Supplementary files for the dingo Python library

To compare the Multiphase Monte Carlo flux Sampling (MMCS) feature of the dingo library with the fastest approach for sampling on metabolic networks, a set of 7 models with a ranging dimension was used (<em>ext_data</em>). Under the <em>simpl_transf_polytopes</em> the polytopes retrieved after the simplify() and transform() functions of the PolyRound library can be found. These polytopes where used as input for the dingo implementation of the MMCS algorithm asking for an ESS of 1,000. Under the <em>dingo_samples_on_simpl_transf_polytopes</em> the resulted samples from <em>dingo</em> can be found. Similarly, under the <em>polyrounded_polytopes </em>the polytopes retrieved after both the simplify() and transform() but also the round() functions of the PolyRound library can be found. These polytopes were used as input for the hopsy library that was compared with dingo, again, asking for an ESS of 1,000. Under the <em>hopsy_samples</em> folder the resulted samples from hopsy<em> </em>library, using a thinning of 100<em>d </em>;<em> </em>only in case of Recon3D a thinning of 200<em>d </em>was used as suggested from the authors.<br>