Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al. The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details. The dataset contains the following files: 1. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of E. coli metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4). 2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b) 3. ODE_solutions.zip - self-explanatory (Figure 2c) 4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d) 5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a) 6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g 7. renaissance_parameter_fixing.zip - self-explanatory (Figure 4); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv). 8. scripts.zip - scripts used to create Figures 2-4.

This work was supported by funding from the Swiss National Science Foundation grant 315230_163423, the European Union's Horizon 2020 research and innovation programme under grant agreement 814408, Swedish Research Council Vetenskapsradet grant 2016-06160, and the Ecole Polytechnique Fédérale de Lausanne (EPFL).