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. 2019 Sep 2;5:32. doi: 10.1038/s41540-019-0109-0

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© The Author(s) 2019

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — Run-times for each of the algorithms on their respective platforms (CHRR: MATLAB; ACHR: Python; OPTGP: Python) when sampling flux solution for the Poolman a, Dal’Molin b, and Arnold model c. Trace and autocorrelation (ACF) plots (showing sampling chains and sample dependence, respectively) for chains of length 5000 with thinnings of T = 100, 1000, and 10,000 of the biomass reaction, as obtained when using each of the algorithms on the Arnold model