Jove
Visualize
联系我们
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

相关概念视频

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

45
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...
45
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

74
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...
74
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

57
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...
57
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

151
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,...
151
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

107
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...
107
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

70
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
70

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

GTRspmix: Capturing Heterogeneity of Exchangeabilities Across Sites to Improve Protein Phylogenetics.

bioRxiv : the preprint server for biology·2026
Same authorSame journal

Modeling Site-and-Branch-Heterogeneity with GFmix.

Systematic biology·2026
Same author

IQ-TREE 3: phylogenomic inference software using complex evolutionary models.

Molecular biology and evolution·2026
Same author

How Does Transcription-Associated Mutagenesis Shape tRNA Microevolution?

Genome biology and evolution·2026
Same author

Allelic Variation at tRNA Genes in Three Nematode Species Indicates Mutation Load Despite Strong Purifying Selection.

Genome biology and evolution·2026
Same author

Comparing partition and mixture models with akaike information criteria.

Systematic biology·2026
Same journal

Diversification dynamics in the global radiation of gobies.

Systematic biology·2026
Same journal

Correction to: nQMaker: Estimating Time Nonreversible Amino Acid Substitution Models.

Systematic biology·2026
Same journal

Phylogenomic challenges in polyploid-rich lineages: Insights from paralog processing and reticulation methods using the complex genus Packera (Asteraceae: Senecioneae).

Systematic biology·2026
Same journal

An evolving view of phylogenetic biogeography.

Systematic biology·2026
Same journal

Coalescent-based branch length estimation improves dating of species trees.

Systematic biology·2026
查看所有相关文章

相关实验视频

Updated: Jul 13, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.6K

过度参数化是配置文件混合模型的一个问题吗?

Hector Baños1,2,3, Edward Susko2,3, Andrew J Roger1,3

  • 1Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada.

Systematic biology
|October 16, 2023
PubMed
概括
此摘要是机器生成的。

配置混合模型有效地估计了蛋白质进化,即使具有许多参数. 过度参数化对于这些模型来说不是一个问题,即使在短时间的对齐中,也提高了家族遗传学准确性.

关键词:
频率特征混合物 频率特征混合物长.分支吸引力吸引力混合物模型模型的混合物模型人类遗传学 遗传学

更多相关视频

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

3.8K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

相关实验视频

Last Updated: Jul 13, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.6K
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

3.8K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

科学领域:

  • 计算生物学 计算生物学
  • 人类遗传学 是一个学科.
  • 生物信息学是一种生物信息学.

背景情况:

  • 由于生化约束,蛋白质序列进化表现出特定位点的异质性.
  • 遗传学模型忽视了遗址异质性的风险长枝吸引 (LBA) 文物.
  • 配置混合模型使用具有不同氨基酸频率的站点类来解决异质性.

研究的目的:

  • 为了调查配置混合模型中的过度参数化是否会影响家族遗传树拓学的估计.
  • 为了评估不同数量的站点类别和对齐长度的配置混合模型的性能.
  • 评估错误指定的参数对模型准确性和LBA文物的影响.

主要方法:

  • 对长序列的参数收的理论分析.
  • 使用短对齐与易患LBA的拓学的模拟研究.
  • 探索具有和没有总体氨基酸频率类 (F类) 的配置混合模型.

主要成果:

  • 过度参数化并不妨碍对形状混合模型的估计,即使具有多个组件,特别是对于较长的序列.
  • 复杂的配置混合模型具有许多站点类比更简单的模型更好地执行,即使是在短的,易于LBA的对齐上.
  • 如果累积分布是准确的,对氨基酸频率向量的错误指定的影响是最小的,但错误指定的可交换率严重影响了估计.
  • 额外的F类通常不会改善参数估计,有时可以降低树估计准确度,尽管提高了概率得分.

结论:

  • 配置文件混合模型在蛋白质进化的基因推断中对过度参数化具有强大耐用性.
  • 模型的复杂性和氨基酸频率和可交换率的准确估计对于可靠的家族遗传结果至关重要.
  • 将总体氨基酸频率类 (F类) 纳入其中并不总是有益的,甚至可能对树拓估计有害.