Designing experiments from noisy metabolomics data to refine constraint-based models

Metabolomics is an emerging technology to make high-throughput measurements of metabolites and is useful for the discovery of novel biomarkers of genetic diseases and for metabolic engineering. The system-wide data can be used to refine predictions made by constraint-based models of cell metabolism. However, the predictions of important output variables may still suffer from high variability due to high variance in the data itself, or from suboptimal choice of measurements in the metabolomics experiment. Here, we present a computational algorithm that uses initial metabolomics data to identify a smaller set of metabolites whose precise measurement most reduces variability of model predictions. We first randomly sample fluxes and concentrations using a new non-convex sampling algorithm that differs from previous approaches in its ability to sample across disjoint regions of the space and in its parallel implementation. We then demonstrate our algorithm´s ability to identify a sequence of experiments that successively refines model predictions using a simplified model of Escherichia coli central metabolism.