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Multi-objective experimental design for 13C-based metabolic flux analysis

机译：基于13C的代谢通量分析的多目标实验设计

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13C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of 13C-tracer experiments, illustrated on two different networks.Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-13C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-13C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-13C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-13C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment.We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of 13C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible 13C-tracers, while the illustrated benefit of multi-objective design should stimulate its application within the field of 13C-based metabolic flux analysis.
机译：基于13C的代谢通量分析是一种解决中央碳代谢中通量的出色技术，但使用专门的示踪剂时，成本可能很高。这项工作为13C示踪剂实验的高成本效益设计框架提供了框架，并在两个不同的网络上进行了说明。为链霉菌链霉菌和癌细胞系的网络计算了线性和非线性最佳输入混合物。如果仅将葡萄糖示踪剂视为癌细胞系或li。S. lividans的标记底物，则通过将大量1,2-13C2葡萄糖与均匀标记的葡萄糖混合后的混合物可获得最佳的参数估计准确性。实验设计基于线性（D标准）和非线性方法（S标准）进行评估。两种方法都生成几乎相同的输入混合，但是，线性方法因其计算量小而受到青睐。在最佳设计中大量的1,2-13C2葡萄糖与高昂的实验成本相吻合，当在谷氨酰胺和天冬氨酸示踪剂中引入标记时，这会进一步提高。多目标优化使同时评估实验质量和成本成为可能，并且可以揭示出色的折衷实验。例如，对于癌细胞系，100％1,2-13C2葡萄糖与100％位置一标记的谷氨酰胺的组合和100％1,2-13C2葡萄糖与100％均匀地标记的谷氨酰胺的组合对于癌细胞系的效果一样好，但是第一种混合物可降低每毫升规模的细胞培养实验$ 120的成本。我们证明了针对13C-代谢通量分析的非线性问题进行最佳实验设计的多目标线性方法的有效性。提供了工具和工作流程来执行多目标设计。可以轻松地计算D标准，以对可能的13C示踪剂进行高通量筛选，而多目标设计的插图优势应会刺激其在基于13C的代谢通量分析领域中的应用。

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