Parallel isotope differential modeling for instationary 13C fluxomics at the genome scale
Parallel isotope differential modeling for instationary 13C fluxomics at the genome scale
复制标题
基因组规模静态 13C 通量组学的并行同位素差分建模
DOI：
10.1186/s13068-020-01737-5
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发表时间：
2020-06
影响因子：
6.3
通讯作者：
Xie Xiaoyao
中科院分区：
文献类型：
--
作者：
Zhang Zhengdong;Liu Zhentao;Meng Yafei;Chen Zhen;Han Jiayu;Wei Yimin;Shen Tie;Yi Yin;Xie Xiaoyao
背景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，最先进的方法是固定 13C 通量组学，它是转录组学或蛋白质组学的兄弟姐妹。稳态 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。结果在这里，我们提出了一种并行方法来对稳态 13C 标记数据进行建模。基本代谢单元 (EMU) 框架经过重组，可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)。引入变域并行算法来并行处理常微分方程。恒定步长建模可实现15倍加速，自适应步长建模可实现5倍加速。结论该算法普遍适用于EMU和Cumomer等同位素颗粒，可大幅加速静态13C通量组学建模。因此，它具有在任何固定 13C 通量组学建模中广泛采用的巨大潜力。
BackgroundA 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.ResultHere, 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.ConclusionThis 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.
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影响因子：
5.3
作者：
通讯作者：
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影响因子：
5.3
作者：
Manolis I. A. Lourakis
通讯作者：
Manolis I. A. Lourakis
影响因子：
2.9
作者：
Toya, Yoshihiro;Ishii, Nobuyoshi;Shimizu, Kazuyuki
通讯作者：
Shimizu, Kazuyuki
影响因子：
3
作者：
He L;Wu SG;Zhang M;Chen Y;Tang YJ
通讯作者：
Tang YJ
影响因子：
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作者：
Kajihata S;Furusawa C;Matsuda F;Shimizu H
通讯作者：
Shimizu H