Ando David, Garcia Martin Hector
Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Joint BioEnergy Institute, Emeryville, CA, USA.
Methods Mol Biol. 2018;1671:333-352. doi: 10.1007/978-1-4939-7295-1_21.
Accelerating the Design-Build-Test-Learn (DBTL) cycle in synthetic biology is critical to achieving rapid and facile bioengineering of organisms for the production of, e.g., biofuels and other chemicals. The Learn phase involves using data obtained from the Test phase to inform the next Design phase. As part of the Learn phase, mathematical models of metabolic fluxes give a mechanistic level of comprehension to cellular metabolism, isolating the principle drivers of metabolic behavior from the peripheral ones, and directing future experimental designs and engineering methodologies. Furthermore, the measurement of intracellular metabolic fluxes is specifically noteworthy as providing a rapid and easy-to-understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis in the Learn phase of the DBTL cycle, where we show how one can take the isotope labeling data from a C labeling experiment and immediately turn it into a determination of cellular fluxes that points in the direction of genetic engineering strategies that will advance the metabolic engineering process.For our modeling purposes we use the Joint BioEnergy Institute (JBEI) Quantitative Metabolic Modeling (jQMM) library, which provides an open-source, python-based framework for modeling internal metabolic fluxes and making actionable predictions on how to modify cellular metabolism for specific bioengineering goals. It presents a complete toolbox for performing different types of flux analysis such as Flux Balance Analysis, C Metabolic Flux Analysis, and it introduces the capability to use C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale C Metabolic Flux Analysis (2S-C MFA) [1]. In addition to several other capabilities, the jQMM is also able to predict the effects of knockouts using the MoMA and ROOM methodologies. The use of the jQMM library is illustrated through a step-by-step demonstration, which is also contained in a digital Jupyter Notebook format that enhances reproducibility and provides the capability to be adopted to the user's specific needs. As an open-source software project, users can modify and extend the code base and make improvements at will, providing a base for future modeling efforts.
加速合成生物学中的设计-构建-测试-学习（DBTL）循环对于实现生物体的快速便捷生物工程以生产生物燃料和其他化学品等至关重要。学习阶段涉及使用从测试阶段获得的数据为下一个设计阶段提供信息。作为学习阶段的一部分，代谢通量的数学模型能从机制层面理解细胞代谢，将代谢行为的主要驱动因素与次要驱动因素区分开来，并指导未来的实验设计和工程方法。此外，细胞内代谢通量的测量特别值得注意，因为它能快速且直观地呈现碳和能量在整个细胞中的流动情况。在此，我们提供一份在DBTL循环的学习阶段进行代谢通量分析的详细指南，展示如何从碳标记实验的同位素标记数据出发，立即将其转化为细胞通量的测定结果，从而为推进代谢工程过程指明基因工程策略的方向。出于建模目的，我们使用联合生物能源研究所（JBEI）的定量代谢建模（jQMM）库，该库提供了一个基于Python的开源框架，用于对内部代谢通量进行建模，并就如何针对特定生物工程目标修改细胞代谢做出可行的预测。它提供了一个完整的工具箱，用于执行不同类型的通量分析，如通量平衡分析、碳代谢通量分析，还引入了通过一种称为双尺度碳代谢通量分析（2S-C MFA）的技术，利用碳标记实验数据来约束综合基因组规模模型的能力[1]。除了其他一些功能外，jQMM还能够使用MoMA和ROOM方法预测基因敲除的效果。通过逐步演示来说明jQMM库的使用方法，该演示还采用数字Jupyter Notebook格式呈现，增强了可重复性，并具备根据用户特定需求进行调整的能力。作为一个开源软件项目，用户可以随意修改和扩展代码库并进行改进，为未来的建模工作提供基础。
