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. 2022 Jan 14;12:762. doi: 10.1038/s41598-021-03934-0

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Pearson correlation coefficients of LK-DFBA and ODE model flux predictions with E. coli experimental data. LK-DFBA (NLR) was the best approach when fitting on low sampling frequency (nT = 15) and high noise (CoV = 0.15) data. Blue and red bars represent LK-DFBA (NLR) and ODE model mean correlations, respectively, between the average predicted flux profiles and experimental steady-state flux data for the wildtype system and various gene knockout conditions. Gene knockouts in the LK-DFBA and ODE-based models were simulated by down-regulating relevant pathways. Error bars for LK-DFBA represent one standard deviation (N = 10 runs).