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A dynamic kinetic model captures cell-free metabolism for improved butanol production.
- Source :
-
Metabolic Engineering . Mar2023, Vol. 76, p133-145. 13p. - Publication Year :
- 2023
-
Abstract
- Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 h. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts. • A kinetic model captures timecourse data, including butanol production, in two experimental conditions in cell-free extracts. • Model hypothesizes metabolic mechanisms to explain experimentally observed behaviors between conditions. • Strategies to increase butanol production are predicted, the most promising of which is experimentally validated. • Analyses reveal how model parameters are constrained between many possible parameter sets within an ensemble. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10967176
- Volume :
- 76
- Database :
- Academic Search Index
- Journal :
- Metabolic Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 162288876
- Full Text :
- https://doi.org/10.1016/j.ymben.2023.01.009