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通过代谢途径抑制和基于机器学习的优化策略提高工程化聚球藻UTEX 2973中异戊二烯的产量。

Enhancement of isoprene production in engineered Synechococcus elongatus UTEX 2973 by metabolic pathway inhibition and machine learning-based optimization strategy.

作者信息

Yadav Indrajeet, Rautela Akhil, Gangwar Agendra, Wagadre Lokesh, Rawat Shweta, Kumar Sanjay

机构信息

School of Biochemical Engineering, IIT (BHU) Varanasi, Varanasi, Uttar Pradesh 221005, India.

School of Biochemical Engineering, IIT (BHU) Varanasi, Varanasi, Uttar Pradesh 221005, India.

出版信息

Bioresour Technol. 2023 Nov;387:129677. doi: 10.1016/j.biortech.2023.129677. Epub 2023 Aug 12.

Abstract

An engineered Synechococcus elongatus UTEX 2973-IspS.IDI is used to enhance isoprene production through geranyl diphosphate synthase (CrtE) inhibition and process parameters (light intensity, NaHCO and growth temperature) optimization approach. A cumulative isoprene production of 1.21 mg/gDCW was achieved with productivity of 12.6 μg/gDCW/h in culture supplemented with 20 μg/mL alendronate. This inhibition strategy improvises the cumulative isoprene production 5.76-fold in presence of alendronate. The maximum cumulative production of isoprene is observed to be 5.22 and 6.20 mg/gDCW (54.4 and 64.6 μg/gDCW/h) at statistical and artificial neural network genetic algorithm (ANN-GA) optimized conditions, respectively. The overall increase of isoprene production is found to be 29.52-fold using an integrated approach of inhibition and ANN-GA optimization in comparison to unoptimized cultures without alendronate. This study reveals that alendronate use as a potential inhibitor and machine learning based optimization is a better approach in comparison to statistical optimization to enhance the isoprene production.

摘要

一种经过工程改造的聚球藻UTEX 2973-IspS.IDI被用于通过抑制香叶基二磷酸合酶(CrtE)和优化工艺参数(光照强度、NaHCO和生长温度)来提高异戊二烯产量。在添加20μg/mL阿仑膦酸盐的培养物中,异戊二烯的累积产量达到1.21mg/gDCW,生产率为12.6μg/gDCW/h。在阿仑膦酸盐存在的情况下,这种抑制策略使异戊二烯的累积产量提高了5.76倍。在统计和人工神经网络遗传算法(ANN-GA)优化条件下,异戊二烯的最大累积产量分别为5.22和6.20mg/gDCW(54.4和64.6μg/gDCW/h)。与未添加阿仑膦酸盐的未优化培养物相比,使用抑制和ANN-GA优化的综合方法,异戊二烯产量的总体增加了29.52倍。这项研究表明,与统计优化相比,使用阿仑膦酸盐作为潜在抑制剂和基于机器学习的优化是提高异戊二烯产量的更好方法。

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