Soft constraints-based multiobjective framework for flux balance analysis.
Soft constraints-based multiobjective framework for flux balance analysis.
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DOI：
10.1016/j.ymben.2010.05.003
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发表时间：
2010-09
影响因子：
8.4
通讯作者：
Yarmush, Martin L.
中科院分区：
文献类型：
--
作者：
Nagrath, Deepak;Avila-Elchiver, Marco;Berthiaume, Francois;Tilles, Arno W.;Messac, Achille;Yarmush, Martin L.
关键词：
通量平衡分析中的线性优化的当前技术状态已被限制为单个目标函数。由于哺乳动物系统执行各种功能，在这些系统中寻求最佳的通量分布时，需要一个多目标的方法。在大多数可用的多目标优化方法中，缺乏对何时使用特定目标以及如何将相互竞争的目标联合收割机和/或优先化以实现真正最优解的理解。为了解决这些限制，我们开发了一个基于软约束的基于线性物理规划的通量平衡分析（LPPFBA）框架，以获得多目标的最优解。开发的框架首先应用于计算一组多目标的最佳解决方案，在生物人工肝系统中的肝细胞功能（尿素分泌，白蛋白，NADPH和谷胱甘肽合成）相关的各种对的目标。接下来，同时分析了三个目标的最优解。利用该框架，在模拟生物人工肝系统中获得真正的最佳条件，以改善肝功能。LPPFBA的组合定量和可视化框架适用于任何大规模的代谢网络系统，包括那些来自基因组分析。
The current state of the art for linear optimization in Flux Balance Analysis has been limited to single objective functions. Since mammalian systems perform various functions, a multiobjective approach is needed when seeking optimal flux distributions in these systems. In most of the available multiobjective optimization methods, there is a lack of understanding of when to use a particular objective, and how to combine and/or prioritize mutually competing objectives to achieve a truly optimal solution. To address these limitations we developed a soft constraints based linear physical programming-based flux balance analysis (LPPFBA) framework to obtain a multiobjective optimal solutions. The developed framework was first applied to compute a set of multiobjective optimal solutions for various pairs of objectives relevant to hepatocyte function (urea secretion, albumin, NADPH, and glutathione syntheses) in bioartificial liver systems. Next, simultaneous analysis of the optimal solutions for three objectives was carried out. Further, this framework was utilized to obtain true optimal conditions to improve the hepatic functions in a simulated bioartificial liver system. The combined quantitative and visualization framework of LPPFBA is applicable to any large-scale metabolic network system, including those derived by genomic analyses.
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