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Abstract：
Genome-scale metabolic models comprehensively describe an organism\'s metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism\'s phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.
摘要：
基因组尺度的代谢模型全面描述了生物体的代谢，并且可以使用组学数据进行调整以模拟特定条件的生理学。背景特定模型的质量受(i)算法和参数的选择和(ii)同样解释组学数据的替代背景特定模型的影响。在这里，我们使用GIMME量化了替代优化对微生物和哺乳动物模型提取的影响，iMAT,MBA,还有MCADRE.我们发现定义生物体表型的代谢任务必须明确和定量地保护。替代模型的范围受到算法选择和亲本基因组尺度模型的拓扑特性的强烈影响，脂肪酸代谢和细胞内代谢物运输对所有模型中的替代解决方案都有很大贡献。mCADRE提取了最具重现性的上下文特定模型，并且使用MBA生成的模型具有最多替代解决方案。GIMME在大肠杆菌中产生的定性不同溶液较少，但这些在哺乳动物模型中大幅增加。使用接收器操作特性图进行筛选的集合确定了性能最佳的模型。使用提取方法和表达阈值的组合对提取的模型进行综合评估，结果表明GIMME在大肠杆菌中产生了性能最好的模型，而mCADRE更适合复杂的哺乳动物模型。这些发现为基准-组学集成算法提供了指导，并激发了系统工作流程的开发，以枚举替代模型并提取生物学相关的上下文特定模型。
