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DISCOPOLIS 2.0: a new recursive version of the algorithm for uniform sampling of metabolic flux distributions with linear programming

机译：Diveropolis 2.0：一种新的递归版本的算法，用于用线性编程采样代谢通量分布的均匀采样

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Metabolic flux values are subject to equality (e.g., mass balances, measured fluxes) and inequality (e.g., upper and lower flux bounds) constraints. The system is generally underdetermined,i.e.with more unknown fluxes than equations, and all the admissible solutions belong to a convex polytope. Sampling that polytope allows subsequently computing marginal distributions for each metabolic flux. We propose a new version of the DISCOPOLIS algorithm (DIscrete Sampling of COnvex POlytopes via Linear program Iterative Sequences) that provides the same weight to all the samples and that approximates a uniform distribution thanks to a recursive loop that computes variable numbers (called grid points) of samples depending on the fluxes that have already been fixed in former iterations. The method is illustrated on three different case studies (with 3, 95 and 1054 fluxes) and shows interesting results in terms of flux distribution convergence and large ranges of the marginal flux distributions. Three consistent criteria are proposed to choose the optimal maximum number of grid points.
机译：代谢助焊剂值受平等（例如，质量平衡，测量的助熔剂）和不等式（例如，上部和下部通量界限）约束。该系统通常是未确定的，即更多未知的助焊剂，而等于方程，所有可允许的解决方案属于凸多孔孔。取样多容渗透允许随后计算每个代谢通量的边际分布。我们提出了一个新版本的Dockopolis算法（通过线性程序迭代序列的离散采样通过线性程序迭代序列）为所有样本提供相同的权重，并且由于计算可变数字（称为网格点）的递归循环，近似于均匀分布取决于已经固定在以前的迭代中的助熔剂的样品。该方法在三种不同的情况下进行说明（具有3,95和1054个助熔剂），并在助焊剂分布会聚和边际通量分布的大范围内显示有趣的结果。提出了三个一致的标准来选择最佳的最大网格点数。

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