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相关概念视频

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

72
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...
72
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
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

103
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.
103
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
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

178
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,...
178
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

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Updated: Jul 16, 2025

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
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基于约束的建模中枢性优化:对人类新陈代谢的应用.

Ronan M T Fleming1,2,3, Hulda S Haraldsdottir2, Le Hoai Minh2

  • 1Metabolomics and Analytics Center, Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, Leiden 2333 CC, The Netherlands.

Bioinformatics (Oxford, England)
|September 12, 2023
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概括

我们开发了新的算法来解决基于约束的建模中复杂的核心优化问题. 我们的方法有效地找到生物化学网络分析的近似解决方案,改进现有方法.

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科学领域:

  • 计算生物学 计算生物学
  • 优化优化 优化优化
  • 系统生物学 系统生物学

背景情况:

  • 核心优化问题在基于约束的建模中对于如一致性测试和稀疏解决方案计算等任务至关重要.
  • 对于这些计算复杂问题的现有方法往往缺乏在多项式时间内准确和全球最佳的解决方案.

研究的目的:

  • 在基于约束的建模中,将枢能数优化问题重新阐述为凸函数的差异.
  • 开发和测试新的算法,以大致解决这些重构的问题.

主要方法:

  • 通过非凸连续函数对零规范进行近似,以转换枢纽性优化问题.
  • 采用一系列凸的程序来代地解决重构的问题.
  • 将算法应用于生物化学网络,包括人类代谢重建.

主要成果:

  • 实施了新的算法并进行了数值测试,证明了效率和实际实用性.
  • 开发的算法与基于约束的建模中对枢性优化的现有相关方法匹配或优于现有的相关方法.
  • 成功地从人类代谢重建中提取热力学流量平衡分析模型的应用.

结论:

  • 拟议的方法提供了一种有效的方法来解决基于约束的建模中具有挑战性的枢密度优化问题.
  • 这些算法为分析生化网络和提取相关模型提供了实用和高效的解决方案.
  • 开源实现可用于可重现性和集成到现有的建模工作流.