面向流加代谢控制遗传学的建模、优化和预测控制框架
Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics
摘要：
生物技术为可持续地生产有价值的产品提供了许多机会。优化生物工艺的工具箱包括\textit{extracellular}工艺要素，如生物反应器设计和操作模式、培养基配方、培养条件、补料速率等。然而，这些要素通常不足以实现最佳工艺性能或精确的产品组成。可以使用代谢和遗传工程方法在细胞内水平进行优化。然而，这些措施往往是静态的，在应用于动态过程或发生扰动时就失效了。此外，许多生物过程都是经验优化的，并且几乎没有反馈控制来抵消干扰。网络遗传学的概念已经打开了优化生物过程的新的可能性，通过外部输入（例如，光遗传学中的光强度）。在这里，我们融合了基于模型的优化和预测控制优化动态生物过程的控制遗传学。要做到这一点，我们建议使用动态约束为基础的模型，整合代谢反应，资源分配和诱导基因表达的动态。我们制定了一个基于模型的最优控制问题，以找到最优的过程输入。此外，我们建议使用模型预测控制，以解决通过在线反馈的不确定性。我们专注于补料分批工艺，其中底物进料速率是一个额外的优化变量。作为一个模拟的例子，我们显示了ATP酶复合物的光遗传学控制，用于动态调节强制ATP消耗以调节产物产率和生产率。
英文摘要：
Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes \textit{extracellular} process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, etc. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.
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