A precise map of the metabolic fluxome, the closest surrogate to the physiological phenotype, is becoming progressively more important in the metabolic engineering of photosynthetic organisms for biofuel and biomass production. For photosynthetic organisms, the state-of-the-art method for this purpose is instationary 13C fluxomics, which has arisen as a sibling of transcriptomics or proteomics. Instationary 13C data processing requires solving high-dimensional nonlinear differential equations and leads to large computational and time costs when its scope is expanded to a genome-scale metabolic network. Here, we present a parallelized method to model instationary 13C labeling data. The elementary metabolite unit (EMU) framework is reorganized to allow treating individual mass isotopomers and breaking up of their networks into strongly connected components (SCCs). A variable domain parallel algorithm is introduced to process ordinary differential equations in a parallel way. 15-fold acceleration is achieved for constant-step-size modeling and ~ fivefold acceleration for adaptive-step-size modeling. This algorithm is universally applicable to isotope granules such as EMUs and cumomers and can substantially accelerate instationary 13C fluxomics modeling. It thus has great potential to be widely adopted in any instationary 13C fluxomics modeling.

中文翻译：

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基因组规模的固定 13C 通量组学的平行同位素差异建模。

代谢通量组的精确图谱是最接近生理表型的替代物，在用于生物燃料和生物质生产的光合生物的代谢工程中变得越来越重要。对于光合生物，用于此目的的最先进方法是固定 13C 通量组学，它已作为转录组学或蛋白质组学的兄弟出现。固定 13C 数据处理需要求解高维非线性微分方程，当其范围扩展到基因组规模的代谢网络时，会导致大量的计算和时间成本。在这里，我们提出了一种并行化的方法来模拟固定的 13C 标记数据。对基本代谢物单元 (EMU) 框架进行了重组，以允许处理单个质量同位素异构体并将其网络分解为强连接组件 (SCC)。引入变域并行算法对常微分方程进行并行处理。恒定步长建模实现了 15 倍加速，自适应步长建模实现了约五倍加速。该算法普遍适用于 EMU 和 cumomers 等同位素颗粒，并且可以显着加速固定 13C 通量组学建模。因此，它具有被广泛应用于任何固定 13C 通量组学建模的巨大潜力。恒定步长建模实现了 15 倍加速，自适应步长建模实现了约五倍加速。该算法普遍适用于 EMU 和 cumomers 等同位素颗粒，并且可以显着加速固定 13C 通量组学建模。因此，它具有被广泛应用于任何固定 13C 通量组学建模的巨大潜力。恒定步长建模实现了 15 倍加速，自适应步长建模实现了约五倍加速。该算法普遍适用于 EMU 和 cumomers 等同位素颗粒，并且可以显着加速固定 13C 通量组学建模。因此，它具有被广泛应用于任何固定 13C 通量组学建模的巨大潜力。