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首页> 外文期刊>Metabolites >Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13 C Metabolic Flux Analysis
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Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13 C Metabolic Flux Analysis

机译：约束基因组规模模型来表示代谢的蝶形领结结构以进行13 C代谢通量分析

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Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) and Two-Scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13 C MFA or 2S- 13 C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore .
机译：内部代谢通量的确定对于基础生物学和应用生物学至关重要，因为它们可以绘制碳和电子如何流过新陈代谢以实现细胞功能。 13 C代谢通量分析（13 C MFA）和两尺度13 C代谢通量分析（2S-13 C MFA）是用于确定此类通量的两种技术。两者都基于简化的近似值进行操作，即从外围代谢到中心“核心”碳代谢的代谢通量极小，在建模核心代谢中的同位素标记时可以忽略。精确建模实验性同位素标记数据的能力以及使用这些方法进行实验验证的代谢工程预测都支持这种“两尺度”或“领结”近似方法的有效性。但是，满足该近似要求的核心代谢的边界可能因物种和细胞培养条件而异。在这里，我们提出了一套算法，（1）系统地计算基因组规模模型的任何指定“核心”的通量范围，以满足领结近似;（2）自动识别一组更新的核心反应，更有效地满足该近似值。首先，我们利用线性规划同时确定从外围代谢到核心代谢的最低通量，该通量与观察到的生长速率和细胞外代谢物交换通量相适应。其次，我们使用模拟退火来识别一组更新的核心反应，以使进入核心代谢的通量最少，以满足这些实验约束。总之，这些方法可加速和自动化鉴定与13 C MFA或2S-13 C MFA一起使用的生物学上合理的核心反应，并为进入核心的焊剂提供了更低的通量范围。以前的方法。我们在https://github.com/JBEI/limitfluxtocore提供了这些算法的开源Python实现。

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