Horvath Nicholas, Vilkhovoy Michael, Wayman Joseph A, Calhoun Kara, Swartz James, Varner Jeffrey D
Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, 14853, USA.
School of Applied and Engineering Physics, Cornell University, Ithaca, NY, 14853, USA.
Metab Eng Commun. 2019 Dec 4;10:e00113. doi: 10.1016/j.mec.2019.e00113. eCollection 2020 Jun.
In this study, we developed a dynamic mathematical model of cell-free protein synthesis (CFPS). Model parameters were estimated from a dataset consisting of glucose, organic acids, energy species, amino acids, and protein product, chloramphenicol acetyltransferase (CAT) measurements. The model was successfully trained to simulate these measurements, especially those of the central carbon metabolism. We then used the trained model to evaluate the performance, e.g., the yield and rates of protein production. CAT was produced with an energy efficiency of 12%, suggesting that the process could be further optimized. Reaction group knockouts showed that protein productivity was most sensitive to the oxidative phosphorylation and glycolysis/gluconeogenesis pathways. Amino acid biosynthesis was also important for productivity, while overflow metabolism and TCA cycle affected the overall system state. In addition, translation was more important to productivity than transcription. Finally, CAT production was robust to allosteric control, as were most of the predicted metabolite concentrations; the exceptions to this were the concentrations of succinate and malate, and to a lesser extent pyruvate and acetate, which varied from the measured values when allosteric control was removed. This study is the first to use kinetic modeling to predict dynamic protein production in a cell-free system, and could provide a foundation for genome scale, dynamic modeling of cell-free protein synthesis.
在本研究中，我们构建了一个无细胞蛋白质合成（CFPS）的动态数学模型。模型参数是根据一个由葡萄糖、有机酸、能量物质、氨基酸、蛋白质产物氯霉素乙酰转移酶（CAT）测量值组成的数据集估算得出的。该模型成功得到训练，能够模拟这些测量值，尤其是中央碳代谢的测量值。然后，我们使用经过训练的模型来评估性能，例如蛋白质生产的产量和速率。CAT的能量效率为12%，这表明该过程可以进一步优化。反应组基因敲除显示，蛋白质生产力对氧化磷酸化和糖酵解/糖异生途径最为敏感。氨基酸生物合成对生产力也很重要，而溢流代谢和三羧酸循环影响整体系统状态。此外，翻译对生产力的重要性高于转录。最后，CAT的生产对变构控制具有鲁棒性，大多数预测的代谢物浓度也是如此；琥珀酸和苹果酸的浓度以及在较小程度上丙酮酸和乙酸的浓度是例外，当去除变构控制时，它们与测量值有所不同。本研究首次使用动力学建模来预测无细胞系统中的动态蛋白质生产，并可为无细胞蛋白质合成的基因组规模动态建模提供基础。
