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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Published on: May 27, 2014
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Metabolomics and modelling approaches for systems metabolic engineering
Jasmeet Kaur Khanijou1, Hanna Kulyk2,3, Cécilia Bergès2,3
1Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (ASTAR), Singapore 138673, Singapore.
Metabolic Engineering Communications
|October 25, 2022
Summary
Advancements in automation and high-throughput workflows are crucial for generating quality time-series metabolomics data. This data, combined with machine learning and dynamic models, will accelerate systems metabolic engineering and microbial compound production.
Area of Science:
- Metabolic engineering and systems biology
- Analytical chemistry and metabolomics
- Computational biology and bioinformatics
Background:
- Metabolic engineering utilizes genetic and synthetic biology to optimize microbial production of target compounds.
- Metabolomics quantifies cellular metabolites using techniques like mass spectrometry and NMR, requiring careful experimental design and sample preparation.
- Computational modeling, including constraint-based and kinetic models, aids in understanding metabolic pathways and identifying bottlenecks.
Purpose of the Study:
- To review the current state and future directions of metabolomics in advancing metabolic engineering.
- To highlight the importance of data generation quality and computational integration for systems metabolic engineering.
- To explore emerging techniques like spatial and single-cell metabolomics and their potential impact.
Main Methods:
- Review of experimental designs, sample preparation, and analytical instrumentation in quantitative metabolomics.
- Discussion of computational modeling approaches (kinetic, constraint-based, dynamic) and their application to metabolic engineering.
- Exploration of advancements in automation, high-throughput workflows, real-time analysis, spatial metabolomics, and single-cell metabolomics.
Main Results:
- Constraint-based models enhance compound yield but lack regulatory insights; dynamic models are limited by data availability.
- Improvements in automation and high-throughput workflows can generate essential time-series metabolomics data for dynamic models.
- Spatial and single-cell metabolomics offer complementary insights into metabolite localization and cellular heterogeneity.
Conclusions:
- Enhanced data generation through improved automation and workflows is key to leveraging dynamic models in metabolic engineering.
- Machine learning algorithms, coupled with dynamic models, can provide predictive insights for optimizing microbial production.
- Future advancements in metabolomics technologies will significantly accelerate progress in systems metabolic engineering.
Keywords:
Constraint-based modellingDynamic metabolomicsKinetic modellingMachine learningQuantitative metabolomicsSpatial metabolomicsMore Related Videos
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