Bayesian-based selection of metabolic objective functions
Bayesian-based selection of metabolic objective functions
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DOI：
10.1093/bioinformatics/btl619
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发表时间：
2007-02-01
期刊：
影响因子：
5.8
通讯作者：
Srivastava, Ranjan
中科院分区：
文献类型：
--
作者：
Knorr, Andrea L.;Jain, Rishi;Srivastava, Ranjan
动机：欠定代谢系统的计算机分析的一个关键组成部分是确定适当的目标函数。一个常见的假设是，细胞的目标是最大限度地增长。这个目标函数已被证明是一致的，在一些有限的实验情况下，但可能不是普遍适用的。在这里，提出了一种方法来定量地确定最可能的目标functions.Results：基因组规模的代谢大肠杆菌生长在琥珀酸作为一个案例研究进行分析。五个不同的目标函数，包括最大化的增长率，选择基于生物相容性。通量平衡分析和线性规划的组合用于模拟细胞代谢，然后使用贝叶斯目标函数判别技术与独立的实验数据进行比较。在比较了氧摄取率和乙酸盐产生率之后，确定氧化还原电位的产生率的最小化为最可能的目标函数。给定适当的反应网络和实验数据，判别技术可以应用于任何细菌，以测试各种不同的可能的目标函数。
Motivation: A critical component of in silico analysis of underdetermined metabolic systems is the identification of the appropriate objective function. A common assumption is that the objective of the cell is to maximize growth. This objective function has been shown to be consistent in a few limited experimental cases, but may not be universally appropriate. Here a method is presented to quantitatively determine the most probable objective function.Results: The genome-scale metabolism of Escherichia coli growing on succinate was used as a case-study for analysis. Five different objective functions, including maximization of growth rate, were chosen based on biological plausibility. A combination of flux balance analysis and linear programming was used to simulate cellular metabolism, which was then compared to independent experimental data using a Bayesian objective function discrimination technique. After comparing rates of oxygen uptake and acetate production, minimization of the production rate of redox potential was determined to be the most probable objective function. Given the appropriate reaction network and experimental data, the discrimination technique can be applied to any bacterium to test a variety of different possible objective functions.