Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle.
Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle.
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DOI：
10.1371/journal.pone.0112524
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Wallqvist A
中科院分区：
文献类型：
--
作者：
Song HS;Reifman J;Wallqvist A
预测代谢网络中可能的通量分布提供了将代谢与细胞生理学联系起来的详细表型信息。为了估计代谢稳态通量，最常见的方法是求解一组受化学计量约束的宏观质量平衡方程，同时试图优化假设的最优目标函数。这一假设在特定情况下是合理的，但在不同条件、细胞群或其他生物体中进行测试时可能无效。为了在广泛的条件下提供更一致和可靠的通量分布预测，在这篇文章中，我们提出了一个框架，使用通量最小化原理来预测mRNA表达数据的活性代谢途径。所提出的算法最小化通量幅度的加权和，而生物质产量可以根据分析的上下文被限制以适应从非常低到非常高的值的充足范围。我们已经制定了相应的酶反应的基因表达值的函数的通量权重，使上下文特定的通量的基础上创建一个通用的代谢网络。在野生型酿酒酵母，野生型和突变型大肠杆菌菌株的案例研究中，我们的方法实现了很高的预测精度，由相关系数和平方误差的总和，相对于实验测量值。与其他方法相比，我们的方法能够在各种条件下为两种模式生物提供定量预测。我们的方法不需要背景特定的代谢功能的先验知识或假设，并且不需要试错参数调整。因此，我们的框架是普遍适用的细菌和酵母菌的转录依赖性代谢建模。
Prediction of possible flux distributions in a metabolic network provides detailed phenotypic information that links metabolism to cellular physiology. To estimate metabolic steady-state fluxes, the most common approach is to solve a set of macroscopic mass balance equations subjected to stoichiometric constraints while attempting to optimize an assumed optimal objective function. This assumption is justifiable in specific cases but may be invalid when tested across different conditions, cell populations, or other organisms. With an aim to providing a more consistent and reliable prediction of flux distributions over a wide range of conditions, in this article we propose a framework that uses the flux minimization principle to predict active metabolic pathways from mRNA expression data. The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context. We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network. In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values. In contrast to other approaches, our method was able to provide quantitative predictions for both model organisms under a variety of conditions. Our approach requires no prior knowledge or assumption of a context-specific metabolic functionality and does not require trial-and-error parameter adjustments. Thus, our framework is of general applicability for modeling the transcription-dependent metabolism of bacteria and yeasts.
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影响因子：
56.9
作者：
Ishii, Nobuyoshi;Nakahigashi, Kenji;Tomita, Masaru
通讯作者：
Tomita, Masaru
影响因子：
4.3
作者：
Fang X;Wallqvist A;Reifman J
通讯作者：
Reifman J
影响因子：
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作者：
Hyduke DR;Lewis NE;Palsson BØ
通讯作者：
Palsson BØ
DOI：
10.1002/wsbm.60
发表时间：
2010-05-01
影响因子：
7.9
作者：
Gianchandani, Erwin P.;Chavali, Arvind K.;Papin, Jason A.
通讯作者：
Papin, Jason A.
影响因子：
9.9
作者：
通讯作者：
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