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A Novel Methodology to Estimate Metabolic Flux Distributions in Constraint-Based Models

机译：基于约束的模型中估计代谢通量分布的新方法

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Quite generally, constraint-based metabolic flux analysis describes the space of viable flux configurations for a metabolic network as a high-dimensional polytope defined by the linear constraints that enforce the balancing of production and consumption fluxes for each chemical species in the system. In some cases, the complexity of the solution space can be reduced by performing an additional optimization, while in other cases, knowing the range of variability of fluxes over the polytope provides a sufficient characterization of the allowed configurations. There are cases, however, in which the thorough information encoded in the individual distributions of viable fluxes over the polytope is required. Obtaining such distributions is known to be a highly challenging computational task when the dimensionality of the polytope is sufficiently large, and the problem of developing cost-effective ad hoc algorithms has recently seen a major surge of interest. Here, we propose a method that allows us to perform the required computation heuristically in a time scaling linearly with the number of reactions in the network, overcoming some limitations of similar techniques employed in recent years. As a case study, we apply it to the analysis of the human red blood cell metabolic network, whose solution space can be sampled by different exact techniques, like Hit-and-Run Monte Carlo (scaling roughly like the third power of the system size). Remarkably accurate estimates for the true distributions of viable reaction fluxes are obtained, suggesting that, although further improvements are desirable, our method enhances our ability to analyze the space of allowed configurations for large biochemical reaction networks.
机译：通常，基于约束的代谢通量分析将代谢网络的可行通量配置空间描述为由线性约束定义的高维多面体，线性约束对系统中每种化学物质的生产通量和消耗通量进行了平衡。在某些情况下，可以通过执行其他优化来降低解决方案空间的复杂性，而在其他情况下，了解通量在多义位上的变化范围可以充分说明允许的构型。但是，在某些情况下，需要在多态性上在活通量的各个分布中编码的彻底信息。当多表位的维数足够大时，获得这样的分布是一项非常具有挑战性的计算任务，并且开发具有成本效益的自组织算法的问题最近引起了人们的极大关注。在这里，我们提出了一种方法，该方法可让我们在与网络中的反应数量成线性比例的时间内按比例试探性地执行所需的计算，从而克服了近年来采用的类似技术的一些局限性。作为案例研究，我们将其应用于人类红细胞代谢网络的分析，该网络的解决方案空间可以通过不同的精确技术进行采样，例如“奔跑而出的蒙特卡洛”（缩放比例大致相当于系统大小的三次方） ）。获得了可行的反应通量的真实分布的非常准确的估计，这表明，尽管需要进一步改进，但我们的方法增强了我们分析大型生化反应网络允许结构空间的能力。

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