向一个建模,优化和预测控制框架为给批量代谢网络遗传学
Sebastián Espinel-Ríos1, Bruno Morabito2, Johannes Pohlodek3
1Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Biotechnology and bioengineering
|November 9, 2023
概括
网络遗传学与基于模型的控制相结合,优化了生物过程. 这种方法使用动态模型和反控制来提高生物技术制造业的产品产量和生产率.
科学领域:
- 生物技术和合成生物学
- 工艺工程和控制系统的控制系统.
背景情况:
- 传统的生物过程优化依赖于静态方法,在动态条件或干扰下经常失败.
- 细胞外因素不足以提供最佳的性能和精确的产品成分.
- 网络遗传学可以实现动态基因表达控制,提供新的优化可能性.
研究的目的:
- 整合网络遗传学与基于模型的优化和动态生物过程的预测控制.
- 开发基于约束的动态模型,包括代谢反应,资源分配和基因表达.
- 通过最佳的基质养速率来增强料批处理过程控制.
主要方法:
- 为过程输入制定基于模型的最佳控制问题.
- 实施在线反和不确定性管理的模型预测控制.
- 利用基于约束的动态模型进行代谢和遗传调节.
主要成果:
- 证明了ATPase酶复合物的成功光遗传控制,用于动态ATP浪费.
- 通过受控的代谢调节来展示产品产量和生产率的调整.
- 在模拟养批次过程中验证了基于模型的预测控制策略.
结论:
- 网络遗传学,基于模型的优化和预测控制的融合为动态生物过程优化提供了一个强大的框架.
- 这种综合方法增强了对基因表达和代谢途径的控制,从而改善了生物制造的结果.
- 未来的应用包括精确控制复杂生物技术系统中的产品成分和产量.
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