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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

26
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...
26
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
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...
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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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相关实验视频

Updated: Jun 1, 2025

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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在上游生物工艺模型中减少结构性不可识别性,使用概率概况.

Heiko Babel1, Ola Omar1, Albert Paul1

  • 1Boehringer Ingelheim Pharma GmbH & Co.KG, Biopharmaceuticals Germany, Biberach an der Riß, Germany.

Biotechnology and bioengineering
|January 18, 2025
PubMed
概括

概率分析通过提高参数确定性来增强生物制药过程模型,即使数据有限. 这种方法减少了过程开发,优化和扩展中的不确定性,最大限度地降低了预测中的风险.

科学领域:

  • 生物制药工艺开发 生物制药过程开发
  • 计算建模 计算建模
  • 系统生物学 系统生物学

背景情况:

  • 工艺模型对于生物制药上游开发至关重要,有助于优化,扩展和减少实验工作.
  • 参数非结构化模型由于最小的数据要求是有希望的,但参数估计确定性至关重要.
  • 参数估计的不确定性会影响模型预测,并增加相关风险,需要强大的估计方法.

研究的目的:

  • 在生物制药上游工艺模型中应用概率概率来确定参数可识别性.
  • 调查数据量对参数识别能力的影响.
  • 利用概率概况来识别和实施结构模型改进.

主要方法:

  • 应用概率分析来评估参数的识别性.
  • 调查不同数据量对模型识别能力的影响.
  • 对不可识别的参数进行概率概况分析,以指导模型结构变化.

主要成果:

  • 发现数据量增加在上游过程模型中减少了不可识别性.
  • 概率概况显示了结构模型的变化,有效地解决了大多数参数的不可识别性.
  • 在21个参数中,只有一个在模型调整后仍然无法识别.
关键词:
生物制药制品 生物制药制品模拟建模模型的使用方法可以识别参数的识别性.概率概率概率概率概率

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Split-BioID — Proteomic Analysis of Context-specific Protein Complexes in Their Native Cellular Environment
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Split-BioID — Proteomic Analysis of Context-specific Protein Complexes in Their Native Cellular Environment

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相关实验视频

Last Updated: Jun 1, 2025

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High-Throughput Metabolic Profiling for Model Refinements of Microalgae

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Split-BioID — Proteomic Analysis of Context-specific Protein Complexes in Their Native Cellular Environment

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结论:

  • 概率概率是在上游过程模型中确定参数置信区间的一个非常合适的方法.
  • 该方法为非线性模型提供可靠的参数估计,即使数据有限.
  • 这项研究首次将概率概率应用到一个完整的上游工艺模型中,证明了它的有效性.