PT  - JOURNAL ARTICLE
AU  - Jacob P. Martin
AU  - Blake J. Rasor
AU  - Jonathon DeBonis
AU  - Ashty S. Karim
AU  - Michael C. Jewett
AU  - Keith E.J. Tyo
AU  - Linda J. Broadbelt
TI  - Dynamic Kinetic Models Capture Cell-Free Metabolism for Improved Butanol Production
AID  - 10.1101/2022.09.20.508127
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.09.20.508127
4099  - http://biorxiv.org/content/early/2022/09/22/2022.09.20.508127.short
4100  - http://biorxiv.org/content/early/2022/09/22/2022.09.20.508127.full
AB  - 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 novel combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 hours. 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.Competing Interest StatementThe authors have declared no competing interest.