Impact of Stoichiometry Representation on Simulation of Genotype-Phenotype Relationships in Metabolic Networks
Impact of Stoichiometry Representation on Simulation of Genotype-Phenotype Relationships in Metabolic Networks
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DOI：
10.1371/journal.pcbi.1002758
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发表时间：
2012-11-01
影响因子：
4.3
通讯作者：
Patil, Kiran R.
中科院分区：
文献类型：
--
作者：
Brochado, Ana Rita;Andrejev, Sergej;Patil, Kiran R.
基因组规模的代谢网络提供了一个全面的结构框架，通过通量模拟建模基因型-表型的关系。细胞的代谢通量状态的解空间通常非常大，并且基于优化的方法通常对于预测特定环境条件下的活性代谢状态是必要的。在这种优化算法中使用的目标函数与模型的生物学假设直接相关，因此它是成功建模的最相关参数之一。虽然选择通量的线性组合被广泛用于制定代谢目标函数，我们表明，由此产生的优化问题是敏感的化学计量表示的代谢网络。当使用数值上不同但生物化学上等效的化学计量表示时，这种不期望的敏感性导致不同的模拟结果，从而使生物学解释本质上主观和模糊。因此，我们提出了一种新的方法，最小化的代谢平衡（MiMBl），它从所需的目标函数的制定，通过铸造目标函数使用代谢物周转，而不是通量的化学计量表示的文物。通过模拟扰动的代谢网络，我们证明了使用化学计量表示独立的算法是明确地将建模结果与生物学解释联系起来的基础。例如，MiMBl使我们能够扩大代谢建模的范围，阐明酿酒酵母中几种遗传相互作用的机制基础。引文：Brochado AR，Andrejev S，Maranas CD，Patil KR（2012）化学计量表示对代谢网络中基因型-表型关系模拟的影响。PLoS Comput Biol 8（11）：e1002758. doi：10.1371/journal.pcbi.1002758
Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in Saccharomyces cerevisiae. Citation: Brochado AR, Andrejev S, Maranas CD, Patil KR (2012) Impact of Stoichiometry Representation on Simulation of Genotype-Phenotype Relationships in Metabolic Networks. PLoS Comput Biol 8(11): e1002758. doi:10.1371/journal.pcbi.1002758