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Fig. 3 | BMC Bioinformatics

Fig. 3

From: LK-DFBA: a linear programming-based modeling strategy for capturing dynamics and metabolite-dependent regulation in metabolism

Fig. 3

Comparison of fitting performance for MM, BST, LK-DFBA (LR), and LK-DFBA (LR+) methods. The black line for data is a benchmark comparison reflecting the noise added to the input data: each of the 750 noisy datasets was compared against the noise-free data to establish a baseline level of inaccuracy dependent on CoV. The penalized relative sum-of-square error (prSSE) calculations terms are all normalized to allow a consistent comparison against this reference. Solid lines represent the median error for each modeling approach and dotted lines represent the median absolute deviation. a CoV = 0.05. b CoV = 0.15. c CoV = 0.25. nT is the number of time points used to fit each model. The LK-DFBA framework with LK-DFBA (LR+) performs particularly well compared to other approaches when input data have significant noise, which is the type of input to be expected from experimental metabolomics analyses

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