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

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

246
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...
246
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

238
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...
238
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

328
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.
328
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

282
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...
282
Clearance Models: Physiological Models01:09

Clearance Models: Physiological Models

288
Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's...
288
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

330
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
330

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

Auditable cross-instrument detection of unusual multivariate psychiatric response configurations using a semantically aligned covariance subspace.

medRxiv : the preprint server for health sciences·2026
Same author

Foundation Model for Biological Temporal Data Dynamics with Experimental Validation.

Research square·2026
Same author

Enhanced stability and root detection in a derivative-free Steffensen algorithm for nonlinear dynamical systems.

Chaos (Woodbury, N.Y.)·2026
Same author

Topological Entropy Correlates with the Predictive Power of Multiplexed Ensemble Reservoir Computing.

bioRxiv : the preprint server for biology·2026
Same author

Forecasting drug resistant HIV protease evolution.

PLoS computational biology·2026
Same author

Variational Garrote for Statistical Physics-based Sparse and Robust Variable Selection.

ArXiv·2026
Same journal

Gap junction communication regulates luminal-myoepithelial crosstalk and cell differentiation in a bilayered human mammary epithelial cell model.

Biology methods & protocols·2026
Same journal

Nonlinear combinatorial analysis of blood transcriptomes identifies PRKAR1A as a regulator of TDP-43 pathophysiology in amyotrophic lateral sclerosis.

Biology methods & protocols·2026
Same journal

Assessing the feasibility of machine learning for ancient DNA age prediction: Limitations and insights.

Biology methods & protocols·2026
Same journal

Advances in the detection of antimicrobial resistance in aquatic environments: a methodological perspective.

Biology methods & protocols·2026
Same journal

sc-rDSeq: Droplet-based single-cell full-length total RNA-seq method.

Biology methods & protocols·2026
Same journal

Discovery of novel InhA inhibitors through structural bioinformatics and machine learning-driven QSAR screening of natural products.

Biology methods & protocols·2026
查看所有相关文章

相关实验视频

Updated: Jan 14, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

深度学习方法用于对生理模型的参数优化.

Xiaoyu Duan1, Vipul Periwal1

  • 1Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Bethesda, MD, 20894, United States.

Biology methods & protocols
|October 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种用于生物数据建模的新型深度学习框架,改善了非线性系统中的参数推理. 该方法使用神经网络准确地重建生理动态,增强模型评估和生物参数约束.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.功能工程的特点工程.脂肪溶解是指脂质分解的过程.参数推断的推断是指参数推断.生理学参数 生理学参数

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

相关实验视频

Last Updated: Jan 14, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

科学领域:

  • 计算生物学 计算生物学
  • 系统生物学 系统生物学
  • 生物物理学的生物物理.

背景情况:

  • 在生物数据建模中推断非线性动态和参数是具有挑战性的.
  • 标准参数优化方法与非线性模型的生物范围约束作斗争.

研究的目的:

  • 提出一种使用神经网络进行生物建模,参数化和参数推理的新方法.
  • 通过同时解决这些挑战来评估和改进假定模型.
  • 适应深度学习框架用于各种生理系统中的参数推理.

主要方法:

  • 利用临床常用样本静脉注射葡萄糖耐受性测试数据.
  • 介绍了葡萄糖,胰岛素和自由脂肪酸动态的两个生理性脂解模型.
  • 在模拟的时间过程数据上训练了一个卷积神经网络,用于参数推理.

主要成果:

  • 经过训练的神经网络在不同环境中实现了准确的参数推断和轨迹重建.
  • 观察到始终高的R平方值和低的P值.
  • 特性工程和培训数据集大小影响了推断性能,特定的选择提高了准确性.

结论:

  • 建立了一个深度学习框架,用于数学模型中的参数推理.
  • 证明了框架对各种生理系统的适应性.
  • 展示了改善生物数据建模和参数估计的潜力.