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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

331
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...
331
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

309
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...
309
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

388
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
388
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

641
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...
641
Metabolism of Chemolithotrophs01:15

Metabolism of Chemolithotrophs

1.1K
Chemolithotrophs are microorganisms that obtain energy by oxidizing inorganic molecules such as hydrogen gas (H₂), ammonia (NH₃), reduced sulfur compounds (H₂S, S²⁻), and ferrous iron (Fe²⁺). Unlike heterotrophic organisms that rely on organic carbon, chemolithotrophs transfer electrons from these inorganic donors to the electron transport chain (ETC), generating a proton motive force (PMF) that drives ATP synthesis through oxidative phosphorylation.
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Overview of Metabolism01:40

Overview of Metabolism

40.7K
Living cells constantly carry out various chemical reactions which are necessary for their proper functioning. These reactions are interlinked to one another via multiple pathways. The collection of these chemical reactions is known as metabolism.
Plant Metabolism
Sunlight, the primary source of energy in plants, is first absorbed by the chlorophyll pigments present in their leaves. Plants then use this energy to carry out photosynthesis, where water is oxidized into oxygen and carbon dioxide...
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相关实验视频

Updated: Mar 17, 2026

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

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通过联合机器学习和代谢建模来优化生物过程.

Guido Zampieri1, Viktor Sandner2, Suraj Verma3

  • 1School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, United Kingdom; Department of Biology, University of Padova, Padova, Italy.

Metabolic engineering
|March 16, 2026
PubMed
概括

这项研究引入了一种混合建模框架,以加速生物工艺开发. 通过整合数据驱动和机械方法,它可以实现对高效生物制造的机制信息预测.

关键词:
生物加工是一种生物加工.埃舍里希亚大肠杆菌 (Escherichia coli) 是一个大肠杆菌.不同类的表达式 不同类的表达式机器学习 机器学习代谢建模 代谢建模系统生物学 系统生物学

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High-Throughput Metabolic Profiling for Model Refinements of Microalgae
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High-Throughput Metabolic Profiling for Model Refinements of Microalgae

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相关实验视频

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科学领域:

  • 生物技术是生物技术.
  • 代谢工程是代谢工程.
  • 系统生物学 系统生物学

背景情况:

  • 设计-构建-测试-学习循环是生物产品开发中的瓶.
  • 传统的数据驱动或机械模型具有局限性,特别是在稀疏数据的情况下.
  • 混合建模,整合两种方法,为复杂的生物系统提供了优势.

研究的目的:

  • 引入一种新的混合建模框架,将数据驱动和机械方法结合起来.
  • 证明该框架在加速生物工艺开发和优化方面的实用性.
  • 为指导生物制造中的实验设计提供基于机制的预测.

主要方法:

  • 开发了一个混合建模框架,集成数据驱动和机械建模.
  • 将框架应用于大肠杆菌中异质的生产.
  • 研究了诸如诱导剂度,温度和等离子体等实验因素对生产的影响.

主要成果:

  • 确定了影响产生的关键代谢途径和反应.
  • 证明实验因素显著改变了代谢活动.
  • 展示了该框架能够指导实验设计并告知预测模型的能力,即使数据有限.

结论:

  • 混合建模框架加速生物工艺开发,优化生物制造.
  • 这种方法提高了预测准确性和实验效率.
  • 可通用的框架支持概念验证和工业生物生产项目,促进可持续性.