State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China.
DSM Biotechnology Center, A. Fleminglaan 1, 2613 AX, Delft, The Netherlands.
Bioprocess Biosyst Eng. 2021 Dec;44(12):2553-2565. doi: 10.1007/s00449-021-02626-3. Epub 2021 Aug 30.
Metabolic flux analysis (MFA) is a powerful tool for studying microbial cell physiology. Isotope-based MFA is the accepted standard for studying metabolic fluxes under steady-state conditions. However, its application under dynamic extracellular conditions is limited due to lack of proper techniques, such as rapid sampling and quenching, high cost and laborious execution. Here, we propose a new strategy to tackle this through incorporating dynamic metabolite abundance data into genome-scale metabolic models (GEM). First, a dummy extracellular pool concept is proposed for each dynamically changing metabolite, which represents a "sink" or "source", with corresponding dummy reactions coded into the GEM model. The dynamic model (expressed as differential equations) is then transformed into a quasi-steady-state model (expressed as linear equations), which can be easily solved by constraining the GEM model with the dynamic metabolite quantification data. For this, common linear-programming optimization algorithms were utilized to estimate the dynamic fluxes. Dynamic high-accuracy metabolite abundance data were obtained through the Isotope Dilution Mass Spectrometry (IDMS) method and high-speed sampling-quenching, and it was demonstrated that the newly proposed strategy could be successfully applied to obtain intracellular dynamic fluxes of Aspergillus niger under regimes of single and periodic extracellular glucose pulses. The applicability of the new method was also tested on dynamic fluxes estimation in a glucose pulse-response study of Saccharomyces cerevisiae. The proposed method provides a powerful tool to investigate cell physiology under dynamic conditions, especially relevant for bioprocess scale-up to industrial-scale bioreactors.
代谢通量分析（MFA）是研究微生物细胞生理学的有力工具。基于同位素的 MFA 是研究稳态条件下代谢通量的公认标准。然而，由于缺乏适当的技术，如快速采样和淬灭、高成本和繁琐的执行，其在动态胞外条件下的应用受到限制。在这里，我们通过将动态代谢物丰度数据纳入基因组规模代谢模型（GEM）来提出一种新的策略来解决这个问题。首先，对于每个动态变化的代谢物，提出了一个虚拟的细胞外池概念，它代表了一个“汇”或“源”，并将相应的虚拟反应编码到 GEM 模型中。然后，将动态模型（表示为微分方程）转换为准稳态模型（表示为线性方程），通过用动态代谢物定量数据约束 GEM 模型可以很容易地求解这个方程。为此，利用常见的线性规划优化算法来估计动态通量。通过同位素稀释质谱法（IDMS）和高速采样-淬灭获得了动态高精度代谢物丰度数据，并证明了所提出的新策略可以成功应用于获得黑曲霉在单外葡萄糖脉冲和周期性外葡萄糖脉冲条件下的细胞内动态通量。该新方法的适用性也在酿酒酵母葡萄糖脉冲响应研究中的动态通量估计中进行了测试。该方法为研究动态条件下的细胞生理学提供了有力的工具，特别是在生物过程放大到工业规模的生物反应器方面具有重要意义。
