论文标题
基于同位素的动态通量分析的随机仿真算法
Stochastic simulation algorithm for isotope-based dynamic flux analysis
论文作者
论文摘要
碳同位素标记方法是用于活细胞通量定量的标准代谢工程工具。为了应对同位素标记系统的高维度，已经开发了不同的算法来减少代谢通量分析（MFA）中变量或操作的数量，但缺乏对非平稳代谢条件的普遍性。在这项研究中，我们提出了一种从同位素标记系统的化学主方程得出的随机模拟算法（SSA）。该算法允许在非平稳条件下计算同位素浓度的时间演变，其有价值的属性是计算时间与同位素数的数量不扩展。算法的效率和局限性是针对戊糖磷酸途径13C-DMFA的正向和反向问题的基准测定的。总体而言，SSA构成了代谢通量分析的确定方法的替代类别，该类别非常适合全面的数据集，包括并行标记实验，并且可以通过使用Monte Carlo采样方法来克服与采样大小相关的局限性。
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.
