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Antibiotic Selection00:57

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Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection
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对抗生素时间机器问题的随机编程方法.

Oğuz Mesüm1, Ali Rana Atilgan1, Burak Kocuk1

  • 1Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey.

Mathematical biosciences
|April 11, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了抗生素时间机器问题,以逆转抗生素耐药性. 开发了数学模型和编程方法,以找到回归基因型的最佳策略,即使增长率和过渡概率不确定.

关键词:
细菌生长速度的增长速度贝塔-乳糖酶是一种药物输送是药物输送的过程.动态编程 是一种动态编程.健身的格局 健身的格局混合整数线性编程 混合整数线性编程

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科学领域:

  • 计算生物学 计算生物学
  • 数学生物学 数学生物学
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 抗生素耐药性是一个日益增长的全球健康威胁.
  • 了解和逆转阻力机制至关重要.
  • 数学建模可以提供对复杂的生物过程的见解,如耐药性进化.

研究的目的:

  • 模拟和解决抗生素时间机器问题,旨在逆转抗生素耐药性.
  • 为优化基因型逆转策略开发计算方法.
  • 为了考虑到增长率和过渡概率的不确定性.

主要方法:

  • 模拟抗生素耐药性的马尔科夫链与基因型和基因突变.
  • 使用随机混合整数线性编程和动态编程.
  • 采用样本平均近似方法来量化不确定性.

主要成果:

  • 开发了基于增长率计算过渡概率的方法.
  • 成功解决了抗生素时间机器问题的静态和动态版本.
  • 通过样本外分析证明了准确和有效的解决方案.

结论:

  • 拟议的数学和计算框架为解决抗生素耐药性的问题提供了一种可行的方法.
  • 这些方法为确定逆转基因型的最佳策略提供了一条途径.
  • 这项工作有助于开发针对抗生素耐药性的新型干预措施.