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相关概念视频

Bioreactor Design and Operational System01:29

Bioreactor Design and Operational System

220
Bioreactors are engineered vessels designed to cultivate microorganisms under controlled conditions for industrial bioprocessing. They maintain sterility and allow precise regulation of pH, temperature, oxygen, and nutrient levels to optimize microbial growth and metabolite production. Bioreactors range from small laboratory units of 1 liter to industrial systems holding up to 500,000 liters, though only about 75% of their volume is actively used for fermentation. The remaining headspace...
220
Bioreactor Controls-II01:18

Bioreactor Controls-II

82
In aerobic fermentations, oxygen is vital for microbial growth and metabolite production. Since air comprises only about 20% oxygen and the gas is poorly soluble in water—just 9 ppm at 20°C—supplying sufficient oxygen becomes a critical challenge, especially in high-demand processes like yeast growth or citric acid production. Even a fully saturated broth may offer only a few seconds of oxygen availability.To address this, sterile or scrubbed air is introduced into the...
82
Bioreactor Controls-III01:22

Bioreactor Controls-III

70
Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
70
Designing Growth Media for Bioreactors01:30

Designing Growth Media for Bioreactors

84
Growth media provide essential nutrients that support cell growth and metabolism, thereby enhancing the yield of valuable products such as enzymes, antibiotics, and biomass. Designing an effective growth medium involves balancing all components to prevent nutrient limitations or toxic excesses, both of which can impair growth and reduce product yields.Composition of a Typical Growth MediumA typical growth medium contains carbon and nitrogen sources, salts, vitamins, trace elements, and...
84
Scale-Up Processes01:14

Scale-Up Processes

119
The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...
119
Upstream Processing01:27

Upstream Processing

102
Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...
102

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相关实验视频

Updated: May 5, 2026

Power Input Measurements in Stirred Bioreactors at Laboratory Scale
10:49

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机器学习在生物反应器扩展中的视角驱动和技术评估:对潜在模型开发的案例研究.

Masih Karimi Alavijeh1,2, Yih Yean Lee3, Sally L Gras1,2

  • 1Department of Chemical Engineering The University of Melbourne Parkville Victoria Australia.

Engineering in life sciences
|July 8, 2024
PubMed
概括

机器学习 (ML) 模型显示出预测生物反应器扩展参数在生物制药开发中的前景. 这种方法有助于优化细胞生长和缩放过程,以生产单克隆抗体.

关键词:
生物处理生物处理.生物反应器是一个生物反应器.基于数据的建模.机器学习是机器学习.哺乳动物细胞的细胞

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相关实验视频

Last Updated: May 5, 2026

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科学领域:

  • 生物制药制造业 生物制药制造业
  • 工艺工程是过程工程.
  • 计算生物学是一种计算生物学.

背景情况:

  • 生物反应器扩展是生物制药行业的一个关键挑战,影响了过程开发和效率.
  • 传统的扩展战略往往缺乏稳定性,需要创新的方法来实现可靠的跨规模流程传输.
  • 数字化转型为通过先进的建模技术优化生物工艺开发提供了新的途径.

研究的目的:

  • 评估机器学习 (ML) 算法的生物反应器扩展应用,重点预测关键扩展参数.
  • 确定开发有效的ML模型的关键因素,以扩大生物工艺的规模.
  • 评估ML在提高预测细胞生长和生物反应器系统中的缩放参数方面的潜力.

主要方法:

  • 从文献和公共来源收集的数据用于生物反应器扩大规模的研究,涉及中国汉姆斯特卵巢 (CHO) 细胞生成的单克隆抗体 (mAb) 产品.
  • 开发了无监督和监督的ML模型,包括嵌入层的人工神经网络.
  • 进行了案例研究,分析了细胞生长和尺度敏感生物反应器特征之间的关系.

主要成果:

  • 在各种规模的生物反应器性能中确定了相似之处和差异,特别是在小规模和大规模系统之间.
  • 嵌入层通过捕捉过程相似性,显著提高了人工神经网络模型对大规模细胞生长的预测能力.
  • 开发了能够预测关键扩展参数的ML模型,证明了ML在协助扩展过程中的实用性.

结论:

  • 机器学习算法提供了一种强大的工具,可以增强生物反应器扩展战略在生物制药行业.
  • 开发的ML模型可以预测细胞生长和缩放参数,促进更有效的过程开发.
  • 未来的进步需要更大,更多样化的数据集,在各种操作条件下进行全面的表征,以进一步完善生物反应器缩放的ML工具.