Artificial Neural Network - Multi-Objective Genetic Algorithm based optimization for the enhanced pigment accumulation in Synechocystis sp. PCC 6803.

IF 3.5 3区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Namrata Bhagat, Guddu Kumar Gupta, Amritpreet Kaur Minhas, Deepak Chhabra, Pratyoosh Shukla
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引用次数: 0

Abstract

Background: Natural colorants produced by the cyanobacterium include carotenoids, chlorophyll a and phycocyanin. The current study used the Synechocystis sp. PCC 6803 to examine how abiotic stress conditions, such as low temperature as well as high light intensity, affect the pigment accumulations in comparison to the control conditions. Additionally, using the response surface methodology (RSM) and artificial neural network - multi-objective genetic algorithm (ANN-MOGA), the impact of several nitrogen sources such as urea, ammonium chloride, and sodium nitrate as nutritional stress on the pigment accumulations in the Synechocystis sp. PCC 6803 was examined.

Results: The results showed that the pigment accumulation was more pronounced when urea and ammonium chloride was used in combination with nitrate, respectively, as nitrogen source. With the help of our prediction model that used ANN-MOGA, we were able to enhance the synthesis of chlorophyll a, carotenoids, and phycocyanin by 21.93 µg/mL, 9.78 µg/mL, and 0.05 µg/mL, respectively compared to control with 6.37, 3.88 and 0.008 µg/mL. The significant scavenging activity of pigment was showed with 7.66 ± 0.001 values of IC50. Additionally, a very good correlation of coefficient (R2) value 0.99, 0.99 and 0.92 was obtained for APX, CAT and GPX enzyme activity, respectively.

Conclusions: The findings lays the groundwork for future attempts to turn cyanobacteria into a commercially viable source of natural pigments by demonstrating the benefits of using the RSM and machine learning techniques like ANN-MOGA to optimise the production of cyanobacterial pigments. The significant scavenging and antioxidant activities like CAT, GPX and APX were also shown by the pigments of the Synechocystis sp. PCC 6803. Furthermore, these machine learning tools can be used as a model to improve and optimize the yields for other metabolites production.

基于人工神经网络和多目标遗传算法的优化技术,用于增强 Synechocystis sp.
背景:蓝藻产生的天然着色剂包括类胡萝卜素、叶绿素a和藻蓝蛋白。目前的研究使用聚胞藻sp. PCC 6803来研究非生物胁迫条件,如低温和高光强,如何影响色素积累,并与对照条件进行比较。此外,利用响应面法(RSM)和人工神经网络-多目标遗传算法(ANN-MOGA),研究了尿素、氯化铵和硝酸钠等氮源作为营养胁迫对聚囊藻PCC 6803色素积累的影响。结果:尿素和氯化铵分别与硝酸盐复合作为氮源时,色素积累更为明显;在ANN-MOGA预测模型的帮助下,叶绿素a、类胡萝卜素和藻蓝蛋白的合成分别比对照组的6.37、3.88和0.008µg/mL提高了21.93µg/mL、9.78µg/mL和0.05µg/mL。色素的IC50值为7.66±0.001,具有显著的清除活性。此外,APX、CAT和GPX酶活性的相关系数(R2)分别为0.99、0.99和0.92。结论:这些发现为未来尝试将蓝藻转化为商业上可行的天然色素来源奠定了基础,展示了使用RSM和ANN-MOGA等机器学习技术优化蓝藻色素生产的好处。聚囊藻(Synechocystis sp. PCC 6803)色素对CAT、GPX和APX具有明显的清除和抗氧化活性。此外,这些机器学习工具可以作为模型来提高和优化其他代谢物的产量。
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来源期刊
BMC Biotechnology
BMC Biotechnology 工程技术-生物工程与应用微生物
CiteScore
6.60
自引率
0.00%
发文量
34
审稿时长
2 months
期刊介绍: BMC Biotechnology is an open access, peer-reviewed journal that considers articles on the manipulation of biological macromolecules or organisms for use in experimental procedures, cellular and tissue engineering or in the pharmaceutical, agricultural biotechnology and allied industries.
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