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相关概念视频

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

494
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...
494
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

242
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...
242
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

456
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
456
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

338
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.
338
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

1.1K
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...
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Fermentation01:29

Fermentation

128.8K
Most eukaryotic organisms require oxygen to survive and function adequately. Such organisms produce large amounts of energy during aerobic respiration by metabolizing glucose and oxygen into carbon dioxide and water. However, most eukaryotes can generate some energy in the absence of oxygen by anaerobic metabolism.
Fermentation is a type of metabolic process that occurs in the absence of oxygen, where organic molecules such as glucose are broken down to produce energy. During this process, the...
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相关实验视频

Updated: Jan 18, 2026

Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism
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Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism

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关于发酵动力学的贝叶斯推论:用频率主义方法进行比较分析.

Xiang Li1, Yen-Han Lin1

  • 1Department of Chemical and Biological Engineering, University of Saskatchewan, Saskatoon, SK, Canada.

Bioresource technology
|January 16, 2026
PubMed
概括

贝叶斯推理改进了发酵动力学模型,与传统的生物和1,3-二醇生产方法相比,提供了更好的参数解释和数据效率.

科学领域:

  • 生物技术是生物技术.
  • 化学工程是化学工程的重要组成部分.
  • 计算生物学 计算生物学

背景情况:

  • 发酵运动建模对于优化生物过程至关重要.
  • 传统的方法,如非线性最小正方形匹配,在参数解释性和数据效率方面存在局限性.
  • 贝叶斯推理为强大的动力模型提供了一个有希望的替代方案.

研究的目的:

  • 为了比较贝叶斯层次模型的性能与频率主义非线性最小平方适合发酵运动模型.
  • 评估这些方法在1,3-propanediol生产的糖醇-葡萄糖共发酵和生物生产的暗发酵中的应用.
  • 评估贝叶斯方法在参数解释性,稳定性和不确定性量化方面的优势.

主要方法:

  • 利用修改后的高压分离函数来建模代谢物度.
  • 实施贝叶斯层次模型用于动力分析.
  • 比较贝叶斯的方法与频率主义的非线性最小正方形匹配.

主要成果:

  • 频率主义方法显示出高计算效率和适用于短期预测的适用性.
  • 贝叶斯方法展示了优越的参数解释性,在有限的数据中具有稳定性,以及增强的不确定性量化.
  • 贝叶斯层次模型提供了更稳定和数据效率高的发酵动力模型.
关键词:
贝叶斯的层次模型是贝叶斯的层次模型.生物气是生物气中的一种.共同发酵的动态马尔科夫链 蒙特卡洛 马尔科夫链不确定性 不确定性

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Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
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In Vivo Monitoring of Transcriptional Activity During Metabolic Transition Using a Bioluminescent Reporter in Yeast
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In Vivo Monitoring of Transcriptional Activity During Metabolic Transition Using a Bioluminescent Reporter in Yeast

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Last Updated: Jan 18, 2026

Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism
07:38

Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism

Published on: September 30, 2018

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Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
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Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

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In Vivo Monitoring of Transcriptional Activity During Metabolic Transition Using a Bioluminescent Reporter in Yeast
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In Vivo Monitoring of Transcriptional Activity During Metabolic Transition Using a Bioluminescent Reporter in Yeast

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结论:

  • 贝叶斯推理为开发可解释,稳定和数据效率高的发酵动力学模型提供了显著的优势.
  • 贝叶斯方法在处理有限的实验数据时特别有用.
  • 这项研究突出了贝叶斯方法在促进发酵过程优化和理解方面的潜力.