通过代谢途径抑制和基于机器学习的优化策略,增强工程化Synechococcus elongatus UTEX 2973中的异烯产量
Indrajeet Yadav1, Akhil Rautela1, Agendra Gangwar1
1School of Biochemical Engineering, IIT (BHU) Varanasi, Varanasi, Uttar Pradesh 221005, India.
Bioresource technology
|August 14, 2023
概括
这项研究提高了Synechococcus elongatus中使用alendronate抑制CrtE的异烯产量,并通过机器学习优化了条件. 这种综合方法与未经优化方法相比,显著提高了异烯产量.
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:
- 异烯是一种有价值的挥发性有机化合物,具有多种工业应用.
- 提高微生物宿主如Synechococcus elongatus的异烯生产对于可持续制造至关重要.
- 向关键酶和优化种植参数是代谢工程的常见策略.
研究的目的:
- 为了增强工程化Synechococcus elongatus菌株的异烯生产.
- 调查阿伦德罗纳酸对日拉尼尔二酸盐合成酶 (CrtE) 的抑制作用.
- 使用统计和机器学习方法优化过程参数,以获得最大的异烯产量.
主要方法:
- 工程造型的Synechococcus elongatus UTEX 2973-IspS.IDI菌株构造. 这种菌株的结构.
- 使用alendronate. 抑制格拉尼尔二酸盐合成酶 (CrtE).
- 通过统计方法和人工神经网络遗传算法 (ANN-GA) 优化工艺参数 (光强度,NaHCO3,温度).
主要成果:
- 阿伦德罗纳酸补充剂增加了累计的异烯产量5.76倍.
- 在ANN-GA优化条件下,最大的累积异烯产量达到6.20 mg/gDCW (64.6 μg/gDCW/h).
- 与非优化培养相比,CrtE抑制和ANN-GA优化的综合方法导致异烯产量增加了29.52倍.
结论:
- 阿伦德罗纳酸是一种有效的抑制剂,可增强工程化Synechococcus elongatus.在工程化Synechococcus elongatus.中的异烯生产.
- 基于机器学习的优化 (ANN-GA) 优于统计优化,可以最大限度地提高异烯产量.
- 联合战略为高效的微生物生产异烯提供了一个有前途的途径.
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