用可识别性分析.

Shun Wang1, Wenrui Hao1

  • 1Department of Mathematics, Penn State University, University Park, Pennsylvania, 16802, USA.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|July 22, 2025
PubMed
概括

这项研究引入了一个新的数学框架来评估生物模型中的参数识别性. 它证明了实际的可识别性与费舍尔信息矩阵相关,为模型分析和实验设计提供了更快,更可靠的方法.

科学领域:

  • 系统生物学 系统生物学
  • 数学建模的数学建模
  • 计算生物学 计算生物学

背景情况:

  • 实际识别参数对于可靠的数据驱动生物模型至关重要.
  • 参数估计的不确定性限制了模型预测和决策.
  • 目前用于识别性分析的方法可能是计算密集的.

研究的目的:

  • 开发一种新的数学框架,用于动态生物模型中的实际可识别性分析.
  • 建立一个严格的定义和有效的参数识别评估指标.
  • 提供改善模型可靠性和指导实验设计的方法.

主要方法:

  • 严格地定义了实际的可识别性,并证明了其与费舍尔信息矩阵 (FIM) 可逆性的等价性.
  • 建立了实用和坐标识别之间的关系,引入了一个高效的指标.
  • 纳入了非可识别参数的规范化术语,并开发了最佳的实验设计算法.

主要成果:

  • 证明实际识别性相当于FIM可逆性.
  • 引入了一种有效的可识别性评估指标,优于传统的概率分析方法.
  • 展示了该框架在改善不确定性量化和通过应用指导实验设计方面的有效性.
关键词:
最佳的数据收集最优的数据收集.参数规范化的参数规范化在实践中可识别性.不确定性量化不确定性量化

更多相关视频

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.2K
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.9K

相关实验视频

Last Updated: Sep 14, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.2K
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.9K

结论:

  • 拟议的框架为实际的可识别性分析提供了一种计算效率高和严格的方法.
  • 这种方法提高了生物模型的可靠性,并有助于识别关键可观测变量.
  • 该框架为生物系统中可靠的参数估计提供了明智的实验设计.