一个集成的多相动态基因组规模模型解释了由Saccharomyces属物种领导的批发发酵
Artai R Moimenta1,2, Diego Troitiño-Jordedo1,3, David Henriques1
1Biosystems and Bioprocess Engineering, IIM-CSIC, Vigo, Spain.
mSystems
|January 22, 2025
概括
本研究引入了一种集成的多相连续模型 (IMC) 用于批量发酵,改进了对代谢途径和化合物合成的预测. IMC模型提供了一种统一的方法来理解微生物生长阶段和优化工业发酵过程.
科学领域:
- 生物技术和代谢工程.
- 系统生物学和计算建模.
背景情况:
- 批量发酵对于生产生物燃料,药品和食品成分至关重要.
- 当前的动态流量平衡分析 (dFBA) 模型与二次新陈代谢和相位过渡作斗争.
- 现有的模型缺乏增长阶段之间的机械联系,需要大量的计算资源.
研究的目的:
- 为批量发酵开发一种新的集成多相连续模型 (IMC).
- 克服现有的dFBA方案的局限性,包括不连续的配方和预测二次新陈代谢的挑战.
- 为了解发酵过程中的微生物代谢提供更准确,更高效的计算工具.
主要方法:
- 结合了新的连续运动模型和基因组规模模型.
- 开发了一种独特的DFBA配方,并提供经验性监管描述,用于自动识别相位过渡.
- 纳入了酵母细胞时间变化的细胞目标的假设.
主要成果:
- 与代谢学数据相比,IMC模型准确地预测了Saccharomyces uvarum*中的细胞内流动和三糖积累.
- 证明了该模型在三种*Saccharomyces*物种的概括性,解释了初级和二级代谢动态.
- 实现了准确的预测,而不需要强制执行特定的代谢输出,与以前的模型不同.
结论:
- IMC模型为探索批量发酵动态提供了一个强大而通用的工具.
- 它提供了对初级和二级新陈代谢有价值的生物学见解,与现有的文献和数据一致.
- 该模型的连续配方和改进的准确性为优化工业发酵过程和新的生物技术应用铺平了道路.
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