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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

118
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...
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Updated: Sep 19, 2025

Author Spotlight: Advancing Real-Time cAMP Detection in Cells Using cADDis Biosensor
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通过生物学引导的机器学习和动态建模进行文本感知生物传感器设计.

Jonathan Tellechea-Luzardo1, Hector Martin Lazaro1, Christian Fernandez Perez2

  • 1Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), València 46022, Spain.

ACS synthetic biology
|June 3, 2025
PubMed
概括
此摘要是机器生成的。

这项研究开发了一个设计-构建-测试-学习管道,用于为循环生物经济设计全细胞生物传感器. 一个机器学习模型预测了各种条件下的生物传感器性能,优化了它们在生物化学生产中的使用.

关键词:
生物传感器生物传感器上下文依赖性上下文依赖性动态建模的动态建模遗传电路是一个遗传电路.科学机器学习 机器学习合成生物学 合成生物学

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科学领域:

  • 合成生物学 合成生物学
  • 代谢工程是代谢工程.
  • 生物技术是生物技术.

背景情况:

  • 实现全球循环生物经济需要高效,可扩展的生物基化学生产过程.
  • 全细胞生物传感器利用遗传电路,是控制细胞行为和开发高效细胞工厂的关键.
  • 现有的生物传感器模型,如纳灵宁,需要进一步的数据来预测各种应用中的动态反应.

研究的目的:

  • 设计和描述FdeR生物传感器库,以提高生物基生产中的性能.
  • 开发一种基于机械和机器学习的生物传感器动态行为的预测模型.
  • 使用设计-构建-测试-学习管道优化生物传感器设计和应用条件.

主要方法:

  • 组装了一个FdeR生物传感器库,并在各种条件下描述它们的性能.
  • 在参考条件下开发了生物传感器动态行为的机制模型.
  • 实施了机器学习模型来预测上下文依赖的动态参数.

主要成果:

  • 设计-构建-测试-学习管道成功确定了理想的生物传感器规格的最佳条件组合.
  • 开发的模型可以预测生物传感器在不同监管元素,媒介和补充剂的性能.
  • 证明了生物传感器在工程路径中的自动选和动态调节的实用性.

结论:

  • 这项工作增强了对全细胞生物传感器的理解和应用,用于生物生产中的精确测量和调节.
  • 开发的DBTL管道和预测模型对于优化循环生物经济应用的生物传感器设计至关重要.
  • 工程生物传感器显示了对工程生产途径对有价值分子的动态调节的巨大潜力.