St John Peter C, Crowley Michael F, Bomble Yannick J
Biosciences Center, National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401 USA.
Biotechnol Biofuels. 2017 Jan 31;10:28. doi: 10.1186/s13068-017-0709-0. eCollection 2017.
Production of chemicals from engineered organisms in a batch culture involves an inherent trade-off between productivity, yield, and titer. Existing strategies for strain design typically focus on designing mutations that achieve the highest yield possible while maintaining growth viability. While these methods are computationally tractable, an optimum productivity could be achieved by a dynamic strategy in which the intracellular division of resources is permitted to change with time. New methods for the design and implementation of dynamic microbial processes, both computational and experimental, have therefore been explored to maximize productivity. However, solving for the optimal metabolic behavior under the assumption that all fluxes in the cell are free to vary is a challenging numerical task. Previous studies have therefore typically focused on simpler strategies that are more feasible to implement in practice, such as the time-dependent control of a single flux or control variable.
This work presents an efficient method for the calculation of a maximum theoretical productivity of a batch culture system using a dynamic optimization framework. The proposed method follows traditional assumptions of dynamic flux balance analysis: first, that internal metabolite fluxes are governed by a pseudo-steady state, and secondly that external metabolite fluxes are dynamically bounded. The optimization is achieved via collocation on finite elements, and accounts explicitly for an arbitrary number of flux changes. The method can be further extended to calculate the complete Pareto surface of productivity as a function of yield. We apply this method to succinate production in two engineered microbial hosts, and , and demonstrate that maximum productivities can be more than doubled under dynamic control regimes.
The maximum theoretical yield is a measure that is well established in the metabolic engineering literature and whose use helps guide strain and pathway selection. We present a robust, efficient method to calculate the maximum theoretical productivity: a metric that will similarly help guide and evaluate the development of dynamic microbial bioconversions. Our results demonstrate that nearly optimal yields and productivities can be achieved with only two discrete flux stages, indicating that near-theoretical productivities might be achievable in practice.
在分批培养中利用工程生物体生产化学品涉及生产力、产率和滴度之间的内在权衡。现有的菌株设计策略通常侧重于设计突变，以在保持生长活力的同时实现尽可能高的产率。虽然这些方法在计算上易于处理，但通过允许细胞内资源分配随时间变化的动态策略可以实现最佳生产力。因此，人们探索了计算和实验方面动态微生物过程设计与实施的新方法，以实现生产力最大化。然而，在假设细胞内所有通量都可自由变化的情况下求解最佳代谢行为是一项具有挑战性的数值任务。因此，先前的研究通常集中在更易于在实践中实施的简单策略上，例如对单个通量或控制变量的时间依赖性控制。
本研究提出了一种使用动态优化框架计算分批培养系统最大理论生产力的有效方法。所提出的方法遵循动态通量平衡分析的传统假设：首先，内部代谢物通量由伪稳态控制；其次，外部代谢物通量受到动态限制。通过有限元配置实现优化，并明确考虑任意数量的通量变化。该方法可以进一步扩展以计算作为产率函数的生产力的完整帕累托表面。我们将此方法应用于两种工程微生物宿主 和 中的琥珀酸生产，并证明在动态控制条件下最大生产力可提高一倍以上。
最大理论产率是代谢工程文献中已确立的一个指标，其使用有助于指导菌株和途径的选择。我们提出了一种稳健、有效的方法来计算最大理论生产力：这一指标同样有助于指导和评估动态微生物生物转化的发展。我们的结果表明，仅通过两个离散通量阶段就可以实现接近最佳的产率和生产力，这表明在实践中可能实现接近理论的生产力。
