To compare the Multiphase Monte Carlo flux Sampling (MMCS) feature of the dingo library against the combined method of Polyround (for rounding) followed by hopsy (for sampling) on a set of 7 models with a ranging dimension was used (ext_data.zip). Under the simpl_transf_polytopes.zip 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 dingo_samples_on_simpl_transf_polytopes.zip the resulted samples from dingo can be found. Similarly, under the polyrounded_polytopes.zip 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 1000. Under the hopsy_samples folder the resulted samples from hopsy library, using a thinning of 100d ; only in case of Recon3D a thinning of 200d was used as suggested from the authors. Further, under the sars_samples.zip file, dingo samples from the solution space of the SARS-CoV-2 integrated model of Renz et al (2020) can be found for the following cases: unbiased; where the zero vector has been used as the objective function of the model after maximizing for the human biomass after maximizing for the virus biomass objective function (VBOF) The Python scripts to perform these experiments are included: polyround_preproces.py : runs the polyround functions and builds the simplified and transformed polytopes that dingo will use as well as the simplified, transformed and rounded polytopes hopsy will use hopsy_on_polyrounded_polytopes.py : performs sampling with hopsy dingo_on_simpl_transf_polytopes.py : performs sampling with dingo A notebook is available for how the integrated model was sampled.