Metabolomics and modelling approaches for systems metabolic engineering

• Published in: Metabolic engineering communications Vol. 15; p. e00209
• Main Authors: Khanijou, Jasmeet Kaur, Kulyk, Hanna, Bergès, Cécilia, Khoo, Leng Wei, Ng, Pnelope, Yeo, Hock Chuan, Helmy, Mohamed, Bellvert, Floriant, Chew, Wee, Selvarajoo, Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2022; Elsevier
• Subjects: Constraint-based modelling; Dynamic metabolomics; Kinetic modelling; Life Sciences; Machine learning; Quantitative metabolomics; Review; Spatial metabolomics; Quantitative metabolomics; Dynamic metabolomics; Spatial metabolomics; Kinetic modelling; Constraint-based modelling; Machine learning
• ISSN: 2214-0301; 2214-0301
• DOI: 10.1016/j.mec.2022.e00209

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. •Quantitative metabolomics data is essential for computational modelling approaches.•Improvements in high-throughput quantitative workflows aid data generation.•Spatial and real-time metabolomics can drive understanding of biological networks.•Kinetic and constraint-based modelling are important computational tools.•Machine learning algorithms can push systems metabolic engineering.