Metabolomics and modelling approaches for systems metabolic engineering

IF 3.7 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Metabolic Engineering Communications Pub Date : 2022-12-01 DOI:10.1016/j.mec.2022.e00209
Jasmeet Kaur Khanijou , Hanna Kulyk , Cécilia Bergès , Leng Wei Khoo , Pnelope Ng , Hock Chuan Yeo , Mohamed Helmy , Floriant Bellvert , Wee Chew , Kumar Selvarajoo
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引用次数: 6

Abstract

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

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代谢组学和系统代谢工程建模方法
代谢工程涉及通过基因工程或合成生物学方法操纵微生物以产生所需的化合物。代谢组学涉及细胞内和细胞外代谢物的定量,其中经常使用质谱和核磁共振分析仪器。在这里,实验设计、样品制备、代谢物淬火和提取是定量代谢组学工作流程的关键。由此产生的代谢组学数据可以与计算建模方法一起使用,例如动力学和基于约束的建模,以更好地了解所需化合物合成的潜在机制和瓶颈,从而通过系统代谢工程加速研究。基于约束的模型,如基因组规模模型,已经成功地用于提高工程微生物所需化合物的产量,然而,与动力学或动态模型不同,基于约束的模型不包含调节作用。然而,直到今天,缺乏时间序列代谢组学数据的生成阻碍了动态模型的有用性。在这篇综述中,我们表明自动化、动态实时分析和高通量工作流程的改进可以通过时间序列代谢组学数据生成来驱动动态模型生成更多高质量的数据。空间代谢组学也有可能被用作传统代谢组学的补充方法,因为它提供了关于代谢物定位的信息。然而,必须付出更多努力,从通过成像质谱法获得的空间代谢组学数据中识别代谢物,在这方面机器学习方法可能被证明是有用的。另一方面,单细胞代谢组学也得到了快速发展,了解细胞-细胞异质性可以为微生物的有效代谢工程提供更多的见解。展望未来,随着自动化、动态实时分析、高通量工作流程和空间代谢组学的潜在改进,可以使用机器学习算法和动态模型产生和研究更多数据,以生成定性和定量预测,以推进代谢工程工作。
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来源期刊
Metabolic Engineering Communications
Metabolic Engineering Communications Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
13.30
自引率
1.90%
发文量
22
审稿时长
18 weeks
期刊介绍: Metabolic Engineering Communications, a companion title to Metabolic Engineering (MBE), is devoted to publishing original research in the areas of metabolic engineering, synthetic biology, computational biology and systems biology for problems related to metabolism and the engineering of metabolism for the production of fuels, chemicals, and pharmaceuticals. The journal will carry articles on the design, construction, and analysis of biological systems ranging from pathway components to biological complexes and genomes (including genomic, analytical and bioinformatics methods) in suitable host cells to allow them to produce novel compounds of industrial and medical interest. Demonstrations of regulatory designs and synthetic circuits that alter the performance of biochemical pathways and cellular processes will also be presented. Metabolic Engineering Communications complements MBE by publishing articles that are either shorter than those published in the full journal, or which describe key elements of larger metabolic engineering efforts.
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