同位素动态通量分析的随机模拟算法
Stochastic simulation algorithm for isotope-based dynamic flux analysis
摘要：
碳同位素标记方法是一种标准的代谢工程工具，用于活细胞中的通量定量。为了应对同位素标记系统的高维性，已经开发了多种算法来减少代谢通量分析（MFA）中的变量或操作的数量，但是缺乏对非平稳代谢条件的概括性。在这项研究中，我们提出了一个随机模拟算法（SSA）来自同位素标记系统的化学主方程。该算法允许在非平稳条件下计算同位素异构体浓度的时间演化，具有计算时间不随同位素异构体的数量缩放的有价值的性质。该算法的效率和局限性是基准的正问题和逆问题的13 C-DMFA在戊糖磷酸途径。总体而言，SSA构成了一个替代类的确定性方法代谢通量分析，很好地适应于全面的数据集，包括平行标记实验，其局限性相关的采样大小可以通过使用蒙特卡洛采样方法来克服。
英文摘要：
Carbon isotope labeling method is a standard metabolic engineering tool for flux quantification in living cells. To cope with the high dimensionality of isotope labeling systems, diverse algorithms have been developed to reduce the number of variables or operations in metabolic flux analysis (MFA), but lacks generalizability to non-stationary metabolic conditions. In this study, we present a stochastic simulation algorithm (SSA) derived from the chemical master equation of the isotope labeling system. This algorithm allows to compute the time evolution of isotopomer concentrations in non-stationary conditions, with the valuable property that computational time does not scale with the number of isotopomers. The efficiency and limitations of the algorithm is benchmarked for the forward and inverse problems of 13C-DMFA in the pentose phosphate pathways. Overall, SSA constitute an alternative class to deterministic approaches for metabolic flux analysis that is well adapted to comprehensive dataset including parallel labeling experiments, and whose limitations associated to the sampling size can be overcome by using Monte Carlo sampling approaches.
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