Constraint-based optimization of microbial strains and model-based bioprocess design have been used extensively to enhance yields in biotechnological processes. However, strain and process optimization are usually carried out in sequential steps, causing underperformance of the biotechnological process when scaling up to industrial fermentation conditions. Herein, we propose the optimization formulation SimulKnock that combines the optimization of a fermentation process with metabolic network design in a bilevel optimization program. The upper level maximizes space-time yield and includes mass balances of a continuous fermentation, while the lower level is based on flux balance analysis. SimulKnock predicts optimal gene deletions and finds the optimal trade-off between growth rate and product yield. Results of a case study with a genome-scale metabolic model of Escherichia coli indicate higher space-time yields than a sequential approach using OptKnock for almost all target products considered. By leveraging SimulKnock, we reduce the gap between strain and process optimization.

中文翻译：

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发酵与微生物同步设计


基于约束的微生物菌株优化和基于模型的生物工艺设计已被广泛用于提高生物技术工艺的产量。然而，菌株和工艺优化通常按顺序步骤进行，导致生物技术工艺在扩大到工业发酵条件时表现不佳。在此，我们提出了优化公式 SimulKnock，它将发酵过程的优化与双层优化程序中的代谢网络设计相结合。上层最大化时空产量并包括连续发酵的质量平衡，而下层基于通量平衡分析。 SimulKnock 预测最佳基因缺失并找到生长速率和产品产量之间的最佳权衡。使用大肠杆菌基因组规模代谢模型进行的案例研究结果表明，对于几乎所有考虑的目标产品，与使用 OptKnock 的顺序方法相比，时空产量更高。通过利用 SimulKnock，我们缩小了应变与工艺优化之间的差距。