动态模型选择和最佳批量设计用于Cupriavidus necator生产的聚氨酸酸 (PHA)
Pema Lhamo1, Biswanath Mahanty2
1Division of Biotechnology, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, 641114, India.
Applied biochemistry and biotechnology
|August 23, 2023
概括
微生物聚酸酸盐 (PHA) 生产的数学建模使用一种新的参数离谱化方法进行了优化. 这种方法通过选择准确的动力学模型和预测最佳PHA积累来改善生物过程设计.
科学领域:
- 生物技术和生物工艺工程.
- 微生物代谢和合成生物学.
背景情况:
- 数学建模对于优化微生物多酸盐 (PHA) 生产至关重要,但模型选择仍然是启发式的.
- 开发强大的模型需要准确的动力参数识别和增长模型选择策略.
研究的目的:
- 在Cupriavidus necator中应用参数离散方法来建模PHA生产.
- 建立可靠的PHA生产动力学,并从实验数据中选择适当的生长模型.
- 为增强PHA积累设计一个最佳的批量启动策略.
主要方法:
- 在Cupriavidus necator中使用糖和尿素模拟PHA生产,采用参数离析方法.
- 从无尿素实验中确定产品形成动力学,并从含糖和尿素的批量培养中选择生长模型.
- 使用后勤增长和Luedeking-Piret模型,使用R2验证,调整R2,AICc,交叉验证,置信区间和灵敏度分析.
主要成果:
- 精选的物流增长和Luedeking-Piret模型具有很高的适合性 (R2: 0.941,调整R2: 0.930,AICc: -42.764).
- 通过交叉验证,置信区间和灵敏度分析验证模型适应性.
- 预测了一个最佳的批量启动政策,在120小时内产生2.030g的L-1PHA积累.
结论:
- 开发的建模框架有效地解决了PHA生产模型中的过度参数化和可识别性问题.
- 这种方法有助于设计微生物PHA生物生产的最佳批量启动政策.
- 该研究提供了一种强大的方法来增强生物工艺设计和PHA产量.
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